Based on the I.Family survey and only using data of parents, we aimed
to investigate whether consumer attitudes as assessed in the I.Family
cohort serve as mediators in the relationship between socioeconomic
factors and diet quality as indicated by a healthy eating score.
family_id: family ID number
ID_cohort / ID_no: Matched ID of
IDEFICS and I. family / ID-Number (I.Family ID)
country: 1 Italy 2 Estonia 3 Cyprus 4 Belgium 6 Sweden
7 Germany 8 Hungary 9 Spain
sex_T3: Sex of participant (1 male, 2 female)
age_status_T3: Age status of participant (1 child, 2
teen, 3 adult)
age_T3: Age of participant (years)
bmi_T3: Body Mass Index of participant
(kg/m2)
isced_cat2011_T3: ISCED level maximum of both parents
(1 low, 2 medium, 3 high)
income_cat_T3: Income categories (1 low, 2 low/medium,
3 medium, 4 medium/high, 5 high)
migration_T3: Migration status of parents (0 both
parents non-, 1 one parent migrant, 2 both parents migrant)
occupst_1_T3: Employment status of parent (1
full-time/30hrs, 2 part-time/15-29hrs, 3 part-time/<15hrs, 4
temporary company leave, 5 apprentice/retrainee, 6 currently
unemployed)
occupst_2_T3: Employment status of partner of parent
(…)
no_occupst_1: Employment status of parent if working
part-time/not employed (1 attend school, 2 attend university, 3
homemaker, 4 retired, 5 unemployed less than 1 year, 6 unemployed for
more than 1 year, 7 on welfare, 8 doing military, 9 others)
no_occupst_2: Employment status of partner of parent if
working part-time/not employed (…)
migration: (Newly created) Migration status of parents
of participant (1 no migrant background, 2 migrant background)
unemploy: (Newly created) Unemployment in household,
one adult/parent is unemployed (1 no unemployment, 2 unemployment)
singlepar: (Newly created) Single parenthood (1 no
single parent, 2 single parent)
hds_T3: Healthy Dietary Adherence Score (range
0-50)
Consumer attitudes as mediators
5-point likert scale: 1 disagree, 2 moderately disagree, 3 unsure, 4 moderately agree, 5 agree
foodst_01_T3: I compare labels to select the most
nutritious food.
foodst_02_T3: I have more confidence in food products
that I have seen advertised than in unadvertised products.
foodst_03_T3: I try to avoid food products with
additives.
foodst_04_T3: I make a point of using natural or
ecological products.
foodst_05_T3: I prefer to buy meat and vegetables fresh
rather than pre-packed.
foodst_06_T3: I frequently use ready-to-eat foods in
our household.
foodst_07_T3: I frequently use mixes, for instance
baking mixes and powder soups.
foodst_08_T3: The kids help in the kitchen, e.g. they
peel the potatoes and cut the vegetables.
knitr::opts_chunk$set(echo = TRUE)
require(knitr)
require(kabelExtra)
require(tidyverse)
require(table1)
require(DiagrammeR)
require(lavaan)
require(car)## create data frame subset with all relevant variables
core_subset <- core %>%
#only relevant variables
select(ID_cohort, ID_no, family_id, country, age_T3, age_status_T3, sex_T3, bmi_T3, isced_cat2011_T3, income_cat_T3, migration_T3, hds_T3) %>%
#adults only
filter(age_status_T3 == 3) %>%
#exclude those with missings for HDAS and BMI
filter(hds_T3 != "NA", bmi_T3 != "NA") %>%
#create new variable for participant whose parents have migration background (no = 1, yes = 2)
mutate(migration = ifelse(migration_T3 %in% c(1,2), 2, 1)) %>%
#migration as integer
mutate(migration = as.integer(migration)) %>%
#create new categorical variable for country
mutate(country_name = case_when(
country == "1" ~ "Italy",
country == "2" ~ "Estonia",
country == "3" ~ "Cyprus",
country == "4" ~ "Belgium",
country == "6" ~ "Sweden",
country == "7" ~ "Germany",
country == "8" ~ "Hungary",
country == "9" ~ "Spain",
TRUE ~ as.character(country)
) )
fa_subset <- fa %>%
#only relevant variables
select(FAMILY_ID, country, sex_fill_T3, occupst_1_T3, occupst_2_T3, no_occupst_1_T3, no_occupst_2_T3,foodst_01_T3, foodst_02_T3, foodst_03_T3, foodst_04_T3, foodst_05_T3, foodst_06_T3, foodst_07_T3, foodst_08_T3) %>%
#exclude those with missings for consumer attitudes
filter(foodst_01_T3 != "NA", foodst_02_T3 != "NA", foodst_03_T3 != "NA", foodst_04_T3 != "NA", foodst_05_T3 != "NA", foodst_06_T3 != "NA", foodst_07_T3 != "NA", foodst_08_T3 != "NA" ) %>%
#new variable for adults living in families with one or both adult family members unemployed (no = 1, yes = 2)
mutate(unemploy = ifelse(no_occupst_1_T3 %in% c(5,6,7) | no_occupst_2_T3 %in% c(5,6,7), 2, 1)) %>%
#unemployment as integer
mutate(unemploy = as.integer(unemploy))
kh_subset <- kh %>%
select(family_ID, country, househ_o18_T3) %>%
#new variable for single parenthood (no = 1, yes = 2)
mutate(singlepar = ifelse(househ_o18_T3 == 1, 2, 1)) %>%
#singlepar as integer
mutate(singlepar = as.integer(singlepar))
# Rename family_ID in fa and kh data frame
names(fa_subset)[names(fa_subset) == "FAMILY_ID"] <- "family_id"
names(fa_subset)[names(fa_subset) == "sex_fill_T3"] <- "sex_T3"
names(kh_subset)[names(kh_subset) == "family_ID"] <- "family_id"
# Merge data frames - automatic merging by family_id, sex, country
subset <- merge(core_subset, fa_subset)
subset <- merge(subset, kh_subset)subset <- subset %>%
mutate(foodst_01_T3 = case_when(
country == "4" & foodst_01_T3 == 1 ~ 5,
country == "4" & foodst_01_T3 == 5 ~ 1,
country == "4" & foodst_01_T3 == 2 ~ 4,
country == "4" & foodst_01_T3 == 4 ~ 2,
TRUE ~ foodst_01_T3
)) %>%
mutate(foodst_02_T3 = case_when(
country == "4" & foodst_02_T3 == 1 ~ 5,
country == "4" & foodst_02_T3 == 5 ~ 1,
country == "4" & foodst_02_T3 == 2 ~ 4,
country == "4" & foodst_02_T3 == 4 ~ 2,
TRUE ~ foodst_02_T3
)) %>%
mutate(foodst_03_T3 = case_when(
country == "4" & foodst_03_T3 == 1 ~ 5,
country == "4" & foodst_03_T3 == 5 ~ 1,
country == "4" & foodst_03_T3 == 2 ~ 4,
country == "4" & foodst_03_T3 == 4 ~ 2,
TRUE ~ foodst_03_T3
)) %>%
mutate(foodst_04_T3 = case_when(
country == "4" & foodst_04_T3 == 1 ~ 5,
country == "4" & foodst_04_T3 == 5 ~ 1,
country == "4" & foodst_04_T3 == 2 ~ 4,
country == "4" & foodst_04_T3 == 4 ~ 2,
TRUE ~ foodst_04_T3
)) %>%
mutate(foodst_05_T3 = case_when(
country == "4" & foodst_05_T3 == 1 ~ 5,
country == "4" & foodst_05_T3 == 5 ~ 1,
country == "4" & foodst_05_T3 == 2 ~ 4,
country == "4" & foodst_05_T3 == 4 ~ 2,
TRUE ~ foodst_05_T3
)) %>%
mutate(foodst_06_T3 = case_when(
country == "4" & foodst_06_T3 == 1 ~ 5,
country == "4" & foodst_06_T3 == 5 ~ 1,
country == "4" & foodst_06_T3 == 2 ~ 4,
country == "4" & foodst_06_T3 == 4 ~ 2,
TRUE ~ foodst_06_T3
)) %>%
mutate(foodst_07_T3 = case_when(
country == "4" & foodst_07_T3 == 1 ~ 5,
country == "4" & foodst_07_T3 == 5 ~ 1,
country == "4" & foodst_07_T3 == 2 ~ 4,
country == "4" & foodst_07_T3 == 4 ~ 2,
TRUE ~ foodst_07_T3
)) %>%
mutate(foodst_08_T3 = case_when(
country == "4" & foodst_08_T3 == 1 ~ 5,
country == "4" & foodst_08_T3 == 5 ~ 1,
country == "4" & foodst_08_T3 == 2 ~ 4,
country == "4" & foodst_08_T3 == 4 ~ 2,
TRUE ~ foodst_08_T3
)) %>%
mutate(foodst_01_T3 = case_when(
country == "9" & foodst_01_T3 == 1 ~ 5,
country == "9" & foodst_01_T3 == 5 ~ 1,
country == "9" & foodst_01_T3 == 2 ~ 4,
country == "9" & foodst_01_T3 == 4 ~ 2,
TRUE ~ foodst_01_T3
)) %>%
mutate(foodst_02_T3 = case_when(
country == "9" & foodst_02_T3 == 1 ~ 5,
country == "9" & foodst_02_T3 == 5 ~ 1,
country == "9" & foodst_02_T3 == 2 ~ 4,
country == "9" & foodst_02_T3 == 4 ~ 2,
TRUE ~ foodst_02_T3
)) %>%
mutate(foodst_03_T3 = case_when(
country == "9" & foodst_03_T3 == 1 ~ 5,
country == "9" & foodst_03_T3 == 5 ~ 1,
country == "9" & foodst_03_T3 == 2 ~ 4,
country == "9" & foodst_03_T3 == 4 ~ 2,
TRUE ~ foodst_03_T3
)) %>%
mutate(foodst_04_T3 = case_when(
country == "9" & foodst_04_T3 == 1 ~ 5,
country == "9" & foodst_04_T3 == 5 ~ 1,
country == "9" & foodst_04_T3 == 2 ~ 4,
country == "9" & foodst_04_T3 == 4 ~ 2,
TRUE ~ foodst_04_T3
)) %>%
mutate(foodst_05_T3 = case_when(
country == "9" & foodst_05_T3 == 1 ~ 5,
country == "9" & foodst_05_T3 == 5 ~ 1,
country == "9" & foodst_05_T3 == 2 ~ 4,
country == "9" & foodst_05_T3 == 4 ~ 2,
TRUE ~ foodst_05_T3
)) %>%
mutate(foodst_06_T3 = case_when(
country == "9" & foodst_06_T3 == 1 ~ 5,
country == "9" & foodst_06_T3 == 5 ~ 1,
country == "9" & foodst_06_T3 == 2 ~ 4,
country == "9" & foodst_06_T3 == 4 ~ 2,
TRUE ~ foodst_06_T3
)) %>%
mutate(foodst_07_T3 = case_when(
country == "9" & foodst_07_T3 == 1 ~ 5,
country == "9" & foodst_07_T3 == 5 ~ 1,
country == "9" & foodst_07_T3 == 2 ~ 4,
country == "9" & foodst_07_T3 == 4 ~ 2,
TRUE ~ foodst_07_T3
)) %>%
mutate(foodst_08_T3 = case_when(
country == "9" & foodst_08_T3 == 1 ~ 5,
country == "9" & foodst_08_T3 == 5 ~ 1,
country == "9" & foodst_08_T3 == 2 ~ 4,
country == "9" & foodst_08_T3 == 4 ~ 2,
TRUE ~ foodst_08_T3
)) subset$isced_cat2011_T3 <-
factor(subset$isced_cat2011_T3, levels=c(1,2,3),
labels = c("low", "medium", "high"))
label(subset$isced_cat2011_T3) <- "Highest education level in household"
subset$income_cat_T3 <-
factor(subset$income_cat_T3, levels=c(1,2,3,4,5),
labels = c("low", "low/medium", "medium", "medium/high", "high"))
label(subset$income_cat_T3) <- "Household income"
subset$migration <-
factor(subset$migration, levels=c(2, 1),
labels=c("Yes",
"No"))
label(subset$migration) <- "Migrant background"
subset$unemploy <-
factor(subset$unemploy, levels=c(2, 1),
labels=c("Yes",
"No"))
label(subset$unemploy) <- "Unemployment in household"
subset$singlepar <-
factor(subset$singlepar, levels=c(2, 1),
labels=c("Yes",
"No"))
label(subset$singlepar) <- "Single parenthood"
subset$sex_T3 <-
factor(subset$sex_T3, levels=c(1, 2),
labels=c("Male",
"Female"))
label(subset$sex_T3) <- "Sex"
label(subset$hds_T3) <- "HDAS"
label(subset$bmi_T3) <- "BMI"
label(subset$age_T3) <- "Age"
units(subset$bmi_T3) <- "kg/m^2"
units(subset$age_T3) <- "years"
caption1 <- "Table 1: Study sample characteristics"
footnote1 <- "Abbreviations: HDAS = Healthy Dietary Adherence Score ; SD = standard deviation ; BMI = body mass index "
table1(~ hds_T3 +
isced_cat2011_T3 + income_cat_T3 +
migration + unemploy + singlepar +
bmi_T3 + age_T3 + sex_T3 |
country_name, data = subset, caption=caption1, footnote=footnote1)| Belgium (N=118) |
Cyprus (N=773) |
Estonia (N=595) |
Germany (N=509) |
Hungary (N=661) |
Italy (N=748) |
Spain (N=208) |
Sweden (N=439) |
Overall (N=4051) |
|
|---|---|---|---|---|---|---|---|---|---|
Abbreviations: HDAS = Healthy Dietary Adherence Score ; SD = standard deviation ; BMI = body mass index | |||||||||
| HDAS | |||||||||
| Mean (SD) | 27.9 (7.23) | 25.0 (9.11) | 27.7 (7.89) | 23.0 (8.89) | 23.2 (8.88) | 24.3 (8.03) | 27.1 (7.82) | 30.7 (8.06) | 25.5 (8.79) |
| Median [Min, Max] | 28.0 [6.00, 45.0] | 25.0 [1.00, 48.0] | 28.0 [3.00, 47.0] | 23.0 [1.00, 45.0] | 23.0 [2.00, 48.0] | 24.5 [0, 48.0] | 27.0 [10.0, 47.0] | 32.0 [7.00, 48.0] | 26.0 [0, 48.0] |
| Highest education level in household | |||||||||
| low | 3 (2.5%) | 9 (1.2%) | 0 (0%) | 32 (6.3%) | 14 (2.1%) | 118 (15.8%) | 5 (2.4%) | 0 (0%) | 181 (4.5%) |
| medium | 20 (16.9%) | 283 (36.6%) | 163 (27.4%) | 293 (57.6%) | 315 (47.7%) | 453 (60.6%) | 64 (30.8%) | 105 (23.9%) | 1696 (41.9%) |
| high | 94 (79.7%) | 437 (56.5%) | 428 (71.9%) | 181 (35.6%) | 308 (46.6%) | 144 (19.3%) | 135 (64.9%) | 327 (74.5%) | 2054 (50.7%) |
| Missing | 1 (0.8%) | 44 (5.7%) | 4 (0.7%) | 3 (0.6%) | 24 (3.6%) | 33 (4.4%) | 4 (1.9%) | 7 (1.6%) | 120 (3.0%) |
| Household income | |||||||||
| low | 3 (2.5%) | 210 (27.2%) | 71 (11.9%) | 85 (16.7%) | 93 (14.1%) | 311 (41.6%) | 14 (6.7%) | 10 (2.3%) | 797 (19.7%) |
| low/medium | 3 (2.5%) | 75 (9.7%) | 23 (3.9%) | 54 (10.6%) | 46 (7.0%) | 112 (15.0%) | 13 (6.3%) | 12 (2.7%) | 338 (8.3%) |
| medium | 65 (55.1%) | 287 (37.1%) | 148 (24.9%) | 233 (45.8%) | 240 (36.3%) | 160 (21.4%) | 78 (37.5%) | 146 (33.3%) | 1357 (33.5%) |
| medium/high | 18 (15.3%) | 80 (10.3%) | 57 (9.6%) | 59 (11.6%) | 92 (13.9%) | 11 (1.5%) | 34 (16.3%) | 119 (27.1%) | 470 (11.6%) |
| high | 22 (18.6%) | 97 (12.5%) | 284 (47.7%) | 48 (9.4%) | 149 (22.5%) | 59 (7.9%) | 58 (27.9%) | 146 (33.3%) | 863 (21.3%) |
| Missing | 7 (5.9%) | 24 (3.1%) | 12 (2.0%) | 30 (5.9%) | 41 (6.2%) | 95 (12.7%) | 11 (5.3%) | 6 (1.4%) | 226 (5.6%) |
| Migrant background | |||||||||
| Yes | 4 (3.4%) | 152 (19.7%) | 20 (3.4%) | 100 (19.6%) | 21 (3.2%) | 132 (17.6%) | 16 (7.7%) | 69 (15.7%) | 514 (12.7%) |
| No | 114 (96.6%) | 621 (80.3%) | 575 (96.6%) | 409 (80.4%) | 640 (96.8%) | 616 (82.4%) | 192 (92.3%) | 370 (84.3%) | 3537 (87.3%) |
| Unemployment in household | |||||||||
| Yes | 2 (1.7%) | 116 (15.0%) | 22 (3.7%) | 38 (7.5%) | 46 (7.0%) | 104 (13.9%) | 19 (9.1%) | 10 (2.3%) | 357 (8.8%) |
| No | 116 (98.3%) | 657 (85.0%) | 573 (96.3%) | 471 (92.5%) | 615 (93.0%) | 644 (86.1%) | 189 (90.9%) | 429 (97.7%) | 3694 (91.2%) |
| Single parenthood | |||||||||
| Yes | 13 (11.0%) | 52 (6.7%) | 70 (11.8%) | 77 (15.1%) | 96 (14.5%) | 27 (3.6%) | 19 (9.1%) | 58 (13.2%) | 412 (10.2%) |
| No | 105 (89.0%) | 721 (93.3%) | 525 (88.2%) | 431 (84.7%) | 565 (85.5%) | 721 (96.4%) | 174 (83.7%) | 381 (86.8%) | 3623 (89.4%) |
| Missing | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.2%) | 0 (0%) | 0 (0%) | 15 (7.2%) | 0 (0%) | 16 (0.4%) |
| BMI (kg/m^2) | |||||||||
| Mean (SD) | 24.1 (4.58) | 26.2 (5.11) | 25.4 (5.37) | 26.7 (5.68) | 26.1 (5.57) | 27.4 (5.38) | 25.1 (4.38) | 24.7 (3.90) | 26.1 (5.27) |
| Median [Min, Max] | 23.1 [17.6, 40.5] | 25.3 [17.3, 46.7] | 24.1 [16.4, 53.5] | 25.4 [16.7, 53.7] | 25.1 [15.5, 55.6] | 26.5 [17.3, 49.7] | 24.2 [17.3, 42.4] | 23.9 [17.1, 50.3] | 25.0 [15.5, 55.6] |
| Age (years) | |||||||||
| Mean (SD) | 41.2 (4.79) | 41.5 (5.91) | 39.6 (5.34) | 42.9 (5.87) | 40.8 (5.12) | 42.7 (5.53) | 44.9 (4.08) | 43.8 (5.26) | 41.9 (5.63) |
| Median [Min, Max] | 41.0 [29.4, 60.8] | 41.0 [27.0, 70.6] | 39.4 [24.0, 75.9] | 42.9 [26.2, 63.0] | 40.0 [24.4, 61.1] | 42.7 [26.7, 65.9] | 44.9 [33.3, 62.1] | 43.7 [30.6, 65.4] | 41.7 [24.0, 75.9] |
| Sex | |||||||||
| Male | 15 (12.7%) | 168 (21.7%) | 56 (9.4%) | 60 (11.8%) | 80 (12.1%) | 92 (12.3%) | 35 (16.8%) | 94 (21.4%) | 600 (14.8%) |
| Female | 103 (87.3%) | 605 (78.3%) | 539 (90.6%) | 449 (88.2%) | 581 (87.9%) | 656 (87.7%) | 173 (83.2%) | 345 (78.6%) | 3451 (85.2%) |
subset$foodst_01_T3 <-
factor(subset$foodst_01_T3, levels=c(1,2,3,4,5),
labels = c("Disagree", "Moderately disagree", "Unsure", "Moderately agree", "Agree"))
subset$foodst_02_T3 <-
factor(subset$foodst_02_T3, levels=c(1,2,3,4,5),
labels = c("Disagree", "Moderately disagree", "Unsure", "Moderately agree", "Agree"))
subset$foodst_03_T3 <-
factor(subset$foodst_03_T3, levels=c(1,2,3,4,5),
labels = c("Disagree", "Moderately disagree", "Unsure", "Moderately agree", "Agree"))
subset$foodst_04_T3 <-
factor(subset$foodst_04_T3, levels=c(1,2,3,4,5),
labels = c("Disagree", "Moderately disagree", "Unsure", "Moderately agree", "Agree"))
subset$foodst_05_T3 <-
factor(subset$foodst_05_T3, levels=c(1,2,3,4,5),
labels = c("Disagree", "Moderately disagree", "Unsure", "Moderately agree", "Agree"))
subset$foodst_06_T3 <-
factor(subset$foodst_06_T3, levels=c(1,2,3,4,5),
labels = c("Disagree", "Moderately disagree", "Unsure", "Moderately agree", "Agree"))
subset$foodst_07_T3 <-
factor(subset$foodst_07_T3, levels=c(1,2,3,4,5),
labels = c("Disagree", "Moderately disagree", "Unsure", "Moderately agree", "Agree"))
subset$foodst_08_T3 <-
factor(subset$foodst_08_T3, levels=c(1,2,3,4,5),
labels = c("Disagree", "Moderately disagree", "Unsure", "Moderately agree", "Agree"))
label(subset$foodst_01_T3) <- "Comparing food labels"
label(subset$foodst_02_T3) <- "Trusting food advertisements"
label(subset$foodst_03_T3) <- "Avoiding food additives"
label(subset$foodst_04_T3) <- "Valuing ecological products"
label(subset$foodst_05_T3) <- "Preferring fresh meat and vegetables"
label(subset$foodst_06_T3) <- "Frequently using ready-to-eat foods"
label(subset$foodst_07_T3) <- "Frequently using pre-made mixes"
label(subset$foodst_08_T3) <- "Having children help in the kitchen"
caption2 <- "Table 2: Frequency of responses in % to questions regarding various consumer attitudes"
table1(~foodst_01_T3 + foodst_02_T3 + foodst_03_T3 + foodst_04_T3 + foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3 | country_name, data = subset, caption=caption2)| Belgium (N=118) |
Cyprus (N=773) |
Estonia (N=595) |
Germany (N=509) |
Hungary (N=661) |
Italy (N=748) |
Spain (N=208) |
Sweden (N=439) |
Overall (N=4051) |
|
|---|---|---|---|---|---|---|---|---|---|
| Comparing food labels | |||||||||
| Disagree | 28 (23.7%) | 58 (7.5%) | 53 (8.9%) | 40 (7.9%) | 107 (16.2%) | 66 (8.8%) | 23 (11.1%) | 61 (13.9%) | 436 (10.8%) |
| Moderately disagree | 33 (28.0%) | 74 (9.6%) | 85 (14.3%) | 147 (28.9%) | 138 (20.9%) | 58 (7.8%) | 22 (10.6%) | 87 (19.8%) | 644 (15.9%) |
| Unsure | 12 (10.2%) | 96 (12.4%) | 100 (16.8%) | 65 (12.8%) | 106 (16.0%) | 99 (13.2%) | 19 (9.1%) | 24 (5.5%) | 521 (12.9%) |
| Moderately agree | 38 (32.2%) | 292 (37.8%) | 253 (42.5%) | 203 (39.9%) | 195 (29.5%) | 291 (38.9%) | 91 (43.8%) | 201 (45.8%) | 1564 (38.6%) |
| Agree | 7 (5.9%) | 253 (32.7%) | 104 (17.5%) | 54 (10.6%) | 115 (17.4%) | 234 (31.3%) | 53 (25.5%) | 66 (15.0%) | 886 (21.9%) |
| Trusting food advertisements | |||||||||
| Disagree | 60 (50.8%) | 336 (43.5%) | 294 (49.4%) | 230 (45.2%) | 358 (54.2%) | 260 (34.8%) | 94 (45.2%) | 303 (69.0%) | 1935 (47.8%) |
| Moderately disagree | 39 (33.1%) | 189 (24.5%) | 228 (38.3%) | 211 (41.5%) | 202 (30.6%) | 169 (22.6%) | 70 (33.7%) | 101 (23.0%) | 1209 (29.8%) |
| Unsure | 10 (8.5%) | 88 (11.4%) | 52 (8.7%) | 39 (7.7%) | 58 (8.8%) | 112 (15.0%) | 12 (5.8%) | 26 (5.9%) | 397 (9.8%) |
| Moderately agree | 9 (7.6%) | 121 (15.7%) | 20 (3.4%) | 25 (4.9%) | 34 (5.1%) | 151 (20.2%) | 24 (11.5%) | 9 (2.1%) | 393 (9.7%) |
| Agree | 0 (0%) | 39 (5.0%) | 1 (0.2%) | 4 (0.8%) | 9 (1.4%) | 56 (7.5%) | 8 (3.8%) | 0 (0%) | 117 (2.9%) |
| Avoiding food additives | |||||||||
| Disagree | 13 (11.0%) | 22 (2.8%) | 17 (2.9%) | 27 (5.3%) | 25 (3.8%) | 20 (2.7%) | 12 (5.8%) | 31 (7.1%) | 167 (4.1%) |
| Moderately disagree | 22 (18.6%) | 38 (4.9%) | 35 (5.9%) | 97 (19.1%) | 42 (6.4%) | 33 (4.4%) | 24 (11.5%) | 78 (17.8%) | 369 (9.1%) |
| Unsure | 20 (16.9%) | 69 (8.9%) | 79 (13.3%) | 73 (14.3%) | 75 (11.3%) | 108 (14.4%) | 22 (10.6%) | 44 (10.0%) | 490 (12.1%) |
| Moderately agree | 48 (40.7%) | 212 (27.4%) | 260 (43.7%) | 227 (44.6%) | 218 (33.0%) | 191 (25.5%) | 79 (38.0%) | 189 (43.1%) | 1424 (35.2%) |
| Agree | 15 (12.7%) | 432 (55.9%) | 204 (34.3%) | 85 (16.7%) | 301 (45.5%) | 396 (52.9%) | 71 (34.1%) | 97 (22.1%) | 1601 (39.5%) |
| Valuing ecological products | |||||||||
| Disagree | 10 (8.5%) | 25 (3.2%) | 60 (10.1%) | 94 (18.5%) | 46 (7.0%) | 32 (4.3%) | 12 (5.8%) | 44 (10.0%) | 323 (8.0%) |
| Moderately disagree | 29 (24.6%) | 39 (5.0%) | 136 (22.9%) | 194 (38.1%) | 67 (10.1%) | 58 (7.8%) | 27 (13.0%) | 109 (24.8%) | 659 (16.3%) |
| Unsure | 22 (18.6%) | 80 (10.3%) | 149 (25.0%) | 69 (13.6%) | 84 (12.7%) | 78 (10.4%) | 12 (5.8%) | 43 (9.8%) | 537 (13.3%) |
| Moderately agree | 47 (39.8%) | 309 (40.0%) | 183 (30.8%) | 136 (26.7%) | 249 (37.7%) | 288 (38.5%) | 100 (48.1%) | 192 (43.7%) | 1504 (37.1%) |
| Agree | 10 (8.5%) | 320 (41.4%) | 67 (11.3%) | 16 (3.1%) | 215 (32.5%) | 292 (39.0%) | 57 (27.4%) | 51 (11.6%) | 1028 (25.4%) |
| Preferring fresh meat and vegetables | |||||||||
| Disagree | 1 (0.8%) | 13 (1.7%) | 10 (1.7%) | 22 (4.3%) | 4 (0.6%) | 11 (1.5%) | 6 (2.9%) | 19 (4.3%) | 86 (2.1%) |
| Moderately disagree | 12 (10.2%) | 15 (1.9%) | 27 (4.5%) | 86 (16.9%) | 20 (3.0%) | 25 (3.3%) | 4 (1.9%) | 51 (11.6%) | 240 (5.9%) |
| Unsure | 8 (6.8%) | 16 (2.1%) | 39 (6.6%) | 41 (8.1%) | 13 (2.0%) | 10 (1.3%) | 2 (1.0%) | 24 (5.5%) | 153 (3.8%) |
| Moderately agree | 39 (33.1%) | 100 (12.9%) | 261 (43.9%) | 237 (46.6%) | 165 (25.0%) | 170 (22.7%) | 44 (21.2%) | 152 (34.6%) | 1168 (28.8%) |
| Agree | 58 (49.2%) | 629 (81.4%) | 258 (43.4%) | 123 (24.2%) | 459 (69.4%) | 532 (71.1%) | 152 (73.1%) | 193 (44.0%) | 2404 (59.3%) |
| Frequently using ready-to-eat foods | |||||||||
| Disagree | 51 (43.2%) | 341 (44.1%) | 182 (30.6%) | 123 (24.2%) | 230 (34.8%) | 587 (78.5%) | 86 (41.3%) | 125 (28.5%) | 1725 (42.6%) |
| Moderately disagree | 46 (39.0%) | 237 (30.7%) | 311 (52.3%) | 289 (56.8%) | 309 (46.7%) | 131 (17.5%) | 67 (32.2%) | 169 (38.5%) | 1559 (38.5%) |
| Unsure | 10 (8.5%) | 41 (5.3%) | 56 (9.4%) | 45 (8.8%) | 53 (8.0%) | 9 (1.2%) | 5 (2.4%) | 23 (5.2%) | 242 (6.0%) |
| Moderately agree | 11 (9.3%) | 117 (15.1%) | 44 (7.4%) | 49 (9.6%) | 51 (7.7%) | 12 (1.6%) | 31 (14.9%) | 98 (22.3%) | 413 (10.2%) |
| Agree | 0 (0%) | 37 (4.8%) | 2 (0.3%) | 3 (0.6%) | 18 (2.7%) | 9 (1.2%) | 19 (9.1%) | 24 (5.5%) | 112 (2.8%) |
| Frequently using pre-made mixes | |||||||||
| Disagree | 67 (56.8%) | 505 (65.3%) | 290 (48.7%) | 164 (32.2%) | 405 (61.3%) | 629 (84.1%) | 136 (65.4%) | 322 (73.3%) | 2518 (62.2%) |
| Moderately disagree | 40 (33.9%) | 153 (19.8%) | 223 (37.5%) | 261 (51.3%) | 199 (30.1%) | 91 (12.2%) | 50 (24.0%) | 87 (19.8%) | 1104 (27.3%) |
| Unsure | 6 (5.1%) | 34 (4.4%) | 43 (7.2%) | 33 (6.5%) | 28 (4.2%) | 5 (0.7%) | 3 (1.4%) | 9 (2.1%) | 161 (4.0%) |
| Moderately agree | 4 (3.4%) | 59 (7.6%) | 36 (6.1%) | 47 (9.2%) | 23 (3.5%) | 20 (2.7%) | 12 (5.8%) | 17 (3.9%) | 218 (5.4%) |
| Agree | 1 (0.8%) | 22 (2.8%) | 3 (0.5%) | 4 (0.8%) | 6 (0.9%) | 3 (0.4%) | 7 (3.4%) | 4 (0.9%) | 50 (1.2%) |
| Having children help in the kitchen | |||||||||
| Disagree | 6 (5.1%) | 100 (12.9%) | 56 (9.4%) | 25 (4.9%) | 50 (7.6%) | 236 (31.6%) | 21 (10.1%) | 32 (7.3%) | 526 (13.0%) |
| Moderately disagree | 36 (30.5%) | 105 (13.6%) | 164 (27.6%) | 147 (28.9%) | 91 (13.8%) | 76 (10.2%) | 43 (20.7%) | 112 (25.5%) | 774 (19.1%) |
| Unsure | 13 (11.0%) | 61 (7.9%) | 86 (14.5%) | 33 (6.5%) | 46 (7.0%) | 36 (4.8%) | 6 (2.9%) | 28 (6.4%) | 309 (7.6%) |
| Moderately agree | 53 (44.9%) | 279 (36.1%) | 229 (38.5%) | 217 (42.6%) | 246 (37.2%) | 260 (34.8%) | 94 (45.2%) | 196 (44.6%) | 1574 (38.9%) |
| Agree | 10 (8.5%) | 228 (29.5%) | 60 (10.1%) | 87 (17.1%) | 228 (34.5%) | 140 (18.7%) | 44 (21.2%) | 71 (16.2%) | 868 (21.4%) |
Check for multicollinearity
model_mc <- lm(hds_T3~income_cat_T3+isced_cat2011_T3+migration+singlepar+unemploy, data=subset)
vif(model_mc)## income_cat_T3 isced_cat2011_T3 migration singlepar
## 1.412247 1.264254 1.022167 1.057336
## unemploy
## 1.074787We performed regression analysis for the association between socioeconomic factors / vulnerabilities and consumer attitudes (path a), the association between consumer attitudes and HDAS (path b) and the association between socioeconomic factors / vulnerabilites and HDAS (total effect, path c). All models are adjusted for age, sex and BMI.
require(MASS)# Consumer attitudes as factors
subset$foodst_01_T3 <- relevel(as.factor(subset$foodst_01_T3), ref = 1)
subset$foodst_02_T3 <- relevel(as.factor(subset$foodst_02_T3), ref = 1)
subset$foodst_03_T3 <- relevel(as.factor(subset$foodst_03_T3), ref = 1)
subset$foodst_04_T3 <- relevel(as.factor(subset$foodst_04_T3), ref = 1)
subset$foodst_05_T3 <- relevel(as.factor(subset$foodst_05_T3), ref = 1)
subset$foodst_06_T3 <- relevel(as.factor(subset$foodst_06_T3), ref = 1)
subset$foodst_07_T3 <- relevel(as.factor(subset$foodst_07_T3), ref = 1)
subset$foodst_08_T3 <- relevel(as.factor(subset$foodst_08_T3), ref = 1)# path a
# Education -> Consumer attitudes
edu1 <- polr(foodst_01_T3 ~ isced_cat2011_T3 + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(edu1)## Call:
## polr(formula = foodst_01_T3 ~ isced_cat2011_T3 + age_T3 + sex_T3 +
## bmi_T3, data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## isced_cat2011_T3 0.091438 0.050538 1.8093
## age_T3 0.009938 0.005331 1.8644
## sex_T3 0.048696 0.084497 0.5763
## bmi_T3 -0.007941 0.005585 -1.4218
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.6008 0.3688 -4.3404
## 2|3 -0.4852 0.3670 -1.3222
## 3|4 0.1041 0.3667 0.2840
## 4|5 1.8132 0.3681 4.9260
##
## Residual Deviance: 11749.68
## AIC: 11765.68
## (120 observations deleted due to missingness)ctable_edu1 <- coef(summary(edu1))
p_edu1 <- pnorm(abs(ctable_edu1[, "t value"]), lower.tail = FALSE) * 2
(ctable_edu1 <- cbind(ctable_edu1, "p-value" = round(p_edu1, 4)))## Value Std. Error t value p-value
## isced_cat2011_T3 0.091438323 0.050538467 1.8092817 0.0704
## age_T3 0.009938352 0.005330591 1.8643997 0.0623
## sex_T3 0.048696142 0.084497066 0.5763057 0.5644
## bmi_T3 -0.007940528 0.005584733 -1.4218277 0.1551
## 1|2 -1.600762674 0.368803159 -4.3404256 0.0000
## 2|3 -0.485207184 0.366976417 -1.3221754 0.1861
## 3|4 0.104131360 0.366710256 0.2839609 0.7764
## 4|5 1.813249492 0.368097782 4.9259995 0.0000(ci_edu1 <- confint(edu1, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## isced_cat2011_T3 -0.04673295 0.22970178
## age_T3 -0.00463682 0.02451905
## sex_T3 -0.18261350 0.27961389
## bmi_T3 -0.02320238 0.00734912exp(cbind(OR = coef(edu1), ci_edu1))## OR 0.3 % 99.7 %
## isced_cat2011_T3 1.0957492 0.9543422 1.258225
## age_T3 1.0099879 0.9953739 1.024822
## sex_T3 1.0499013 0.8330901 1.322619
## bmi_T3 0.9920909 0.9770647 1.007376edu2 <- polr(foodst_02_T3 ~ isced_cat2011_T3 + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(edu2)## Call:
## polr(formula = foodst_02_T3 ~ isced_cat2011_T3 + age_T3 + sex_T3 +
## bmi_T3, data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## isced_cat2011_T3 -3.671e-01 0.052844 -6.946003
## age_T3 -1.041e-05 0.005533 -0.001882
## sex_T3 -5.224e-02 0.087908 -0.594310
## bmi_T3 9.911e-03 0.005831 1.699554
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.8536 0.3785 -2.2553
## 2|3 0.5104 0.3783 1.3492
## 3|4 1.2281 0.3791 3.2394
## 4|5 2.8219 0.3880 7.2725
##
## Residual Deviance: 9912.099
## AIC: 9928.099
## (120 observations deleted due to missingness)ctable_edu2 <- coef(summary(edu2))
p_edu2 <- pnorm(abs(ctable_edu2[, "t value"]), lower.tail = FALSE) * 2
(ctable_edu2 <- cbind(ctable_edu2, "p-value" = round(p_edu2, 4)))## Value Std. Error t value p-value
## isced_cat2011_T3 -3.670574e-01 0.052844409 -6.946002768 0.0000
## age_T3 -1.041159e-05 0.005533139 -0.001881679 0.9985
## sex_T3 -5.224481e-02 0.087908306 -0.594310335 0.5523
## bmi_T3 9.910583e-03 0.005831285 1.699553789 0.0892
## 1|2 -8.536021e-01 0.378479881 -2.255343418 0.0241
## 2|3 5.103684e-01 0.378266612 1.349229212 0.1773
## 3|4 1.228101e+00 0.379116241 3.239377385 0.0012
## 4|5 2.821854e+00 0.388015222 7.272535431 0.0000(ci_edu2 <- confint(edu2, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## isced_cat2011_T3 -0.511638605 -0.22258179
## age_T3 -0.015153043 0.01511259
## sex_T3 -0.291800427 0.18918981
## bmi_T3 -0.006065622 0.02583794exp(cbind(OR = coef(edu2), ci_edu2))## OR 0.3 % 99.7 %
## isced_cat2011_T3 0.6927699 0.5995124 0.8004495
## age_T3 0.9999896 0.9849612 1.0152274
## sex_T3 0.9490965 0.7469176 1.2082703
## bmi_T3 1.0099599 0.9939527 1.0261746edu3 <- polr(foodst_03_T3 ~ isced_cat2011_T3 + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(edu3)## Call:
## polr(formula = foodst_03_T3 ~ isced_cat2011_T3 + age_T3 + sex_T3 +
## bmi_T3, data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## isced_cat2011_T3 0.08707 0.051998 1.675
## age_T3 0.02219 0.005396 4.113
## sex_T3 0.30008 0.085407 3.513
## bmi_T3 -0.01107 0.005703 -1.942
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.7408 0.3773 -4.6136
## 2|3 -0.4780 0.3722 -1.2844
## 3|4 0.3276 0.3717 0.8813
## 4|5 1.8572 0.3729 4.9801
##
## Residual Deviance: 10502.87
## AIC: 10518.87
## (120 observations deleted due to missingness)ctable_edu3 <- coef(summary(edu3))
p_edu3 <- pnorm(abs(ctable_edu3[, "t value"]), lower.tail = FALSE) * 2
(ctable_edu3 <- cbind(ctable_edu3, "p-value" = round(p_edu3, 4)))## Value Std. Error t value p-value
## isced_cat2011_T3 0.08707306 0.051997575 1.6745599 0.0940
## age_T3 0.02219129 0.005395885 4.1126317 0.0000
## sex_T3 0.30007813 0.085407234 3.5134979 0.0004
## bmi_T3 -0.01107335 0.005702560 -1.9418204 0.0522
## 1|2 -1.74076268 0.377309077 -4.6136252 0.0000
## 2|3 -0.47803727 0.372200841 -1.2843530 0.1990
## 3|4 0.32756755 0.371683264 0.8813083 0.3782
## 4|5 1.85716812 0.372919990 4.9800712 0.0000(ci_edu3 <- confint(edu3, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## isced_cat2011_T3 -0.05518181 0.229241443
## age_T3 0.00745838 0.036971703
## sex_T3 0.06623416 0.533470865
## bmi_T3 -0.02664443 0.004552253exp(cbind(OR = coef(edu3), ci_edu3))## OR 0.3 % 99.7 %
## isced_cat2011_T3 1.0909764 0.9463131 1.257646
## age_T3 1.0224393 1.0074863 1.037664
## sex_T3 1.3499643 1.0684769 1.704839
## bmi_T3 0.9889877 0.9737074 1.004563edu4 <- polr(foodst_04_T3 ~ isced_cat2011_T3 + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(edu4)## Call:
## polr(formula = foodst_04_T3 ~ isced_cat2011_T3 + age_T3 + sex_T3 +
## bmi_T3, data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## isced_cat2011_T3 0.03998 0.051490 0.7764
## age_T3 0.02745 0.005385 5.0982
## sex_T3 0.04184 0.084840 0.4932
## bmi_T3 -0.00467 0.005613 -0.8320
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.2449 0.3717 -3.3495
## 2|3 0.0735 0.3696 0.1989
## 3|4 0.7076 0.3697 1.9140
## 4|5 2.3169 0.3714 6.2379
##
## Residual Deviance: 11626.99
## AIC: 11642.99
## (120 observations deleted due to missingness)ctable_edu4 <- coef(summary(edu4))
p_edu4 <- pnorm(abs(ctable_edu4[, "t value"]), lower.tail = FALSE) * 2
(ctable_edu4 <- cbind(ctable_edu4, "p-value" = round(p_edu4, 4)))## Value Std. Error t value p-value
## isced_cat2011_T3 0.039976344 0.051489972 0.7763909 0.4375
## age_T3 0.027451346 0.005384567 5.0981526 0.0000
## sex_T3 0.041843951 0.084839623 0.4932124 0.6219
## bmi_T3 -0.004670169 0.005613369 -0.8319725 0.4054
## 1|2 -1.244855288 0.371650614 -3.3495311 0.0008
## 2|3 0.073510270 0.369617754 0.1988819 0.8424
## 3|4 0.707645831 0.369726895 1.9139690 0.0556
## 4|5 2.316926769 0.371428899 6.2378743 0.0000(ci_edu4 <- confint(edu4, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## isced_cat2011_T3 -0.10081851 0.18081736
## age_T3 0.01274421 0.04219584
## sex_T3 -0.19042073 0.27368701
## bmi_T3 -0.02001034 0.01069763exp(cbind(OR = coef(edu4), ci_edu4))## OR 0.3 % 99.7 %
## isced_cat2011_T3 1.0407862 0.9040971 1.198196
## age_T3 1.0278316 1.0128258 1.043099
## sex_T3 1.0427317 0.8266113 1.314803
## bmi_T3 0.9953407 0.9801885 1.010755edu5 <- polr(foodst_05_T3 ~ isced_cat2011_T3 + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(edu5)## Call:
## polr(formula = foodst_05_T3 ~ isced_cat2011_T3 + age_T3 + sex_T3 +
## bmi_T3, data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## isced_cat2011_T3 -0.1075927 0.055925 -1.9239
## age_T3 0.0106768 0.005869 1.8193
## sex_T3 0.1355348 0.092450 1.4660
## bmi_T3 0.0009313 0.006156 0.1513
##
## Intercepts:
## Value Std. Error t value
## 1|2 -3.3601 0.4141 -8.1142
## 2|3 -1.9680 0.4036 -4.8759
## 3|4 -1.5465 0.4026 -3.8418
## 4|5 0.0962 0.4014 0.2397
##
## Residual Deviance: 8218.918
## AIC: 8234.918
## (120 observations deleted due to missingness)ctable_edu5 <- coef(summary(edu5))
p_edu5 <- pnorm(abs(ctable_edu5[, "t value"]), lower.tail = FALSE) * 2
(ctable_edu5 <- cbind(ctable_edu5, "p-value" = round(p_edu5, 4)))## Value Std. Error t value p-value
## isced_cat2011_T3 -0.1075926622 0.055925046 -1.9238726 0.0544
## age_T3 0.0106767637 0.005868534 1.8193239 0.0689
## sex_T3 0.1355347750 0.092449939 1.4660342 0.1426
## bmi_T3 0.0009312951 0.006156020 0.1512820 0.8798
## 1|2 -3.3600800422 0.414099989 -8.1141756 0.0000
## 2|3 -1.9679804781 0.403611776 -4.8759243 0.0000
## 3|4 -1.5465192420 0.402551615 -3.8417912 0.0001
## 4|5 0.0962301563 0.401444818 0.2397095 0.8106(ci_edu5 <- confint(edu5, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## isced_cat2011_T3 -0.26102249 0.04492039
## age_T3 -0.00534058 0.02676261
## sex_T3 -0.11910227 0.38681551
## bmi_T3 -0.01581825 0.01786904exp(cbind(OR = coef(edu5), ci_edu5))## OR 0.3 % 99.7 %
## isced_cat2011_T3 0.8979933 0.7702636 1.045945
## age_T3 1.0107340 0.9946737 1.027124
## sex_T3 1.1451490 0.8877170 1.472285
## bmi_T3 1.0009317 0.9843062 1.018030edu6 <- polr(foodst_06_T3 ~ isced_cat2011_T3 + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(edu6)## Call:
## polr(formula = foodst_06_T3 ~ isced_cat2011_T3 + age_T3 + sex_T3 +
## bmi_T3, data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## isced_cat2011_T3 0.44755 0.053972 8.2923
## age_T3 -0.02691 0.005563 -4.8379
## sex_T3 -0.33343 0.086985 -3.8332
## bmi_T3 -0.00548 0.005877 -0.9326
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.0896 0.3819 -2.8530
## 2|3 0.7175 0.3817 1.8799
## 3|4 1.1676 0.3823 3.0541
## 4|5 2.8221 0.3913 7.2118
##
## Residual Deviance: 9562.962
## AIC: 9578.962
## (120 observations deleted due to missingness)ctable_edu6 <- coef(summary(edu6))
p_edu6 <- pnorm(abs(ctable_edu6[, "t value"]), lower.tail = FALSE) * 2
(ctable_edu6 <- cbind(ctable_edu6, "p-value" = round(p_edu6, 4)))## Value Std. Error t value p-value
## isced_cat2011_T3 0.447552787 0.053971949 8.2923222 0.0000
## age_T3 -0.026914149 0.005563196 -4.8378935 0.0000
## sex_T3 -0.333434984 0.086985061 -3.8332442 0.0001
## bmi_T3 -0.005480449 0.005876795 -0.9325574 0.3510
## 1|2 -1.089561110 0.381898171 -2.8530147 0.0043
## 2|3 0.717496324 0.381661777 1.8799271 0.0601
## 3|4 1.167561589 0.382298299 3.0540591 0.0023
## 4|5 2.822052381 0.391311756 7.2117751 0.0000(ci_edu6 <- confint(edu6, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## isced_cat2011_T3 0.30043715 0.59567642
## age_T3 -0.04216387 -0.01173394
## sex_T3 -0.57107457 -0.09520116
## bmi_T3 -0.02160535 0.01054729exp(cbind(OR = coef(edu6), ci_edu6))## OR 0.3 % 99.7 %
## isced_cat2011_T3 1.5644789 1.3504490 1.8142577
## age_T3 0.9734448 0.9587127 0.9883346
## sex_T3 0.7164585 0.5649181 0.9091900
## bmi_T3 0.9945345 0.9786264 1.0106031edu7 <- polr(foodst_07_T3 ~ isced_cat2011_T3 + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(edu7)## Call:
## polr(formula = foodst_07_T3 ~ isced_cat2011_T3 + age_T3 + sex_T3 +
## bmi_T3, data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## isced_cat2011_T3 0.10912 0.057108 1.911
## age_T3 -0.02325 0.005996 -3.877
## sex_T3 -0.35063 0.091742 -3.822
## bmi_T3 0.01337 0.006251 2.139
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.5207 0.4068 -1.2800
## 2|3 1.1376 0.4078 2.7895
## 3|4 1.6540 0.4094 4.0399
## 4|5 3.4256 0.4300 7.9660
##
## Residual Deviance: 7770.595
## AIC: 7786.595
## (120 observations deleted due to missingness)ctable_edu7 <- coef(summary(edu7))
p_edu7 <- pnorm(abs(ctable_edu7[, "t value"]), lower.tail = FALSE) * 2
(ctable_edu7 <- cbind(ctable_edu7, "p-value" = round(p_edu7, 4)))## Value Std. Error t value p-value
## isced_cat2011_T3 0.10911887 0.057107860 1.910750 0.0560
## age_T3 -0.02324836 0.005996308 -3.877112 0.0001
## sex_T3 -0.35062675 0.091742287 -3.821866 0.0001
## bmi_T3 0.01337050 0.006250810 2.139004 0.0324
## 1|2 -0.52070242 0.406812749 -1.279956 0.2006
## 2|3 1.13760163 0.407808338 2.789550 0.0053
## 3|4 1.65399818 0.409413292 4.039923 0.0001
## 4|5 3.42560734 0.430030921 7.965956 0.0000(ci_edu7 <- confint(edu7, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## isced_cat2011_T3 -0.046542262 0.265877410
## age_T3 -0.039708828 -0.006905864
## sex_T3 -0.600312483 -0.098281432
## bmi_T3 -0.003817573 0.030385845exp(cbind(OR = coef(edu7), ci_edu7))## OR 0.3 % 99.7 %
## isced_cat2011_T3 1.1152949 0.9545242 1.3045751
## age_T3 0.9770198 0.9610692 0.9931179
## sex_T3 0.7042466 0.5486402 0.9063938
## bmi_T3 1.0134603 0.9961897 1.0308522edu8 <- polr(foodst_08_T3 ~ isced_cat2011_T3 + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(edu8)## Call:
## polr(formula = foodst_08_T3 ~ isced_cat2011_T3 + age_T3 + sex_T3 +
## bmi_T3, data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## isced_cat2011_T3 -0.050052 0.051532 -0.9713
## age_T3 0.003353 0.005349 0.6268
## sex_T3 0.364969 0.084476 4.3204
## bmi_T3 0.012618 0.005592 2.2564
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.8865 0.3692 -2.4012
## 2|3 0.2769 0.3684 0.7518
## 3|4 0.6117 0.3684 1.6603
## 4|5 2.3501 0.3702 6.3483
##
## Residual Deviance: 11571.75
## AIC: 11587.75
## (120 observations deleted due to missingness)ctable_edu8 <- coef(summary(edu8))
p_edu8 <- pnorm(abs(ctable_edu8[, "t value"]), lower.tail = FALSE) * 2
(ctable_edu8 <- cbind(ctable_edu8, "p-value" = round(p_edu8, 4)))## Value Std. Error t value p-value
## isced_cat2011_T3 -0.050052184 0.051531689 -0.9712894 0.3314
## age_T3 0.003352603 0.005348847 0.6267898 0.5308
## sex_T3 0.364969311 0.084475842 4.3203986 0.0000
## bmi_T3 0.012617638 0.005591845 2.2564358 0.0240
## 1|2 -0.886477965 0.369177834 -2.4012221 0.0163
## 2|3 0.276919209 0.368355726 0.7517712 0.4522
## 3|4 0.611679639 0.368411405 1.6603168 0.0969
## 4|5 2.350133174 0.370199170 6.3482940 0.0000(ci_edu8 <- confint(edu8, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## isced_cat2011_T3 -0.191057399 0.09080663
## age_T3 -0.011274115 0.01798289
## sex_T3 0.133876358 0.59600004
## bmi_T3 -0.002656678 0.02793296exp(cbind(OR = coef(edu8), ci_edu8))## OR 0.3 % 99.7 %
## isced_cat2011_T3 0.9511798 0.8260852 1.095057
## age_T3 1.0033582 0.9887892 1.018146
## sex_T3 1.4404698 1.1432515 1.814845
## bmi_T3 1.0126976 0.9973468 1.028327# path a
# Income -> Consumer attitudes
inc1 <- polr(foodst_01_T3 ~ income_cat_T3 + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(inc1)## Call:
## polr(formula = foodst_01_T3 ~ income_cat_T3 + age_T3 + sex_T3 +
## bmi_T3, data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## income_cat_T3 -0.006820 0.021305 -0.3201
## age_T3 0.008562 0.005349 1.6007
## sex_T3 0.012033 0.084429 0.1425
## bmi_T3 -0.008414 0.005657 -1.4873
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.9784 0.3530 -5.6050
## 2|3 -0.8718 0.3510 -2.4841
## 3|4 -0.2829 0.3506 -0.8071
## 4|5 1.4446 0.3515 4.1095
##
## Residual Deviance: 11422.58
## AIC: 11438.58
## (226 observations deleted due to missingness)ctable_inc1 <- coef(summary(inc1))
p_inc1 <- pnorm(abs(ctable_inc1[, "t value"]), lower.tail = FALSE) * 2
(ctable_inc1 <- cbind(ctable_inc1, "p-value" = round(p_inc1, 4)))## Value Std. Error t value p-value
## income_cat_T3 -0.006819589 0.021304981 -0.3200937 0.7489
## age_T3 0.008562444 0.005349283 1.6006714 0.1094
## sex_T3 0.012033406 0.084428573 0.1425276 0.8867
## bmi_T3 -0.008414024 0.005657252 -1.4872989 0.1369
## 1|2 -1.978397566 0.352968965 -5.6050185 0.0000
## 2|3 -0.871783684 0.350950684 -2.4840632 0.0130
## 3|4 -0.282947223 0.350593611 -0.8070518 0.4196
## 4|5 1.444604460 0.351527232 4.1095094 0.0000(ci_inc1 <- confint(inc1, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## income_cat_T3 -0.065095652 0.05143488
## age_T3 -0.006063394 0.02319495
## sex_T3 -0.219109910 0.24273888
## bmi_T3 -0.023874539 0.00707371exp(cbind(OR = coef(inc1), ci_inc1))## OR 0.3 % 99.7 %
## income_cat_T3 0.9932036 0.9369778 1.052781
## age_T3 1.0085992 0.9939550 1.023466
## sex_T3 1.0121061 0.8032334 1.274736
## bmi_T3 0.9916213 0.9764082 1.007099inc2 <- polr(foodst_02_T3 ~ income_cat_T3 + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(inc2)## Call:
## polr(formula = foodst_02_T3 ~ income_cat_T3 + age_T3 + sex_T3 +
## bmi_T3, data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## income_cat_T3 -0.138494 0.022247 -6.2253
## age_T3 -0.005906 0.005560 -1.0621
## sex_T3 -0.067005 0.087915 -0.7622
## bmi_T3 0.012509 0.005913 2.1154
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.5463 0.3627 -1.5063
## 2|3 0.8030 0.3627 2.2138
## 3|4 1.5006 0.3637 4.1261
## 4|5 3.1166 0.3736 8.3415
##
## Residual Deviance: 9594.474
## AIC: 9610.474
## (226 observations deleted due to missingness)ctable_inc2 <- coef(summary(inc2))
p_inc2 <- pnorm(abs(ctable_inc2[, "t value"]), lower.tail = FALSE) * 2
(ctable_inc2 <- cbind(ctable_inc2, "p-value" = round(p_inc2, 4)))## Value Std. Error t value p-value
## income_cat_T3 -0.13849355 0.022246791 -6.225327 0.0000
## age_T3 -0.00590597 0.005560495 -1.062130 0.2882
## sex_T3 -0.06700520 0.087915213 -0.762157 0.4460
## bmi_T3 0.01250916 0.005913420 2.115386 0.0344
## 1|2 -0.54627087 0.362656169 -1.506305 0.1320
## 2|3 0.80296942 0.362705927 2.213830 0.0268
## 3|4 1.50057070 0.363681065 4.126062 0.0000
## 4|5 3.11655708 0.373619188 8.341534 0.0000(ci_inc2 <- confint(inc2, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## income_cat_T3 -0.199435129 -0.07774578
## age_T3 -0.021129585 0.00928617
## sex_T3 -0.306600214 0.17442550
## bmi_T3 -0.003694691 0.02865998exp(cbind(OR = coef(inc2), ci_inc2))## OR 0.3 % 99.7 %
## income_cat_T3 0.8706689 0.8191934 0.9251996
## age_T3 0.9941114 0.9790921 1.0093294
## sex_T3 0.9351903 0.7359448 1.1905620
## bmi_T3 1.0125877 0.9963121 1.0290746inc3 <- polr(foodst_03_T3 ~ income_cat_T3 + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(inc3)## Call:
## polr(formula = foodst_03_T3 ~ income_cat_T3 + age_T3 + sex_T3 +
## bmi_T3, data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## income_cat_T3 -0.03566 0.021795 -1.636
## age_T3 0.02240 0.005444 4.114
## sex_T3 0.26987 0.085429 3.159
## bmi_T3 -0.01544 0.005804 -2.661
##
## Intercepts:
## Value Std. Error t value
## 1|2 -2.2394 0.3623 -6.1804
## 2|3 -0.9732 0.3566 -2.7292
## 3|4 -0.1708 0.3559 -0.4800
## 4|5 1.3637 0.3567 3.8234
##
## Residual Deviance: 10202.30
## AIC: 10218.30
## (226 observations deleted due to missingness)ctable_inc3 <- coef(summary(inc3))
p_inc3 <- pnorm(abs(ctable_inc3[, "t value"]), lower.tail = FALSE) * 2
(ctable_inc3 <- cbind(ctable_inc3, "p-value" = round(p_inc3, 4)))## Value Std. Error t value p-value
## income_cat_T3 -0.03565796 0.021795492 -1.6360244 0.1018
## age_T3 0.02239599 0.005444416 4.1135703 0.0000
## sex_T3 0.26986511 0.085428754 3.1589494 0.0016
## bmi_T3 -0.01544226 0.005803502 -2.6608515 0.0078
## 1|2 -2.23935743 0.362330478 -6.1804280 0.0000
## 2|3 -0.97321571 0.356595446 -2.7291871 0.0063
## 3|4 -0.17082816 0.355878189 -0.4800186 0.6312
## 4|5 1.36366989 0.356666836 3.8233717 0.0001(ci_inc3 <- confint(inc3, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## income_cat_T3 -0.09530923 0.0239082631
## age_T3 0.00753342 0.0373126174
## sex_T3 0.03594088 0.5032927956
## bmi_T3 -0.03129550 0.0004535244exp(cbind(OR = coef(inc3), ci_inc3))## OR 0.3 % 99.7 %
## income_cat_T3 0.9649703 0.9090918 1.024196
## age_T3 1.0226487 1.0075619 1.038017
## sex_T3 1.3097878 1.0365946 1.654159
## bmi_T3 0.9846764 0.9691891 1.000454inc4 <- polr(foodst_04_T3 ~ income_cat_T3 + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(inc4)## Call:
## polr(formula = foodst_04_T3 ~ income_cat_T3 + age_T3 + sex_T3 +
## bmi_T3, data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## income_cat_T3 -0.086625 0.021375 -4.0526
## age_T3 0.028524 0.005415 5.2672
## sex_T3 -0.002181 0.084846 -0.0257
## bmi_T3 -0.010322 0.005708 -1.8082
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.7964 0.3558 -5.0488
## 2|3 -0.4902 0.3532 -1.3878
## 3|4 0.1510 0.3530 0.4277
## 4|5 1.7640 0.3543 4.9794
##
## Residual Deviance: 11302.07
## AIC: 11318.07
## (226 observations deleted due to missingness)ctable_inc4 <- coef(summary(inc4))
p_inc4 <- pnorm(abs(ctable_inc4[, "t value"]), lower.tail = FALSE) * 2
(ctable_inc4 <- cbind(ctable_inc4, "p-value" = round(p_inc4, 4)))## Value Std. Error t value p-value
## income_cat_T3 -0.086624858 0.021375264 -4.05257479 0.0001
## age_T3 0.028524413 0.005415438 5.26723997 0.0000
## sex_T3 -0.002180812 0.084846004 -0.02570318 0.9795
## bmi_T3 -0.010321558 0.005708348 -1.80815147 0.0706
## 1|2 -1.796422110 0.355810710 -5.04881404 0.0000
## 2|3 -0.490180642 0.353210556 -1.38778594 0.1652
## 3|4 0.150992266 0.353039056 0.42769281 0.6689
## 4|5 1.763998563 0.354256445 4.97943958 0.0000(ci_inc4 <- confint(inc4, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## income_cat_T3 -0.14513834 -0.02822309
## age_T3 0.01373679 0.04335757
## sex_T3 -0.23445627 0.22968500
## bmi_T3 -0.02592380 0.00530397exp(cbind(OR = coef(inc4), ci_inc4))## OR 0.3 % 99.7 %
## income_cat_T3 0.9170210 0.8649026 0.9721715
## age_T3 1.0289351 1.0138316 1.0443112
## sex_T3 0.9978216 0.7910008 1.2582036
## bmi_T3 0.9897315 0.9744093 1.0053181inc5 <- polr(foodst_05_T3 ~ income_cat_T3 + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(inc5)## Call:
## polr(formula = foodst_05_T3 ~ income_cat_T3 + age_T3 + sex_T3 +
## bmi_T3, data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## income_cat_T3 -0.0827307 0.023360 -3.54149
## age_T3 0.0076358 0.005908 1.29243
## sex_T3 0.1020467 0.092343 1.10508
## bmi_T3 0.0003209 0.006251 0.05134
##
## Intercepts:
## Value Std. Error t value
## 1|2 -3.5534 0.3984 -8.9185
## 2|3 -2.1591 0.3872 -5.5765
## 3|4 -1.7321 0.3860 -4.4868
## 4|5 -0.1017 0.3847 -0.2642
##
## Residual Deviance: 8004.392
## AIC: 8020.392
## (226 observations deleted due to missingness)ctable_inc5 <- coef(summary(inc5))
p_inc5 <- pnorm(abs(ctable_inc5[, "t value"]), lower.tail = FALSE) * 2
(ctable_inc5 <- cbind(ctable_inc5, "p-value" = round(p_inc5, 4)))## Value Std. Error t value p-value
## income_cat_T3 -0.0827306521 0.023360440 -3.54148518 0.0004
## age_T3 0.0076357735 0.005908083 1.29242830 0.1962
## sex_T3 0.1020466612 0.092343438 1.10507756 0.2691
## bmi_T3 0.0003209228 0.006251465 0.05133562 0.9591
## 1|2 -3.5534075150 0.398431539 -8.91848955 0.0000
## 2|3 -2.1591417676 0.387186545 -5.57648966 0.0000
## 3|4 -1.7320868667 0.386043885 -4.48676156 0.0000
## 4|5 -0.1016551312 0.384739574 -0.26421803 0.7916(ci_inc5 <- confint(inc5, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## income_cat_T3 -0.146740031 -0.01895159
## age_T3 -0.008491954 0.02382696
## sex_T3 -0.152243534 0.35308108
## bmi_T3 -0.016685518 0.01752532exp(cbind(OR = coef(inc5), ci_inc5))## OR 0.3 % 99.7 %
## income_cat_T3 0.9205991 0.8635184 0.9812269
## age_T3 1.0076650 0.9915440 1.0241131
## sex_T3 1.1074351 0.8587791 1.4234465
## bmi_T3 1.0003210 0.9834529 1.0176798inc6 <- polr(foodst_06_T3 ~ income_cat_T3 + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(inc6)## Call:
## polr(formula = foodst_06_T3 ~ income_cat_T3 + age_T3 + sex_T3 +
## bmi_T3, data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## income_cat_T3 0.170060 0.022281 7.632
## age_T3 -0.020352 0.005567 -3.656
## sex_T3 -0.333059 0.086558 -3.848
## bmi_T3 -0.006149 0.005942 -1.035
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.4395 0.3639 -3.9557
## 2|3 0.3561 0.3632 0.9806
## 3|4 0.8114 0.3638 2.2307
## 4|5 2.5106 0.3737 6.7185
##
## Residual Deviance: 9379.917
## AIC: 9395.917
## (226 observations deleted due to missingness)ctable_inc6 <- coef(summary(inc6))
p_inc6 <- pnorm(abs(ctable_inc6[, "t value"]), lower.tail = FALSE) * 2
(ctable_inc6 <- cbind(ctable_inc6, "p-value" = round(p_inc6, 4)))## Value Std. Error t value p-value
## income_cat_T3 0.170059909 0.022281134 7.6324622 0.0000
## age_T3 -0.020351863 0.005567386 -3.6555507 0.0003
## sex_T3 -0.333059265 0.086558137 -3.8478100 0.0001
## bmi_T3 -0.006149077 0.005942326 -1.0347931 0.3008
## 1|2 -1.439534486 0.363918331 -3.9556526 0.0001
## 2|3 0.356117419 0.363170885 0.9805781 0.3268
## 3|4 0.811411869 0.363750959 2.2306797 0.0257
## 4|5 2.510579525 0.373680681 6.7185157 0.0000(ci_inc6 <- confint(inc6, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## income_cat_T3 0.10926407 0.231139842
## age_T3 -0.03560932 -0.005156541
## sex_T3 -0.56957434 -0.096041078
## bmi_T3 -0.02244974 0.010061936exp(cbind(OR = coef(inc6), ci_inc6))## OR 0.3 % 99.7 %
## income_cat_T3 1.1853759 1.1154569 1.2600354
## age_T3 0.9798538 0.9650172 0.9948567
## sex_T3 0.7167277 0.5657662 0.9084267
## bmi_T3 0.9938698 0.9778004 1.0101127inc7 <- polr(foodst_07_T3 ~ income_cat_T3 + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(inc7)## Call:
## polr(formula = foodst_07_T3 ~ income_cat_T3 + age_T3 + sex_T3 +
## bmi_T3, data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## income_cat_T3 0.07786 0.023987 3.246
## age_T3 -0.02107 0.006025 -3.497
## sex_T3 -0.30721 0.091777 -3.347
## bmi_T3 0.01645 0.006328 2.600
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.2909 0.3903 -0.7454
## 2|3 1.3522 0.3914 3.4544
## 3|4 1.8674 0.3932 4.7497
## 4|5 3.6455 0.4152 8.7803
##
## Residual Deviance: 7564.081
## AIC: 7580.081
## (226 observations deleted due to missingness)ctable_inc7 <- coef(summary(inc7))
p_inc7 <- pnorm(abs(ctable_inc7[, "t value"]), lower.tail = FALSE) * 2
(ctable_inc7 <- cbind(ctable_inc7, "p-value" = round(p_inc7, 4)))## Value Std. Error t value p-value
## income_cat_T3 0.07785923 0.023986991 3.2458939 0.0012
## age_T3 -0.02107233 0.006025159 -3.4973900 0.0005
## sex_T3 -0.30721183 0.091776828 -3.3473790 0.0008
## bmi_T3 0.01645370 0.006327668 2.6002780 0.0093
## 1|2 -0.29089088 0.390259263 -0.7453785 0.4560
## 2|3 1.35221107 0.391445380 3.4544055 0.0006
## 3|4 1.86739516 0.393163855 4.7496613 0.0000
## 4|5 3.64553908 0.415193347 8.7803408 0.0000(ci_inc7 <- confint(inc7, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## income_cat_T3 0.0123727634 0.143591233
## age_T3 -0.0376085431 -0.004647152
## sex_T3 -0.5569612135 -0.054745642
## bmi_T3 -0.0009435682 0.033681321exp(cbind(OR = coef(inc7), ci_inc7))## OR 0.3 % 99.7 %
## income_cat_T3 1.0809705 1.0124496 1.1544121
## age_T3 0.9791481 0.9630899 0.9953636
## sex_T3 0.7354948 0.5729475 0.9467259
## bmi_T3 1.0165898 0.9990569 1.0342550inc8 <- polr(foodst_08_T3 ~ income_cat_T3 + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(inc8)## Call:
## polr(formula = foodst_08_T3 ~ income_cat_T3 + age_T3 + sex_T3 +
## bmi_T3, data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## income_cat_T3 -0.056197 0.021520 -2.6114
## age_T3 0.001453 0.005371 0.2706
## sex_T3 0.329620 0.084563 3.8979
## bmi_T3 0.010945 0.005667 1.9313
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.1596 0.3531 -3.2840
## 2|3 0.0181 0.3519 0.0513
## 3|4 0.3590 0.3519 1.0201
## 4|5 2.1033 0.3538 5.9457
##
## Residual Deviance: 11244.30
## AIC: 11260.30
## (226 observations deleted due to missingness)ctable_inc8 <- coef(summary(inc8))
p_inc8 <- pnorm(abs(ctable_inc8[, "t value"]), lower.tail = FALSE) * 2
(ctable_inc8 <- cbind(ctable_inc8, "p-value" = round(p_inc8, 4)))## Value Std. Error t value p-value
## income_cat_T3 -0.056196614 0.02151969 -2.61140443 0.0090
## age_T3 0.001453217 0.00537064 0.27058539 0.7867
## sex_T3 0.329620066 0.08456285 3.89792999 0.0001
## bmi_T3 0.010945036 0.00566716 1.93130872 0.0534
## 1|2 -1.159643402 0.35311527 -3.28403639 0.0010
## 2|3 0.018063244 0.35189223 0.05133175 0.9591
## 3|4 0.358986158 0.35192639 1.02006035 0.3077
## 4|5 2.103314122 0.35375279 5.94571738 0.0000(ci_inc8 <- confint(inc8, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## income_cat_T3 -0.115079727 0.002625074
## age_T3 -0.013225470 0.016150881
## sex_T3 0.098301778 0.560895014
## bmi_T3 -0.004537679 0.026464545exp(cbind(OR = coef(inc8), ci_inc8))## OR 0.3 % 99.7 %
## income_cat_T3 0.9453532 0.8912951 1.002629
## age_T3 1.0014543 0.9868616 1.016282
## sex_T3 1.3904398 1.1032957 1.752240
## bmi_T3 1.0110052 0.9954726 1.026818# path a
# Migration background -> Consumer attitudes
mig1 <- polr(foodst_01_T3 ~ migration + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(mig1)## Call:
## polr(formula = foodst_01_T3 ~ migration + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## migration 0.181742 0.085591 2.1234
## age_T3 0.010845 0.005194 2.0879
## sex_T3 0.054131 0.082561 0.6556
## bmi_T3 -0.009229 0.005408 -1.7065
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.6004 0.3458 -4.6277
## 2|3 -0.4966 0.3440 -1.4435
## 3|4 0.0909 0.3438 0.2643
## 4|5 1.7940 0.3451 5.1983
##
## Residual Deviance: 12108.49
## AIC: 12124.49ctable_mig1 <- coef(summary(mig1))
p_mig1 <- pnorm(abs(ctable_mig1[, "t value"]), lower.tail = FALSE) * 2
(ctable_mig1 <- cbind(ctable_mig1, "p-value" = round(p_mig1, 4)))## Value Std. Error t value p-value
## migration 0.181741624 0.085591422 2.1233626 0.0337
## age_T3 0.010845490 0.005194448 2.0879003 0.0368
## sex_T3 0.054130582 0.082561400 0.6556403 0.5121
## bmi_T3 -0.009228956 0.005408269 -1.7064530 0.0879
## 1|2 -1.600412630 0.345833875 -4.6276919 0.0000
## 2|3 -0.496615073 0.344043119 -1.4434675 0.1489
## 3|4 0.090866712 0.343767875 0.2643258 0.7915
## 4|5 1.793987131 0.345107051 5.1983497 0.0000(ci_mig1 <- confint(mig1, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## migration -0.051978122 0.416259389
## age_T3 -0.003356617 0.025054688
## sex_T3 -0.171867804 0.279764036
## bmi_T3 -0.024009957 0.005576048exp(cbind(OR = coef(mig1), ci_mig1))## OR 0.3 % 99.7 %
## migration 1.1993043 0.9493496 1.516279
## age_T3 1.0109045 0.9966490 1.025371
## sex_T3 1.0556224 0.8420905 1.322818
## bmi_T3 0.9908135 0.9762760 1.005592mig2 <- polr(foodst_02_T3 ~ migration + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(mig2)## Call:
## polr(formula = foodst_02_T3 ~ migration + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## migration 0.1442818 0.089112 1.619111
## age_T3 -0.0038423 0.005397 -0.711920
## sex_T3 0.0003707 0.085541 0.004333
## bmi_T3 0.0191903 0.005622 3.413314
##
## Intercepts:
## Value Std. Error t value
## 1|2 0.4109 0.3538 1.1615
## 2|3 1.7467 0.3546 4.9260
## 3|4 2.4429 0.3559 6.8638
## 4|5 4.0227 0.3655 11.0054
##
## Residual Deviance: 10275.54
## AIC: 10291.54ctable_mig2 <- coef(summary(mig2))
p_mig2 <- pnorm(abs(ctable_mig2[, "t value"]), lower.tail = FALSE) * 2
(ctable_mig2 <- cbind(ctable_mig2, "p-value" = round(p_mig2, 4)))## Value Std. Error t value p-value
## migration 0.1442818240 0.089111742 1.61911124 0.1054
## age_T3 -0.0038423262 0.005397132 -0.71191995 0.4765
## sex_T3 0.0003706695 0.085541349 0.00433322 0.9965
## bmi_T3 0.0191902741 0.005622183 3.41331390 0.0006
## 1|2 0.4109004773 0.353766742 1.16150115 0.2454
## 2|3 1.7466636116 0.354583476 4.92595886 0.0000
## 3|4 2.4429348385 0.355914080 6.86383309 0.0000
## 4|5 4.0227294256 0.365524767 11.00535393 0.0000(ci_mig2 <- confint(mig2, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## migration -0.100495636 0.38712292
## age_T3 -0.018615510 0.01090619
## sex_T3 -0.232700261 0.23533225
## bmi_T3 0.003797474 0.03455699exp(cbind(OR = coef(mig2), ci_mig2))## OR 0.3 % 99.7 %
## migration 1.155210 0.9043891 1.472738
## age_T3 0.996165 0.9815567 1.010966
## sex_T3 1.000371 0.7923910 1.265329
## bmi_T3 1.019376 1.0038047 1.035161mig3 <- polr(foodst_03_T3 ~ migration + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(mig3)## Call:
## polr(formula = foodst_03_T3 ~ migration + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## migration 0.15249 0.088660 1.720
## age_T3 0.02174 0.005288 4.112
## sex_T3 0.31594 0.083433 3.787
## bmi_T3 -0.01441 0.005524 -2.608
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.8686 0.3532 -5.2898
## 2|3 -0.5998 0.3478 -1.7245
## 3|4 0.2047 0.3471 0.5897
## 4|5 1.7229 0.3482 4.9475
##
## Residual Deviance: 10817.00
## AIC: 10833.00ctable_mig3 <- coef(summary(mig3))
p_mig3 <- pnorm(abs(ctable_mig3[, "t value"]), lower.tail = FALSE) * 2
(ctable_mig3 <- cbind(ctable_mig3, "p-value" = round(p_mig3, 4)))## Value Std. Error t value p-value
## migration 0.15248643 0.088660211 1.7198969 0.0855
## age_T3 0.02174284 0.005287952 4.1117686 0.0000
## sex_T3 0.31593695 0.083432769 3.7867250 0.0002
## bmi_T3 -0.01440886 0.005524138 -2.6083460 0.0091
## 1|2 -1.86860557 0.353243875 -5.2898456 0.0000
## 2|3 -0.59975069 0.347775087 -1.7245361 0.0846
## 3|4 0.20466696 0.347076759 0.5896879 0.5554
## 4|5 1.72289619 0.348238400 4.9474618 0.0000(ci_mig3 <- confint(mig3, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## migration -0.089152235 0.3959648342
## age_T3 0.007305595 0.0362281090
## sex_T3 0.087512565 0.5439403738
## bmi_T3 -0.029493137 0.0007273322exp(cbind(OR = coef(mig3), ci_mig3))## OR 0.3 % 99.7 %
## migration 1.1647267 0.9147063 1.485817
## age_T3 1.0219809 1.0073323 1.036892
## sex_T3 1.3715438 1.0914560 1.722782
## bmi_T3 0.9856944 0.9709375 1.000728mig4 <- polr(foodst_04_T3 ~ migration + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(mig4)## Call:
## polr(formula = foodst_04_T3 ~ migration + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## migration 0.281580 0.086290 3.2632
## age_T3 0.027577 0.005252 5.2513
## sex_T3 0.067304 0.082938 0.8115
## bmi_T3 -0.005877 0.005437 -1.0809
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.0151 0.3465 -2.9293
## 2|3 0.2950 0.3443 0.8569
## 3|4 0.9279 0.3444 2.6945
## 4|5 2.5311 0.3466 7.3021
##
## Residual Deviance: 11958.34
## AIC: 11974.34ctable_mig4 <- coef(summary(mig4))
p_mig4 <- pnorm(abs(ctable_mig4[, "t value"]), lower.tail = FALSE) * 2
(ctable_mig4 <- cbind(ctable_mig4, "p-value" = round(p_mig4, 4)))## Value Std. Error t value p-value
## migration 0.28157995 0.086289995 3.2631818 0.0011
## age_T3 0.02757748 0.005251580 5.2512730 0.0000
## sex_T3 0.06730417 0.082938260 0.8114973 0.4171
## bmi_T3 -0.00587731 0.005437414 -1.0809016 0.2797
## 1|2 -1.01510190 0.346538508 -2.9292615 0.0034
## 2|3 0.29500011 0.344259014 0.8569132 0.3915
## 3|4 0.92793048 0.344376372 2.6945242 0.0070
## 4|5 2.53107553 0.346621243 7.3021362 0.0000(ci_mig4 <- confint(mig4, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## migration 0.04596312 0.518038883
## age_T3 0.01323503 0.041959191
## sex_T3 -0.15971364 0.293988918
## bmi_T3 -0.02073456 0.009010957exp(cbind(OR = coef(mig4), ci_mig4))## OR 0.3 % 99.7 %
## migration 1.3252219 1.0470358 1.678732
## age_T3 1.0279613 1.0133230 1.042852
## sex_T3 1.0696208 0.8523878 1.341769
## bmi_T3 0.9941399 0.9794789 1.009052mig5 <- polr(foodst_05_T3 ~ migration + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(mig5)## Call:
## polr(formula = foodst_05_T3 ~ migration + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## migration 0.035542 0.094868 0.3746
## age_T3 0.009063 0.005733 1.5808
## sex_T3 0.147833 0.090407 1.6352
## bmi_T3 0.003142 0.005981 0.5253
##
## Intercepts:
## Value Std. Error t value
## 1|2 -3.0576 0.3880 -7.8797
## 2|3 -1.6627 0.3769 -4.4116
## 3|4 -1.2356 0.3757 -3.2886
## 4|5 0.3971 0.3749 1.0593
##
## Residual Deviance: 8431.253
## AIC: 8447.253ctable_mig5 <- coef(summary(mig5))
p_mig5 <- pnorm(abs(ctable_mig5[, "t value"]), lower.tail = FALSE) * 2
(ctable_mig5 <- cbind(ctable_mig5, "p-value" = round(p_mig5, 4)))## Value Std. Error t value p-value
## migration 0.035541701 0.094868002 0.3746437 0.7079
## age_T3 0.009063196 0.005733329 1.5807913 0.1139
## sex_T3 0.147832795 0.090406963 1.6351926 0.1020
## bmi_T3 0.003142256 0.005981360 0.5253415 0.5993
## 1|2 -3.057643295 0.388039912 -7.8797134 0.0000
## 2|3 -1.662655119 0.376879272 -4.4116385 0.0000
## 3|4 -1.235604288 0.375724682 -3.2885896 0.0010
## 4|5 0.397142191 0.374897171 1.0593363 0.2894(ci_mig5 <- confint(mig5, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## migration -0.221561370 0.29770373
## age_T3 -0.006591711 0.02477494
## sex_T3 -0.101127955 0.39359956
## bmi_T3 -0.013123304 0.01960777exp(cbind(OR = coef(mig5), ci_mig5))## OR 0.3 % 99.7 %
## migration 1.036181 0.8012667 1.346763
## age_T3 1.009104 0.9934300 1.025084
## sex_T3 1.159319 0.9038174 1.482307
## bmi_T3 1.003147 0.9869624 1.019801mig6 <- polr(foodst_06_T3 ~ migration + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(mig6)## Call:
## polr(formula = foodst_06_T3 ~ migration + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## migration -0.27121 0.091143 -2.976
## age_T3 -0.01964 0.005393 -3.643
## sex_T3 -0.40447 0.084555 -4.783
## bmi_T3 -0.01257 0.005690 -2.210
##
## Intercepts:
## Value Std. Error t value
## 1|2 -2.5050 0.3554 -7.0488
## 2|3 -0.7367 0.3532 -2.0858
## 3|4 -0.2850 0.3537 -0.8058
## 4|5 1.3726 0.3630 3.7813
##
## Residual Deviance: 9933.728
## AIC: 9949.728ctable_mig6 <- coef(summary(mig6))
p_mig6 <- pnorm(abs(ctable_mig6[, "t value"]), lower.tail = FALSE) * 2
(ctable_mig6 <- cbind(ctable_mig6, "p-value" = round(p_mig6, 4)))## Value Std. Error t value p-value
## migration -0.27120905 0.091143181 -2.9756373 0.0029
## age_T3 -0.01964414 0.005392681 -3.6427408 0.0003
## sex_T3 -0.40446814 0.084555311 -4.7834741 0.0000
## bmi_T3 -0.01257458 0.005689574 -2.2101100 0.0271
## 1|2 -2.50503695 0.355384208 -7.0488134 0.0000
## 2|3 -0.73669005 0.353191069 -2.0858117 0.0370
## 3|4 -0.28501398 0.353691671 -0.8058261 0.4203
## 4|5 1.37259705 0.362993406 3.7813278 0.0002(ci_mig6 <- confint(mig6, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## migration -0.52186508 -0.023111948
## age_T3 -0.03442076 -0.004924118
## sex_T3 -0.63555955 -0.172987545
## bmi_T3 -0.02819107 0.002937323exp(cbind(OR = coef(mig6), ci_mig6))## OR 0.3 % 99.7 %
## migration 0.7624571 0.5934128 0.9771531
## age_T3 0.9805476 0.9661649 0.9950880
## sex_T3 0.6673316 0.5296390 0.8411481
## bmi_T3 0.9875041 0.9722026 1.0029416mig7 <- polr(foodst_07_T3 ~ migration + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(mig7)## Call:
## polr(formula = foodst_07_T3 ~ migration + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## migration -0.02699 0.096124 -0.2808
## age_T3 -0.02124 0.005853 -3.6286
## sex_T3 -0.34993 0.089619 -3.9046
## bmi_T3 0.01125 0.006073 1.8528
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.7769 0.3794 -2.0478
## 2|3 0.8694 0.3801 2.2874
## 3|4 1.3850 0.3817 3.6281
## 4|5 3.1209 0.4024 7.7553
##
## Residual Deviance: 7989.372
## AIC: 8005.372ctable_mig7 <- coef(summary(mig7))
p_mig7 <- pnorm(abs(ctable_mig7[, "t value"]), lower.tail = FALSE) * 2
(ctable_mig7 <- cbind(ctable_mig7, "p-value" = round(p_mig7, 4)))## Value Std. Error t value p-value
## migration -0.02699475 0.096124413 -0.2808313 0.7788
## age_T3 -0.02123737 0.005852691 -3.6286496 0.0003
## sex_T3 -0.34993122 0.089619428 -3.9046357 0.0001
## bmi_T3 0.01125106 0.006072598 1.8527595 0.0639
## 1|2 -0.77692773 0.379400658 -2.0477764 0.0406
## 2|3 0.86937485 0.380065290 2.2874355 0.0222
## 3|4 1.38497172 0.381737881 3.6280699 0.0003
## 4|5 3.12088536 0.402417607 7.7553400 0.0000(ci_mig7 <- confint(mig7, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## migration -0.29291889 0.233246415
## age_T3 -0.03729851 -0.005281409
## sex_T3 -0.59382951 -0.103423042
## bmi_T3 -0.00545204 0.027776528exp(cbind(OR = coef(mig7), ci_mig7))## OR 0.3 % 99.7 %
## migration 0.9733664 0.7460827 1.2626926
## age_T3 0.9789866 0.9633885 0.9947325
## sex_T3 0.7047366 0.5522085 0.9017454
## bmi_T3 1.0113146 0.9945628 1.0281659mig8 <- polr(foodst_08_T3 ~ migration + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(mig8)## Call:
## polr(formula = foodst_08_T3 ~ migration + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## migration 0.154663 0.086337 1.7914
## age_T3 0.001362 0.005225 0.2606
## sex_T3 0.367983 0.082546 4.4579
## bmi_T3 0.011921 0.005409 2.2038
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.6880 0.3438 -2.0013
## 2|3 0.4683 0.3427 1.3665
## 3|4 0.8029 0.3429 2.3414
## 4|5 2.5294 0.3455 7.3203
##
## Residual Deviance: 11924.12
## AIC: 11940.12ctable_mig8 <- coef(summary(mig8))
p_mig8 <- pnorm(abs(ctable_mig8[, "t value"]), lower.tail = FALSE) * 2
(ctable_mig8 <- cbind(ctable_mig8, "p-value" = round(p_mig8, 4)))## Value Std. Error t value p-value
## migration 0.154663185 0.086337057 1.7913882 0.0732
## age_T3 0.001361524 0.005224751 0.2605911 0.7944
## sex_T3 0.367982826 0.082545535 4.4579374 0.0000
## bmi_T3 0.011921068 0.005409434 2.2037550 0.0275
## 1|2 -0.687961515 0.343752304 -2.0013292 0.0454
## 2|3 0.468328721 0.342727628 1.3664750 0.1718
## 3|4 0.802880645 0.342906862 2.3413957 0.0192
## 4|5 2.529364855 0.345527209 7.3203059 0.0000(ci_mig8 <- confint(mig8, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## migration -0.081222908 0.39109539
## age_T3 -0.012922132 0.01565563
## sex_T3 0.142199612 0.59375602
## bmi_T3 -0.002855227 0.02673648exp(cbind(OR = coef(mig8), ci_mig8))## OR 0.3 % 99.7 %
## migration 1.167265 0.9219881 1.478600
## age_T3 1.001362 0.9871610 1.015779
## sex_T3 1.444817 1.1528067 1.810777
## bmi_T3 1.011992 0.9971488 1.027097# path a
# Unemployment in Household -> Consumer attitudes
une1 <- polr(foodst_01_T3 ~ unemploy + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(une1)## Call:
## polr(formula = foodst_01_T3 ~ unemploy + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## unemploy 0.024730 0.100763 0.2454
## age_T3 0.010897 0.005195 2.0976
## sex_T3 0.047524 0.082520 0.5759
## bmi_T3 -0.009034 0.005428 -1.6644
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.7818 0.3457 -5.1538
## 2|3 -0.6785 0.3438 -1.9736
## 3|4 -0.0916 0.3434 -0.2667
## 4|5 1.6101 0.3445 4.6735
##
## Residual Deviance: 12112.95
## AIC: 12128.95ctable_une1 <- coef(summary(une1))
p_une1 <- pnorm(abs(ctable_une1[, "t value"]), lower.tail = FALSE) * 2
(ctable_une1 <- cbind(ctable_une1, "p-value" = round(p_une1, 4)))## Value Std. Error t value p-value
## unemploy 0.024729790 0.100763443 0.2454242 0.8061
## age_T3 0.010897040 0.005195084 2.0975676 0.0359
## sex_T3 0.047524022 0.082519652 0.5759116 0.5647
## bmi_T3 -0.009034155 0.005428031 -1.6643522 0.0960
## 1|2 -1.781767042 0.345718214 -5.1538130 0.0000
## 2|3 -0.678549264 0.343814303 -1.9735923 0.0484
## 3|4 -0.091586103 0.343443594 -0.2666700 0.7897
## 4|5 1.610139830 0.344527864 4.6734677 0.0000(ci_une1 <- confint(une1, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## unemploy -0.250405598 0.300916885
## age_T3 -0.003307678 0.025106969
## sex_T3 -0.178365557 0.273038078
## bmi_T3 -0.023869164 0.005824807exp(cbind(OR = coef(une1), ci_une1))## OR 0.3 % 99.7 %
## unemploy 1.0250381 0.7784850 1.351097
## age_T3 1.0109566 0.9966978 1.025425
## sex_T3 1.0486714 0.8366365 1.313950
## bmi_T3 0.9910065 0.9764135 1.005842une2 <- polr(foodst_02_T3 ~ unemploy + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(une2)## Call:
## polr(formula = foodst_02_T3 ~ unemploy + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## unemploy 0.097209 0.104458 0.93060
## age_T3 -0.003766 0.005398 -0.69781
## sex_T3 -0.006438 0.085532 -0.07527
## bmi_T3 0.019016 0.005642 3.37052
##
## Intercepts:
## Value Std. Error t value
## 1|2 0.3406 0.3535 0.9633
## 2|3 1.6763 0.3543 4.7306
## 3|4 2.3723 0.3556 6.6705
## 4|5 3.9514 0.3651 10.8224
##
## Residual Deviance: 10277.29
## AIC: 10293.29ctable_une2 <- coef(summary(une2))
p_une2 <- pnorm(abs(ctable_une2[, "t value"]), lower.tail = FALSE) * 2
(ctable_une2 <- cbind(ctable_une2, "p-value" = round(p_une2, 4)))## Value Std. Error t value p-value
## unemploy 0.097209282 0.104458293 0.93060378 0.3521
## age_T3 -0.003766443 0.005397541 -0.69780711 0.4853
## sex_T3 -0.006437898 0.085532304 -0.07526861 0.9400
## bmi_T3 0.019015691 0.005641776 3.37051509 0.0008
## 1|2 0.340560038 0.353519836 0.96334068 0.3354
## 2|3 1.676300341 0.354349048 4.73064723 0.0000
## 3|4 2.372302813 0.355641528 6.67048876 0.0000
## 4|5 3.951400818 0.365113789 10.82238178 0.0000(ci_une2 <- confint(une2, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## unemploy -0.190303910 0.38145858
## age_T3 -0.018542186 0.01098176
## sex_T3 -0.239469204 0.22851357
## bmi_T3 0.003568907 0.03443532exp(cbind(OR = coef(une2), ci_une2))## OR 0.3 % 99.7 %
## unemploy 1.1020910 0.8267079 1.464419
## age_T3 0.9962406 0.9816287 1.011042
## sex_T3 0.9935828 0.7870455 1.256731
## bmi_T3 1.0191976 1.0035753 1.035035une3 <- polr(foodst_03_T3 ~ unemploy + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(une3)## Call:
## polr(formula = foodst_03_T3 ~ unemploy + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## unemploy 0.16830 0.106223 1.584
## age_T3 0.02189 0.005284 4.143
## sex_T3 0.30693 0.083457 3.678
## bmi_T3 -0.01488 0.005545 -2.684
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.8797 0.3537 -5.3146
## 2|3 -0.6111 0.3482 -1.7552
## 3|4 0.1930 0.3474 0.5555
## 4|5 1.7111 0.3485 4.9093
##
## Residual Deviance: 10817.44
## AIC: 10833.44ctable_une3 <- coef(summary(une3))
p_une3 <- pnorm(abs(ctable_une3[, "t value"]), lower.tail = FALSE) * 2
(ctable_une3 <- cbind(ctable_une3, "p-value" = round(p_une3, 4)))## Value Std. Error t value p-value
## unemploy 0.16830157 0.106222742 1.584421 0.1131
## age_T3 0.02189332 0.005284487 4.142942 0.0000
## sex_T3 0.30692733 0.083456865 3.677676 0.0002
## bmi_T3 -0.01488380 0.005545100 -2.684135 0.0073
## 1|2 -1.87967337 0.353681884 -5.314588 0.0000
## 2|3 -0.61109587 0.348154789 -1.755242 0.0792
## 3|4 0.19298095 0.347392944 0.555512 0.5785
## 4|5 1.71107197 0.348534897 4.909328 0.0000(ci_une3 <- confint(une3, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## unemploy -0.12088202 0.460511676
## age_T3 0.00746569 0.036369285
## sex_T3 0.07843332 0.534993000
## bmi_T3 -0.03002637 0.000308599exp(cbind(OR = coef(une3), ci_une3))## OR 0.3 % 99.7 %
## unemploy 1.1832934 0.8861385 1.584885
## age_T3 1.0221347 1.0074936 1.037039
## sex_T3 1.3592422 1.0815912 1.707436
## bmi_T3 0.9852264 0.9704199 1.000309une4 <- polr(foodst_04_T3 ~ unemploy + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(une4)## Call:
## polr(formula = foodst_04_T3 ~ unemploy + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## unemploy 0.284269 0.102432 2.7752
## age_T3 0.027788 0.005252 5.2911
## sex_T3 0.053867 0.082955 0.6494
## bmi_T3 -0.006642 0.005465 -1.2153
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.0580 0.3470 -3.0493
## 2|3 0.2519 0.3447 0.7308
## 3|4 0.8842 0.3448 2.5646
## 4|5 2.4859 0.3469 7.1658
##
## Residual Deviance: 11961.30
## AIC: 11977.30ctable_une4 <- coef(summary(une4))
p_une4 <- pnorm(abs(ctable_une4[, "t value"]), lower.tail = FALSE) * 2
(ctable_une4 <- cbind(ctable_une4, "p-value" = round(p_une4, 4)))## Value Std. Error t value p-value
## unemploy 0.284269397 0.102432494 2.7751877 0.0055
## age_T3 0.027787997 0.005251802 5.2911358 0.0000
## sex_T3 0.053866899 0.082955098 0.6493501 0.5161
## bmi_T3 -0.006642202 0.005465408 -1.2153168 0.2242
## 1|2 -1.058048807 0.346986105 -3.0492541 0.0023
## 2|3 0.251892610 0.344692232 0.7307754 0.4649
## 3|4 0.884206041 0.344768160 2.5646395 0.0103
## 4|5 2.485891424 0.346911468 7.1657805 0.0000(ci_une4 <- confint(une4, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## unemploy 0.004677695 0.565173140
## age_T3 0.013444217 0.042169550
## sex_T3 -0.173210705 0.280583344
## bmi_T3 -0.021577777 0.008320673exp(cbind(OR = coef(une4), ci_une4))## OR 0.3 % 99.7 %
## unemploy 1.3287909 1.0046887 1.759752
## age_T3 1.0281777 1.0135350 1.043071
## sex_T3 1.0553441 0.8409604 1.323902
## bmi_T3 0.9933798 0.9786534 1.008355une5 <- polr(foodst_05_T3 ~ unemploy + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(une5)## Call:
## polr(formula = foodst_05_T3 ~ unemploy + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## unemploy 0.370465 0.117890 3.1425
## age_T3 0.009375 0.005741 1.6331
## sex_T3 0.140318 0.090489 1.5507
## bmi_T3 0.001744 0.006003 0.2906
##
## Intercepts:
## Value Std. Error t value
## 1|2 -2.7371 0.3918 -6.9859
## 2|3 -1.3419 0.3808 -3.5237
## 3|4 -0.9147 0.3797 -2.4089
## 4|5 0.7207 0.3792 1.9008
##
## Residual Deviance: 8421.132
## AIC: 8437.132ctable_une5 <- coef(summary(une5))
p_une5 <- pnorm(abs(ctable_une5[, "t value"]), lower.tail = FALSE) * 2
(ctable_une5 <- cbind(ctable_une5, "p-value" = round(p_une5, 4)))## Value Std. Error t value p-value
## unemploy 0.370465143 0.117889562 3.1424762 0.0017
## age_T3 0.009374915 0.005740631 1.6330809 0.1025
## sex_T3 0.140317628 0.090488620 1.5506660 0.1210
## bmi_T3 0.001744237 0.006003177 0.2905523 0.7714
## 1|2 -2.737124125 0.391806490 -6.9859081 0.0000
## 2|3 -1.341864182 0.380812480 -3.5236875 0.0004
## 3|4 -0.914692143 0.379716437 -2.4088821 0.0160
## 4|5 0.720744420 0.379174613 1.9008246 0.0573(ci_une5 <- confint(une5, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## unemploy 0.053435579 0.69923829
## age_T3 -0.006296284 0.02510638
## sex_T3 -0.108869085 0.38630438
## bmi_T3 -0.014581684 0.01826884exp(cbind(OR = coef(une5), ci_une5))## OR 0.3 % 99.7 %
## unemploy 1.448408 1.0548890 2.012219
## age_T3 1.009419 0.9937235 1.025424
## sex_T3 1.150639 0.8968478 1.471533
## bmi_T3 1.001746 0.9855241 1.018437une6 <- polr(foodst_06_T3 ~ unemploy + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(une6)## Call:
## polr(formula = foodst_06_T3 ~ unemploy + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## unemploy -0.45705 0.109811 -4.162
## age_T3 -0.01994 0.005398 -3.693
## sex_T3 -0.39284 0.084601 -4.643
## bmi_T3 -0.01146 0.005709 -2.008
##
## Intercepts:
## Value Std. Error t value
## 1|2 -2.6581 0.3585 -7.4142
## 2|3 -0.8868 0.3562 -2.4898
## 3|4 -0.4348 0.3566 -1.2193
## 4|5 1.2229 0.3658 3.3428
##
## Residual Deviance: 9924.972
## AIC: 9940.972ctable_une6 <- coef(summary(une6))
p_une6 <- pnorm(abs(ctable_une6[, "t value"]), lower.tail = FALSE) * 2
(ctable_une6 <- cbind(ctable_une6, "p-value" = round(p_une6, 4)))## Value Std. Error t value p-value
## unemploy -0.45704589 0.109810951 -4.162116 0.0000
## age_T3 -0.01993793 0.005398242 -3.693412 0.0002
## sex_T3 -0.39283735 0.084600734 -4.643427 0.0000
## bmi_T3 -0.01146270 0.005709027 -2.007819 0.0447
## 1|2 -2.65809571 0.358516367 -7.414154 0.0000
## 2|3 -0.88675270 0.356152916 -2.489809 0.0128
## 3|4 -0.43482467 0.356626041 -1.219273 0.2227
## 4|5 1.22287803 0.365827648 3.342771 0.0008(ci_une6 <- confint(une6, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## unemploy -0.76023313 -0.159026622
## age_T3 -0.03472753 -0.005200483
## sex_T3 -0.62403357 -0.161214004
## bmi_T3 -0.02713043 0.004104280exp(cbind(OR = coef(une6), ci_une6))## OR 0.3 % 99.7 %
## unemploy 0.6331513 0.4675574 0.8529737
## age_T3 0.9802595 0.9658686 0.9948130
## sex_T3 0.6751386 0.5357790 0.8511099
## bmi_T3 0.9886028 0.9732343 1.0041127une7 <- polr(foodst_07_T3 ~ unemploy + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(une7)## Call:
## polr(formula = foodst_07_T3 ~ unemploy + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## unemploy -0.34625 0.119857 -2.889
## age_T3 -0.02143 0.005858 -3.658
## sex_T3 -0.34208 0.089675 -3.815
## bmi_T3 0.01285 0.006103 2.106
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.0731 0.3824 -2.8061
## 2|3 0.5756 0.3828 1.5036
## 3|4 1.0913 0.3845 2.8384
## 4|5 2.8271 0.4050 6.9800
##
## Residual Deviance: 7980.777
## AIC: 7996.777ctable_une7 <- coef(summary(une7))
p_une7 <- pnorm(abs(ctable_une7[, "t value"]), lower.tail = FALSE) * 2
(ctable_une7 <- cbind(ctable_une7, "p-value" = round(p_une7, 4)))## Value Std. Error t value p-value
## unemploy -0.34625309 0.119856796 -2.888890 0.0039
## age_T3 -0.02142816 0.005857769 -3.658076 0.0003
## sex_T3 -0.34208108 0.089674605 -3.814693 0.0001
## bmi_T3 0.01285085 0.006103415 2.105518 0.0352
## 1|2 -1.07309390 0.382421369 -2.806051 0.0050
## 2|3 0.57563973 0.382840624 1.503601 0.1327
## 3|4 1.09132044 0.384485933 2.838388 0.0045
## 4|5 2.82708553 0.405023869 6.980047 0.0000(ci_une7 <- confint(une7, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## unemploy -0.681005028 -0.024386737
## age_T3 -0.037503322 -0.005458346
## sex_T3 -0.586121610 -0.095414340
## bmi_T3 -0.003935069 0.029461553exp(cbind(OR = coef(une7), ci_une7))## OR 0.3 % 99.7 %
## unemploy 0.7073334 0.5061081 0.9759082
## age_T3 0.9787998 0.9631912 0.9945565
## sex_T3 0.7102906 0.5564814 0.9089962
## bmi_T3 1.0129338 0.9960727 1.0298998une8 <- polr(foodst_08_T3 ~ unemploy + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(une8)## Call:
## polr(formula = foodst_08_T3 ~ unemploy + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## unemploy 0.003809 0.103379 0.03685
## age_T3 0.001348 0.005224 0.25798
## sex_T3 0.364149 0.082486 4.41466
## bmi_T3 0.012264 0.005426 2.26022
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.8555 0.3446 -2.4822
## 2|3 0.3007 0.3435 0.8753
## 3|4 0.6349 0.3436 1.8476
## 4|5 2.3597 0.3460 6.8202
##
## Residual Deviance: 11927.33
## AIC: 11943.33ctable_une8 <- coef(summary(une8))
p_une8 <- pnorm(abs(ctable_une8[, "t value"]), lower.tail = FALSE) * 2
(ctable_une8 <- cbind(ctable_une8, "p-value" = round(p_une8, 4)))## Value Std. Error t value p-value
## unemploy 0.003809375 0.103378987 0.03684864 0.9706
## age_T3 0.001347786 0.005224286 0.25798481 0.7964
## sex_T3 0.364149359 0.082486419 4.41465837 0.0000
## bmi_T3 0.012263651 0.005425878 2.26021502 0.0238
## 1|2 -0.855467384 0.344634014 -2.48224885 0.0131
## 2|3 0.300698362 0.343525904 0.87532951 0.3814
## 3|4 0.634926406 0.343648010 1.84760681 0.0647
## 4|5 2.359663792 0.345981121 6.82020968 0.0000(ci_une8 <- confint(une8, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## unemploy -0.278670922 0.28697125
## age_T3 -0.012935471 0.01563979
## sex_T3 0.138517576 0.58975032
## bmi_T3 -0.002557929 0.02712369exp(cbind(OR = coef(une8), ci_une8))## OR 0.3 % 99.7 %
## unemploy 1.003817 0.7567889 1.332386
## age_T3 1.001349 0.9871478 1.015763
## sex_T3 1.439289 1.1485699 1.803538
## bmi_T3 1.012339 0.9974453 1.027495# path a
# Single parenthood -> Consumer attitudes
sin1 <- polr(foodst_01_T3 ~ singlepar + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(sin1)## Call:
## polr(formula = foodst_01_T3 ~ singlepar + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## singlepar -0.39417 0.093505 -4.2155
## age_T3 0.01124 0.005199 2.1614
## sex_T3 0.07625 0.082871 0.9201
## bmi_T3 -0.00959 0.005424 -1.7680
##
## Intercepts:
## Value Std. Error t value
## 1|2 -2.1969 0.3455 -6.3587
## 2|3 -1.0927 0.3433 -3.1825
## 3|4 -0.5043 0.3428 -1.4712
## 4|5 1.2076 0.3434 3.5165
##
## Residual Deviance: 12040.34
## AIC: 12056.34
## (16 observations deleted due to missingness)ctable_sin1 <- coef(summary(sin1))
p_sin1 <- pnorm(abs(ctable_sin1[, "t value"]), lower.tail = FALSE) * 2
(ctable_sin1 <- cbind(ctable_sin1, "p-value" = round(p_sin1, 4)))## Value Std. Error t value p-value
## singlepar -0.394169402 0.093505058 -4.2154875 0.0000
## age_T3 0.011236582 0.005198780 2.1613883 0.0307
## sex_T3 0.076248675 0.082870825 0.9200907 0.3575
## bmi_T3 -0.009590377 0.005424487 -1.7679784 0.0771
## 1|2 -2.196939083 0.345500783 -6.3587094 0.0000
## 2|3 -1.092699509 0.343343017 -3.1825302 0.0015
## 3|4 -0.504331187 0.342805029 -1.4711896 0.1412
## 4|5 1.207644153 0.343417845 3.5165446 0.0004(ci_sin1 <- confint(sin1, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## singlepar -0.649821305 -0.138238649
## age_T3 -0.002977701 0.025457117
## sex_T3 -0.150596412 0.302727212
## bmi_T3 -0.024415954 0.005258776exp(cbind(OR = coef(sin1), ci_sin1))## OR 0.3 % 99.7 %
## singlepar 0.6742398 0.5221391 0.8708908
## age_T3 1.0112999 0.9970267 1.0257839
## sex_T3 1.0792309 0.8601948 1.3535452
## bmi_T3 0.9904555 0.9758797 1.0052726sin2 <- polr(foodst_02_T3 ~ singlepar + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(sin2)## Call:
## polr(formula = foodst_02_T3 ~ singlepar + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## singlepar -0.064971 0.095748 -0.67857
## age_T3 -0.003718 0.005405 -0.68785
## sex_T3 0.001333 0.085836 0.01553
## bmi_T3 0.019435 0.005631 3.45164
##
## Intercepts:
## Value Std. Error t value
## 1|2 0.1914 0.3519 0.5440
## 2|3 1.5241 0.3526 4.3227
## 3|4 2.2178 0.3538 6.2692
## 4|5 3.7974 0.3632 10.4561
##
## Residual Deviance: 10241.05
## AIC: 10257.05
## (16 observations deleted due to missingness)ctable_sin2 <- coef(summary(sin2))
p_sin2 <- pnorm(abs(ctable_sin2[, "t value"]), lower.tail = FALSE) * 2
(ctable_sin2 <- cbind(ctable_sin2, "p-value" = round(p_sin2, 4)))## Value Std. Error t value p-value
## singlepar -0.064971368 0.095748036 -0.6785661 0.4974
## age_T3 -0.003717549 0.005404611 -0.6878476 0.4915
## sex_T3 0.001332873 0.085835621 0.0155282 0.9876
## bmi_T3 0.019434733 0.005630574 3.4516432 0.0006
## 1|2 0.191409784 0.351858786 0.5439960 0.5864
## 2|3 1.524075695 0.352575175 4.3226971 0.0000
## 3|4 2.217803918 0.353761582 6.2692051 0.0000
## 4|5 3.797361277 0.363171961 10.4560971 0.0000(ci_sin2 <- confint(sin2, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## singlepar -0.328676552 0.19535534
## age_T3 -0.018511705 0.01105096
## sex_T3 -0.232562105 0.23707942
## bmi_T3 0.004019616 0.03482512exp(cbind(OR = coef(sin2), ci_sin2))## OR 0.3 % 99.7 %
## singlepar 0.9370943 0.7198758 1.215743
## age_T3 0.9962894 0.9816586 1.011112
## sex_T3 1.0013338 0.7925005 1.267542
## bmi_T3 1.0196248 1.0040277 1.035439sin3 <- polr(foodst_03_T3 ~ singlepar + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(sin3)## Call:
## polr(formula = foodst_03_T3 ~ singlepar + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## singlepar -0.41759 0.095437 -4.376
## age_T3 0.02240 0.005294 4.231
## sex_T3 0.33450 0.083814 3.991
## bmi_T3 -0.01503 0.005538 -2.714
##
## Intercepts:
## Value Std. Error t value
## 1|2 -2.4574 0.3529 -6.9629
## 2|3 -1.1945 0.3473 -3.4397
## 3|4 -0.3848 0.3463 -1.1112
## 4|5 1.1377 0.3467 3.2813
##
## Residual Deviance: 10755.99
## AIC: 10771.99
## (16 observations deleted due to missingness)ctable_sin3 <- coef(summary(sin3))
p_sin3 <- pnorm(abs(ctable_sin3[, "t value"]), lower.tail = FALSE) * 2
(ctable_sin3 <- cbind(ctable_sin3, "p-value" = round(p_sin3, 4)))## Value Std. Error t value p-value
## singlepar -0.41759302 0.095436990 -4.375589 0.0000
## age_T3 0.02239857 0.005293530 4.231311 0.0000
## sex_T3 0.33449618 0.083813920 3.990938 0.0001
## bmi_T3 -0.01503307 0.005538485 -2.714293 0.0066
## 1|2 -2.45736059 0.352922417 -6.962892 0.0000
## 2|3 -1.19450267 0.347269379 -3.439701 0.0006
## 3|4 -0.38481534 0.346295120 -1.111235 0.2665
## 4|5 1.13767726 0.346717954 3.281276 0.0010(ci_sin3 <- confint(sin3, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## singlepar -0.678274007 -0.156084567
## age_T3 0.007947825 0.036900720
## sex_T3 0.105029020 0.563541796
## bmi_T3 -0.030160521 0.000138258exp(cbind(OR = coef(sin3), ci_sin3))## OR 0.3 % 99.7 %
## singlepar 0.6586302 0.5074922 0.8554868
## age_T3 1.0226513 1.0079795 1.0375900
## sex_T3 1.3972363 1.1107428 1.7568840
## bmi_T3 0.9850794 0.9702898 1.0001383sin4 <- polr(foodst_04_T3 ~ singlepar + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(sin4)## Call:
## polr(formula = foodst_04_T3 ~ singlepar + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## singlepar -0.487575 0.093018 -5.242
## age_T3 0.027951 0.005254 5.320
## sex_T3 0.089624 0.083212 1.077
## bmi_T3 -0.006229 0.005450 -1.143
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.8240 0.3465 -5.2646
## 2|3 -0.5121 0.3437 -1.4899
## 3|4 0.1244 0.3435 0.3621
## 4|5 1.7252 0.3447 5.0055
##
## Residual Deviance: 11905.29
## AIC: 11921.29
## (16 observations deleted due to missingness)ctable_sin4 <- coef(summary(sin4))
p_sin4 <- pnorm(abs(ctable_sin4[, "t value"]), lower.tail = FALSE) * 2
(ctable_sin4 <- cbind(ctable_sin4, "p-value" = round(p_sin4, 4)))## Value Std. Error t value p-value
## singlepar -0.487575049 0.093017978 -5.2417292 0.0000
## age_T3 0.027951474 0.005254079 5.3199574 0.0000
## sex_T3 0.089623710 0.083211919 1.0770538 0.2815
## bmi_T3 -0.006229357 0.005450207 -1.1429578 0.2531
## 1|2 -1.823981072 0.346463488 -5.2645694 0.0000
## 2|3 -0.512131926 0.343734121 -1.4899072 0.1362
## 3|4 0.124356100 0.343467537 0.3620607 0.7173
## 4|5 1.725241672 0.344670537 5.0054806 0.0000(ci_sin4 <- confint(sin4, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## singlepar -0.74195915 -0.233040158
## age_T3 0.01360091 0.042338659
## sex_T3 -0.13815079 0.317048101
## bmi_T3 -0.02112112 0.008694042exp(cbind(OR = coef(sin4), ci_sin4))## OR 0.3 % 99.7 %
## singlepar 0.6141138 0.4761801 0.7921218
## age_T3 1.0283458 1.0136938 1.0432477
## sex_T3 1.0937626 0.8709673 1.3730686
## bmi_T3 0.9937900 0.9791004 1.0087319sin5 <- polr(foodst_05_T3 ~ singlepar + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(sin5)## Call:
## polr(formula = foodst_05_T3 ~ singlepar + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## singlepar -0.385001 0.100766 -3.8208
## age_T3 0.009361 0.005739 1.6312
## sex_T3 0.167732 0.090856 1.8461
## bmi_T3 0.002301 0.005986 0.3845
##
## Intercepts:
## Value Std. Error t value
## 1|2 -3.4976 0.3874 -9.0292
## 2|3 -2.1040 0.3761 -5.5942
## 3|4 -1.6746 0.3749 -4.4672
## 4|5 -0.0381 0.3735 -0.1021
##
## Residual Deviance: 8390.313
## AIC: 8406.313
## (16 observations deleted due to missingness)ctable_sin5 <- coef(summary(sin5))
p_sin5 <- pnorm(abs(ctable_sin5[, "t value"]), lower.tail = FALSE) * 2
(ctable_sin5 <- cbind(ctable_sin5, "p-value" = round(p_sin5, 4)))## Value Std. Error t value p-value
## singlepar -0.385001348 0.100765722 -3.8207571 0.0001
## age_T3 0.009360884 0.005738814 1.6311530 0.1029
## sex_T3 0.167731988 0.090855770 1.8461347 0.0649
## bmi_T3 0.002301470 0.005986040 0.3844728 0.7006
## 1|2 -3.497614007 0.387368125 -9.0291735 0.0000
## 2|3 -2.103957371 0.376093669 -5.5942377 0.0000
## 3|4 -1.674613056 0.374872709 -4.4671512 0.0000
## 4|5 -0.038138289 0.373534001 -0.1021013 0.9187(ci_sin5 <- confint(sin5, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## singlepar -0.659031720 -0.10748491
## age_T3 -0.006304964 0.02508797
## sex_T3 -0.082438006 0.41474830
## bmi_T3 -0.013978465 0.01877863exp(cbind(OR = coef(sin5), ci_sin5))## OR 0.3 % 99.7 %
## singlepar 0.6804497 0.5173520 0.8980901
## age_T3 1.0094048 0.9937149 1.0254053
## sex_T3 1.1826196 0.9208685 1.5139896
## bmi_T3 1.0023041 0.9861188 1.0189561sin6 <- polr(foodst_06_T3 ~ singlepar + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(sin6)## Call:
## polr(formula = foodst_06_T3 ~ singlepar + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## singlepar 0.52023 0.095502 5.447
## age_T3 -0.02086 0.005399 -3.863
## sex_T3 -0.43134 0.085039 -5.072
## bmi_T3 -0.01228 0.005682 -2.162
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.7199 0.3531 -4.8715
## 2|3 0.0571 0.3520 0.1623
## 3|4 0.5095 0.3527 1.4447
## 4|5 2.1732 0.3623 5.9983
##
## Residual Deviance: 9865.621
## AIC: 9881.621
## (16 observations deleted due to missingness)ctable_sin6 <- coef(summary(sin6))
p_sin6 <- pnorm(abs(ctable_sin6[, "t value"]), lower.tail = FALSE) * 2
(ctable_sin6 <- cbind(ctable_sin6, "p-value" = round(p_sin6, 4)))## Value Std. Error t value p-value
## singlepar 0.52023192 0.095501816 5.4473511 0.0000
## age_T3 -0.02085752 0.005398722 -3.8634178 0.0001
## sex_T3 -0.43133826 0.085039065 -5.0722366 0.0000
## bmi_T3 -0.01228475 0.005682431 -2.1618829 0.0306
## 1|2 -1.71989387 0.353052031 -4.8715025 0.0000
## 2|3 0.05713057 0.352046527 0.1622813 0.8711
## 3|4 0.50952709 0.352692704 1.4446771 0.1485
## 4|5 2.17324670 0.362310958 5.9982914 0.0000(ci_sin6 <- confint(sin6, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## singlepar 0.25873381 0.781260087
## age_T3 -0.03564857 -0.006118936
## sex_T3 -0.66375936 -0.198542322
## bmi_T3 -0.02788655 0.003203038exp(cbind(OR = coef(sin6), ci_sin6))## OR 0.3 % 99.7 %
## singlepar 1.6824178 1.2952890 2.1842228
## age_T3 0.9793585 0.9649794 0.9938997
## sex_T3 0.6496391 0.5149120 0.8199251
## bmi_T3 0.9877904 0.9724987 1.0032082sin7 <- polr(foodst_07_T3 ~ singlepar + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(sin7)## Call:
## polr(formula = foodst_07_T3 ~ singlepar + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## singlepar 0.29628 0.102333 2.895
## age_T3 -0.02195 0.005860 -3.746
## sex_T3 -0.36080 0.090171 -4.001
## bmi_T3 0.01207 0.006079 1.985
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.4477 0.3783 -1.1836
## 2|3 1.2042 0.3794 3.1742
## 3|4 1.7244 0.3811 4.5249
## 4|5 3.4695 0.4023 8.6241
##
## Residual Deviance: 7943.762
## AIC: 7959.762
## (16 observations deleted due to missingness)ctable_sin7 <- coef(summary(sin7))
p_sin7 <- pnorm(abs(ctable_sin7[, "t value"]), lower.tail = FALSE) * 2
(ctable_sin7 <- cbind(ctable_sin7, "p-value" = round(p_sin7, 4)))## Value Std. Error t value p-value
## singlepar 0.29627829 0.102332699 2.895246 0.0038
## age_T3 -0.02195441 0.005860170 -3.746379 0.0002
## sex_T3 -0.36079642 0.090170850 -4.001253 0.0001
## bmi_T3 0.01206685 0.006078561 1.985149 0.0471
## 1|2 -0.44774830 0.378295974 -1.183593 0.2366
## 2|3 1.20415712 0.379353968 3.174231 0.0015
## 3|4 1.72443008 0.381094128 4.524945 0.0000
## 4|5 3.46949808 0.402303485 8.624082 0.0000(ci_sin7 <- confint(sin7, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## singlepar 0.013771664 0.573961303
## age_T3 -0.038037202 -0.005979136
## sex_T3 -0.606221017 -0.112795550
## bmi_T3 -0.004652772 0.028608500exp(cbind(OR = coef(sin7), ci_sin7))## OR 0.3 % 99.7 %
## singlepar 1.3448444 1.0138669 1.7752856
## age_T3 0.9782848 0.9626771 0.9940387
## sex_T3 0.6971209 0.5454081 0.8933333
## bmi_T3 1.0121399 0.9953580 1.0290217sin8 <- polr(foodst_08_T3 ~ singlepar + age_T3 + sex_T3 + bmi_T3, data = subset, Hess=TRUE)
summary(sin8)## Call:
## polr(formula = foodst_08_T3 ~ singlepar + age_T3 + sex_T3 + bmi_T3,
## data = subset, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## singlepar 0.1352820 0.094666 1.4290
## age_T3 0.0009608 0.005228 0.1838
## sex_T3 0.3573892 0.082800 4.3163
## bmi_T3 0.0125159 0.005417 2.3103
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.7285 0.3433 -2.1224
## 2|3 0.4258 0.3422 1.2440
## 3|4 0.7600 0.3424 2.2198
## 4|5 2.4793 0.3448 7.1906
##
## Residual Deviance: 11886.19
## AIC: 11902.19
## (16 observations deleted due to missingness)ctable_sin8 <- coef(summary(sin8))
p_sin8 <- pnorm(abs(ctable_sin8[, "t value"]), lower.tail = FALSE) * 2
(ctable_sin8 <- cbind(ctable_sin8, "p-value" = round(p_sin8, 4)))## Value Std. Error t value p-value
## singlepar 0.1352820435 0.094666217 1.4290425 0.1530
## age_T3 0.0009608112 0.005228326 0.1837703 0.8542
## sex_T3 0.3573892445 0.082799566 4.3163179 0.0000
## bmi_T3 0.0125159075 0.005417425 2.3103056 0.0209
## 1|2 -0.7285348470 0.343264343 -2.1223726 0.0338
## 2|3 0.4257571614 0.342249149 1.2439977 0.2135
## 3|4 0.7600221966 0.342375627 2.2198490 0.0264
## 4|5 2.4793138890 0.344798138 7.1906244 0.0000(ci_sin8 <- confint(sin8, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## singlepar -0.123139280 0.39478447
## age_T3 -0.013333671 0.01526368
## sex_T3 0.130907975 0.58385299
## bmi_T3 -0.002282132 0.02735336exp(cbind(OR = coef(sin8), ci_sin8))## OR 0.3 % 99.7 %
## singlepar 1.144860 0.8841405 1.484064
## age_T3 1.000961 0.9867548 1.015381
## sex_T3 1.429592 1.1398629 1.792933
## bmi_T3 1.012595 0.9977205 1.027731subset$foodst_01_T3 <- as.numeric(subset$foodst_01_T3)
subset$foodst_02_T3 <- as.numeric(subset$foodst_02_T3)
subset$foodst_03_T3 <- as.numeric(subset$foodst_03_T3)
subset$foodst_04_T3 <- as.numeric(subset$foodst_04_T3)
subset$foodst_05_T3 <- as.numeric(subset$foodst_05_T3)
subset$foodst_06_T3 <- as.numeric(subset$foodst_06_T3)
subset$foodst_07_T3 <- as.numeric(subset$foodst_07_T3)
subset$foodst_08_T3 <- as.numeric(subset$foodst_08_T3)# path b
# Consumer attitudes -> HDAS
reg1 <- lm(hds_T3 ~ foodst_01_T3 + age_T3 + sex_T3 + bmi_T3, data = subset)
summary(reg1)##
## Call:
## lm(formula = hds_T3 ~ foodst_01_T3 + age_T3 + sex_T3 + bmi_T3,
## data = subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.0222 -5.9920 0.2374 6.2447 22.7475
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.02958 1.60807 8.724 < 2e-16 ***
## foodst_01_T3 1.11221 0.10568 10.525 < 2e-16 ***
## age_T3 0.14288 0.02479 5.763 8.85e-09 ***
## sex_T3 1.34284 0.39488 3.401 0.000679 ***
## bmi_T3 -0.03071 0.02603 -1.180 0.238239
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.631 on 4046 degrees of freedom
## Multiple R-squared: 0.03663, Adjusted R-squared: 0.03568
## F-statistic: 38.46 on 4 and 4046 DF, p-value: < 2.2e-16confint(reg1, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 9.63021827 18.42894200
## foodst_01_T3 0.82310301 1.40132001
## age_T3 0.07505702 0.21070072
## sex_T3 0.26253466 2.42313972
## bmi_T3 -0.10193049 0.04051385reg2 <- lm(hds_T3 ~ foodst_02_T3 + age_T3 + sex_T3 + bmi_T3, data = subset)
summary(reg2)##
## Call:
## lm(formula = hds_T3 ~ foodst_02_T3 + age_T3 + sex_T3 + bmi_T3,
## data = subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.736 -5.992 0.287 6.322 24.872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.96400 1.59565 11.885 < 2e-16 ***
## foodst_02_T3 -0.79453 0.12438 -6.388 1.87e-10 ***
## age_T3 0.14886 0.02499 5.956 2.81e-09 ***
## sex_T3 1.36898 0.39823 3.438 0.000593 ***
## bmi_T3 -0.02636 0.02630 -1.002 0.316231
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.705 on 4046 degrees of freedom
## Multiple R-squared: 0.02014, Adjusted R-squared: 0.01917
## F-statistic: 20.79 on 4 and 4046 DF, p-value: < 2.2e-16confint(reg2, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 14.59862696 23.32937778
## foodst_02_T3 -1.13481367 -0.45423708
## age_T3 0.08047762 0.21723797
## sex_T3 0.27949995 2.45845923
## bmi_T3 -0.09831033 0.04558807reg3 <- lm(hds_T3 ~ foodst_03_T3 + age_T3 + sex_T3 + bmi_T3, data = subset)
summary(reg3)##
## Call:
## lm(formula = hds_T3 ~ foodst_03_T3 + age_T3 + sex_T3 + bmi_T3,
## data = subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.4779 -5.9759 0.2085 6.2403 23.7434
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.55950 1.63073 8.928 < 2e-16 ***
## foodst_03_T3 0.94850 0.12254 7.740 1.25e-14 ***
## age_T3 0.13704 0.02499 5.485 4.39e-08 ***
## sex_T3 1.18704 0.39806 2.982 0.00288 **
## bmi_T3 -0.02781 0.02621 -1.061 0.28874
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.685 on 4046 degrees of freedom
## Multiple R-squared: 0.0247, Adjusted R-squared: 0.02373
## F-statistic: 25.61 on 4 and 4046 DF, p-value: < 2.2e-16confint(reg3, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 10.09814199 19.02085379
## foodst_03_T3 0.61324730 1.28375533
## age_T3 0.06868901 0.20539969
## sex_T3 0.09803522 2.27604048
## bmi_T3 -0.09952690 0.04390104reg4 <- lm(hds_T3 ~ foodst_04_T3 + age_T3 + sex_T3 + bmi_T3, data = subset)
summary(reg4)##
## Call:
## lm(formula = hds_T3 ~ foodst_04_T3 + age_T3 + sex_T3 + bmi_T3,
## data = subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.2614 -6.0536 0.2757 6.3392 24.2052
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.05623 1.60696 9.369 < 2e-16 ***
## foodst_04_T3 0.93636 0.10949 8.552 < 2e-16 ***
## age_T3 0.13245 0.02497 5.304 1.2e-07 ***
## sex_T3 1.32438 0.39671 3.338 0.00085 ***
## bmi_T3 -0.03260 0.02615 -1.247 0.21259
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.671 on 4046 degrees of freedom
## Multiple R-squared: 0.02783, Adjusted R-squared: 0.02687
## F-statistic: 28.96 on 4 and 4046 DF, p-value: < 2.2e-16confint(reg4, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 10.65990003 19.45255490
## foodst_04_T3 0.63682143 1.23589051
## age_T3 0.06412922 0.20077100
## sex_T3 0.23906051 2.40969826
## bmi_T3 -0.10414239 0.03894093reg5 <- lm(hds_T3 ~ foodst_05_T3 + age_T3 + sex_T3 + bmi_T3, data = subset)
summary(reg5)##
## Call:
## lm(formula = hds_T3 ~ foodst_05_T3 + age_T3 + sex_T3 + bmi_T3,
## data = subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.6868 -6.0207 0.2928 6.3088 23.0017
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.74471 1.69398 9.295 < 2e-16 ***
## foodst_05_T3 0.47618 0.14320 3.325 0.000891 ***
## age_T3 0.14793 0.02509 5.896 4.02e-09 ***
## sex_T3 1.33642 0.39987 3.342 0.000839 ***
## bmi_T3 -0.03689 0.02635 -1.400 0.161482
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.737 on 4046 degrees of freedom
## Multiple R-squared: 0.01295, Adjusted R-squared: 0.01198
## F-statistic: 13.27 on 4 and 4046 DF, p-value: 9.562e-11confint(reg5, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 11.11032914 20.3790968
## foodst_05_T3 0.08440203 0.8679574
## age_T3 0.07929028 0.2165661
## sex_T3 0.24245880 2.4303868
## bmi_T3 -0.10896732 0.0351822reg6 <- lm(hds_T3 ~ foodst_06_T3 + age_T3 + sex_T3 + bmi_T3, data = subset)
summary(reg6)##
## Call:
## lm(formula = hds_T3 ~ foodst_06_T3 + age_T3 + sex_T3 + bmi_T3,
## data = subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.0263 -6.0410 0.2621 6.2407 22.9212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.16731 1.63042 11.756 < 2e-16 ***
## foodst_06_T3 -0.51472 0.12913 -3.986 6.84e-05 ***
## age_T3 0.14444 0.02510 5.755 9.31e-09 ***
## sex_T3 1.26643 0.40041 3.163 0.00157 **
## bmi_T3 -0.03980 0.02634 -1.511 0.13089
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.732 on 4046 degrees of freedom
## Multiple R-squared: 0.01413, Adjusted R-squared: 0.01315
## F-statistic: 14.49 on 4 and 4046 DF, p-value: 9.364e-12confint(reg6, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 14.70679143 23.62782046
## foodst_06_T3 -0.86800304 -0.16142876
## age_T3 0.07577726 0.21310977
## sex_T3 0.17100099 2.36186100
## bmi_T3 -0.11186016 0.03226471reg7 <- lm(hds_T3 ~ foodst_07_T3 + age_T3 + sex_T3 + bmi_T3, data = subset)
summary(reg7)##
## Call:
## lm(formula = hds_T3 ~ foodst_07_T3 + age_T3 + sex_T3 + bmi_T3,
## data = subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.944 -6.089 0.358 6.244 23.551
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.21057 1.61634 11.885 < 2e-16 ***
## foodst_07_T3 -0.75476 0.15403 -4.900 9.96e-07 ***
## age_T3 0.14253 0.02508 5.683 1.42e-08 ***
## sex_T3 1.27769 0.39956 3.198 0.0014 **
## bmi_T3 -0.03186 0.02632 -1.210 0.2262
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.723 on 4046 degrees of freedom
## Multiple R-squared: 0.01609, Adjusted R-squared: 0.01512
## F-statistic: 16.55 on 4 and 4046 DF, p-value: 1.867e-13confint(reg7, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 14.78858979 23.63254853
## foodst_07_T3 -1.17616335 -0.33336066
## age_T3 0.07391277 0.21115513
## sex_T3 0.18456735 2.37080608
## bmi_T3 -0.10387097 0.04015171reg8 <- lm(hds_T3 ~ foodst_08_T3 + age_T3 + sex_T3 + bmi_T3, data = subset)
summary(reg8)##
## Call:
## lm(formula = hds_T3 ~ foodst_08_T3 + age_T3 + sex_T3 + bmi_T3,
## data = subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.2375 -5.9759 0.2956 6.3236 22.4188
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.51693 1.61022 10.258 < 2e-16 ***
## foodst_08_T3 0.44553 0.10188 4.373 1.26e-05 ***
## age_T3 0.14924 0.02506 5.955 2.82e-09 ***
## sex_T3 1.25626 0.40024 3.139 0.00171 **
## bmi_T3 -0.04057 0.02633 -1.541 0.12345
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.728 on 4046 degrees of freedom
## Multiple R-squared: 0.01491, Adjusted R-squared: 0.01394
## F-statistic: 15.31 on 4 and 4046 DF, p-value: 1.97e-12confint(reg8, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 12.11168830 20.92217876
## foodst_08_T3 0.16680153 0.72426191
## age_T3 0.08067631 0.21780061
## sex_T3 0.16128910 2.35123427
## bmi_T3 -0.11261723 0.03146905# Education -> HDAS
edu_total <- lm(hds_T3 ~ isced_cat2011_T3 + age_T3 + sex_T3 + bmi_T3, data = subset)
summary(edu_total)##
## Call:
## lm(formula = hds_T3 ~ isced_cat2011_T3 + age_T3 + sex_T3 + bmi_T3,
## data = subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.2573 -5.9538 0.2701 6.1794 23.2303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.75044 1.74216 5.597 2.33e-08 ***
## isced_cat2011_T3 2.83593 0.24122 11.757 < 2e-16 ***
## age_T3 0.11714 0.02528 4.634 3.70e-06 ***
## sex_T3 1.71025 0.40340 4.240 2.29e-05 ***
## bmi_T3 0.02774 0.02678 1.036 0.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.595 on 3926 degrees of freedom
## (120 observations deleted due to missingness)
## Multiple R-squared: 0.04332, Adjusted R-squared: 0.04234
## F-statistic: 44.44 on 4 and 3926 DF, p-value: < 2.2e-16confint(edu_total, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 4.98415372 14.5167271
## isced_cat2011_T3 2.17598537 3.4958782
## age_T3 0.04798404 0.1862944
## sex_T3 0.60660351 2.8139041
## bmi_T3 -0.04552748 0.1010094# Income -> HDAS
inc_total <- lm(hds_T3 ~ income_cat_T3 + age_T3 + sex_T3 + bmi_T3, data = subset)
summary(inc_total)##
## Call:
## lm(formula = hds_T3 ~ income_cat_T3 + age_T3 + sex_T3 + bmi_T3,
## data = subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.3508 -5.9875 0.2266 6.1946 24.3266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.218987 1.674442 7.895 3.78e-15 ***
## income_cat_T3 0.998361 0.101950 9.793 < 2e-16 ***
## age_T3 0.141672 0.025556 5.544 3.17e-08 ***
## sex_T3 1.794955 0.404294 4.440 9.26e-06 ***
## bmi_T3 0.004667 0.027293 0.171 0.864
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.642 on 3820 degrees of freedom
## (226 observations deleted due to missingness)
## Multiple R-squared: 0.03526, Adjusted R-squared: 0.03425
## F-statistic: 34.91 on 4 and 3820 DF, p-value: < 2.2e-16confint(inc_total, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 8.63790504 17.80006897
## income_cat_T3 0.71943808 1.27728480
## age_T3 0.07175274 0.21159123
## sex_T3 0.68885257 2.90105827
## bmi_T3 -0.07000288 0.07933741# Migration background -> HDAS
mig_total <- lm(hds_T3 ~ migration + age_T3 + sex_T3 + bmi_T3, data = subset)
summary(mig_total)##
## Call:
## lm(formula = hds_T3 ~ migration + age_T3 + sex_T3 + bmi_T3, data = subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.6288 -5.9917 0.2796 6.2904 22.9987
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.29861 1.65292 11.070 < 2e-16 ***
## migration -0.55498 0.41340 -1.342 0.17952
## age_T3 0.14946 0.02512 5.951 2.9e-09 ***
## sex_T3 1.36421 0.40025 3.408 0.00066 ***
## bmi_T3 -0.03541 0.02639 -1.342 0.17976
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.747 on 4046 degrees of freedom
## Multiple R-squared: 0.0107, Adjusted R-squared: 0.009718
## F-statistic: 10.94 on 4 and 4046 DF, p-value: 8.076e-09confint(mig_total, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 13.77655692 22.82066973
## migration -1.68595976 0.57599835
## age_T3 0.08074509 0.21816724
## sex_T3 0.26919942 2.45921517
## bmi_T3 -0.10761993 0.03679476# Unemployment in Household -> HDAS
une_total <- lm(hds_T3 ~ unemploy + age_T3 + sex_T3 + bmi_T3, data = subset)
summary(une_total)##
## Call:
## lm(formula = hds_T3 ~ unemploy + age_T3 + sex_T3 + bmi_T3, data = subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.6787 -5.9764 0.2377 6.2548 22.5006
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.58349 1.65523 11.831 < 2e-16 ***
## unemploy -1.96346 0.48576 -4.042 5.4e-05 ***
## age_T3 0.14765 0.02507 5.889 4.2e-09 ***
## sex_T3 1.41920 0.39956 3.552 0.000387 ***
## bmi_T3 -0.02774 0.02642 -1.050 0.293901
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.731 on 4046 degrees of freedom
## Multiple R-squared: 0.01424, Adjusted R-squared: 0.01326
## F-statistic: 14.61 on 4 and 4046 DF, p-value: 7.537e-12confint(une_total, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 15.05510962 24.11187149
## unemploy -3.29241270 -0.63451523
## age_T3 0.07905807 0.21624483
## sex_T3 0.32606907 2.51232562
## bmi_T3 -0.10002093 0.04454884# Single parenthood -> HDAS
sin_total <- lm(hds_T3 ~ singlepar + age_T3 + sex_T3 + bmi_T3, data = subset)
summary(sin_total)##
## Call:
## lm(formula = hds_T3 ~ singlepar + age_T3 + sex_T3 + bmi_T3, data = subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.5548 -6.0104 0.2556 6.3154 22.6570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.89985 1.65218 10.834 < 2e-16 ***
## singlepar -0.19402 0.45571 -0.426 0.670303
## age_T3 0.14869 0.02512 5.919 3.51e-09 ***
## sex_T3 1.38268 0.40136 3.445 0.000577 ***
## bmi_T3 -0.03619 0.02640 -1.371 0.170541
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.739 on 4030 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.01022, Adjusted R-squared: 0.009238
## F-statistic: 10.4 on 4 and 4030 DF, p-value: 2.212e-08confint(sin_total, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 13.37979980 22.41990204
## singlepar -1.44075322 1.05270542
## age_T3 0.07996228 0.21741905
## sex_T3 0.28463933 2.48072971
## bmi_T3 -0.10841117 0.03603819unloadNamespace("MASS")Controlled for age, sex, BMI
SEM_model_edu1 <- '
level: 1
hds_T3 ~ isced_cat2011_T3 + a1*foodst_01_T3 + a2*foodst_02_T3 + a3*foodst_03_T3 + a4*foodst_04_T3 + a5*foodst_05_T3 + a6*foodst_06_T3 + a7*foodst_07_T3 + a8*foodst_08_T3 + age_T3 + sex_T3 + bmi_T3
foodst_01_T3 ~ b1*isced_cat2011_T3
foodst_02_T3 ~ b2*isced_cat2011_T3
foodst_03_T3 ~ b3*isced_cat2011_T3
foodst_04_T3 ~ b4*isced_cat2011_T3
foodst_05_T3 ~ b5*isced_cat2011_T3
foodst_06_T3 ~ b6*isced_cat2011_T3
foodst_07_T3 ~ b7*isced_cat2011_T3
foodst_08_T3 ~ b8*isced_cat2011_T3
foodst_01_T3 ~~ foodst_02_T3 + foodst_03_T3 + foodst_04_T3 + foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_02_T3 ~~ foodst_03_T3 + foodst_04_T3 + foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_03_T3 ~~ foodst_04_T3 + foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_04_T3 ~~ foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_05_T3 ~~ foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_06_T3 ~~ foodst_07_T3 + foodst_08_T3
foodst_07_T3 ~~ foodst_08_T3
ebindfoodst_01_T3 := a1*b1
ebindfoodst_02_T3 := a2*b2
ebindfoodst_03_T3 := a3*b3
ebindfoodst_04_T3 := a4*b4
ebindfoodst_05_T3 := a5*b5
ebindfoodst_06_T3 := a6*b6
ebindfoodst_07_T3 := a7*b7
ebindfoodst_08_T3 := a8*b8
level: 2
hds_T3 ~ 1
'
fit_SEM_edu1 <- sem(model = SEM_model_edu1, data = subset, cluster = "country")
summary(fit_SEM_edu1)## lavaan 0.6.16 ended normally after 126 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 67
##
## Used Total
## Number of observations 3931 4051
## Number of clusters [country] 8
##
## Model Test User Model:
##
## Test statistic 55.391
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
##
## Level 1 [within]:
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## hds_T3 ~
## i_2011_T3 1.547 0.232 6.678 0.000
## fds_01_T3 (a1) 0.775 0.113 6.856 0.000
## fds_02_T3 (a2) -0.561 0.121 -4.627 0.000
## fds_03_T3 (a3) 0.305 0.146 2.084 0.037
## fds_04_T3 (a4) 0.600 0.131 4.563 0.000
## fds_05_T3 (a5) -0.103 0.152 -0.679 0.497
## fds_06_T3 (a6) -0.640 0.138 -4.651 0.000
## fds_07_T3 (a7) -0.018 0.162 -0.113 0.910
## fds_08_T3 (a8) 0.341 0.098 3.495 0.000
## age_T3 0.092 0.024 3.846 0.000
## sex_T3 1.641 0.383 4.290 0.000
## bmi_T3 0.075 0.025 2.963 0.003
## foodst_01_T3 ~
## i_2011_T3 (b1) 0.079 0.035 2.253 0.024
## foodst_02_T3 ~
## i_2011_T3 (b2) -0.258 0.030 -8.739 0.000
## foodst_03_T3 ~
## i_2011_T3 (b3) 0.089 0.030 2.916 0.004
## foodst_04_T3 ~
## i_2011_T3 (b4) 0.081 0.034 2.395 0.017
## foodst_05_T3 ~
## i_2011_T3 (b5) -0.025 0.026 -0.939 0.348
## foodst_06_T3 ~
## i_2011_T3 (b6) 0.190 0.029 6.570 0.000
## foodst_07_T3 ~
## i_2011_T3 (b7) -0.002 0.024 -0.064 0.949
## foodst_08_T3 ~
## i_2011_T3 (b8) -0.027 0.037 -0.739 0.460
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .foodst_01_T3 ~~
## .foodst_02_T3 0.183 0.022 8.217 0.000
## .foodst_03_T3 0.579 0.025 23.532 0.000
## .foodst_04_T3 0.565 0.027 20.918 0.000
## .foodst_05_T3 0.196 0.020 9.854 0.000
## .foodst_06_T3 -0.108 0.022 -4.959 0.000
## .foodst_07_T3 -0.092 0.018 -5.037 0.000
## .foodst_08_T3 0.159 0.028 5.741 0.000
## .foodst_02_T3 ~~
## .foodst_03_T3 0.067 0.019 3.487 0.000
## .foodst_04_T3 0.106 0.022 4.919 0.000
## .foodst_05_T3 0.044 0.017 2.617 0.009
## .foodst_06_T3 0.070 0.018 3.811 0.000
## .foodst_07_T3 0.062 0.015 4.029 0.000
## .foodst_08_T3 -0.037 0.023 -1.597 0.110
## .foodst_03_T3 ~~
## .foodst_04_T3 0.753 0.025 29.849 0.000
## .foodst_05_T3 0.361 0.018 19.979 0.000
## .foodst_06_T3 -0.239 0.019 -12.420 0.000
## .foodst_07_T3 -0.191 0.016 -11.819 0.000
## .foodst_08_T3 0.175 0.024 7.244 0.000
## .foodst_04_T3 ~~
## .foodst_05_T3 0.490 0.021 23.720 0.000
## .foodst_06_T3 -0.238 0.021 -11.137 0.000
## .foodst_07_T3 -0.193 0.018 -10.732 0.000
## .foodst_08_T3 0.209 0.027 7.737 0.000
## .foodst_05_T3 ~~
## .foodst_06_T3 -0.231 0.017 -13.877 0.000
## .foodst_07_T3 -0.174 0.014 -12.449 0.000
## .foodst_08_T3 0.164 0.021 7.877 0.000
## .foodst_06_T3 ~~
## .foodst_07_T3 0.400 0.016 24.472 0.000
## .foodst_08_T3 -0.055 0.023 -2.414 0.016
## .foodst_07_T3 ~~
## .foodst_08_T3 -0.033 0.019 -1.735 0.083
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 0.000
## .foodst_01_T3 3.251 0.089 36.549 0.000
## .foodst_02_T3 2.538 0.075 33.732 0.000
## .foodst_03_T3 3.745 0.078 48.317 0.000
## .foodst_04_T3 3.343 0.087 38.621 0.000
## .foodst_05_T3 4.427 0.067 66.221 0.000
## .foodst_06_T3 1.452 0.073 19.756 0.000
## .foodst_07_T3 1.570 0.062 25.353 0.000
## .foodst_08_T3 3.424 0.094 36.574 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 65.685 1.483 44.289 0.000
## .foodst_01_T3 1.641 0.037 44.334 0.000
## .foodst_02_T3 1.174 0.026 44.334 0.000
## .foodst_03_T3 1.246 0.028 44.334 0.000
## .foodst_04_T3 1.554 0.035 44.334 0.000
## .foodst_05_T3 0.927 0.021 44.334 0.000
## .foodst_06_T3 1.120 0.025 44.334 0.000
## .foodst_07_T3 0.796 0.018 44.334 0.000
## .foodst_08_T3 1.818 0.041 44.334 0.000
##
##
## Level 2 [country]:
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 9.042 2.046 4.419 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 6.064 3.111 1.949 0.051
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ebndfdst_01_T3 0.061 0.029 2.141 0.032
## ebndfdst_02_T3 0.145 0.035 4.089 0.000
## ebndfdst_03_T3 0.027 0.016 1.695 0.090
## ebndfdst_04_T3 0.049 0.023 2.121 0.034
## ebndfdst_05_T3 0.003 0.005 0.550 0.582
## ebndfdst_06_T3 -0.122 0.032 -3.796 0.000
## ebndfdst_07_T3 0.000 0.001 0.056 0.956
## ebndfdst_08_T3 -0.009 0.013 -0.723 0.470parameterEstimates(fit_SEM_edu1, ci=TRUE, level=.99375)## lhs op rhs block level label est
## 1 hds_T3 ~ isced_cat2011_T3 1 1 1.547
## 2 hds_T3 ~ foodst_01_T3 1 1 a1 0.775
## 3 hds_T3 ~ foodst_02_T3 1 1 a2 -0.561
## 4 hds_T3 ~ foodst_03_T3 1 1 a3 0.305
## 5 hds_T3 ~ foodst_04_T3 1 1 a4 0.600
## 6 hds_T3 ~ foodst_05_T3 1 1 a5 -0.103
## 7 hds_T3 ~ foodst_06_T3 1 1 a6 -0.640
## 8 hds_T3 ~ foodst_07_T3 1 1 a7 -0.018
## 9 hds_T3 ~ foodst_08_T3 1 1 a8 0.341
## 10 hds_T3 ~ age_T3 1 1 0.092
## 11 hds_T3 ~ sex_T3 1 1 1.641
## 12 hds_T3 ~ bmi_T3 1 1 0.075
## 13 foodst_01_T3 ~ isced_cat2011_T3 1 1 b1 0.079
## 14 foodst_02_T3 ~ isced_cat2011_T3 1 1 b2 -0.258
## 15 foodst_03_T3 ~ isced_cat2011_T3 1 1 b3 0.089
## 16 foodst_04_T3 ~ isced_cat2011_T3 1 1 b4 0.081
## 17 foodst_05_T3 ~ isced_cat2011_T3 1 1 b5 -0.025
## 18 foodst_06_T3 ~ isced_cat2011_T3 1 1 b6 0.190
## 19 foodst_07_T3 ~ isced_cat2011_T3 1 1 b7 -0.002
## 20 foodst_08_T3 ~ isced_cat2011_T3 1 1 b8 -0.027
## 21 foodst_01_T3 ~~ foodst_02_T3 1 1 0.183
## 22 foodst_01_T3 ~~ foodst_03_T3 1 1 0.579
## 23 foodst_01_T3 ~~ foodst_04_T3 1 1 0.565
## 24 foodst_01_T3 ~~ foodst_05_T3 1 1 0.196
## 25 foodst_01_T3 ~~ foodst_06_T3 1 1 -0.108
## 26 foodst_01_T3 ~~ foodst_07_T3 1 1 -0.092
## 27 foodst_01_T3 ~~ foodst_08_T3 1 1 0.159
## 28 foodst_02_T3 ~~ foodst_03_T3 1 1 0.067
## 29 foodst_02_T3 ~~ foodst_04_T3 1 1 0.106
## 30 foodst_02_T3 ~~ foodst_05_T3 1 1 0.044
## 31 foodst_02_T3 ~~ foodst_06_T3 1 1 0.070
## 32 foodst_02_T3 ~~ foodst_07_T3 1 1 0.062
## 33 foodst_02_T3 ~~ foodst_08_T3 1 1 -0.037
## 34 foodst_03_T3 ~~ foodst_04_T3 1 1 0.753
## 35 foodst_03_T3 ~~ foodst_05_T3 1 1 0.361
## 36 foodst_03_T3 ~~ foodst_06_T3 1 1 -0.239
## 37 foodst_03_T3 ~~ foodst_07_T3 1 1 -0.191
## 38 foodst_03_T3 ~~ foodst_08_T3 1 1 0.175
## 39 foodst_04_T3 ~~ foodst_05_T3 1 1 0.490
## 40 foodst_04_T3 ~~ foodst_06_T3 1 1 -0.238
## 41 foodst_04_T3 ~~ foodst_07_T3 1 1 -0.193
## 42 foodst_04_T3 ~~ foodst_08_T3 1 1 0.209
## 43 foodst_05_T3 ~~ foodst_06_T3 1 1 -0.231
## 44 foodst_05_T3 ~~ foodst_07_T3 1 1 -0.174
## 45 foodst_05_T3 ~~ foodst_08_T3 1 1 0.164
## 46 foodst_06_T3 ~~ foodst_07_T3 1 1 0.400
## 47 foodst_06_T3 ~~ foodst_08_T3 1 1 -0.055
## 48 foodst_07_T3 ~~ foodst_08_T3 1 1 -0.033
## 49 hds_T3 ~~ hds_T3 1 1 65.685
## 50 foodst_01_T3 ~~ foodst_01_T3 1 1 1.641
## 51 foodst_02_T3 ~~ foodst_02_T3 1 1 1.174
## 52 foodst_03_T3 ~~ foodst_03_T3 1 1 1.246
## 53 foodst_04_T3 ~~ foodst_04_T3 1 1 1.554
## 54 foodst_05_T3 ~~ foodst_05_T3 1 1 0.927
## 55 foodst_06_T3 ~~ foodst_06_T3 1 1 1.120
## 56 foodst_07_T3 ~~ foodst_07_T3 1 1 0.796
## 57 foodst_08_T3 ~~ foodst_08_T3 1 1 1.818
## 58 isced_cat2011_T3 ~~ isced_cat2011_T3 1 1 0.342
## 59 isced_cat2011_T3 ~~ age_T3 1 1 0.306
## 60 isced_cat2011_T3 ~~ sex_T3 1 1 -0.015
## 61 isced_cat2011_T3 ~~ bmi_T3 1 1 -0.590
## 62 age_T3 ~~ age_T3 1 1 31.331
## 63 age_T3 ~~ sex_T3 1 1 -0.444
## 64 age_T3 ~~ bmi_T3 1 1 2.395
## 65 sex_T3 ~~ sex_T3 1 1 0.124
## 66 sex_T3 ~~ bmi_T3 1 1 -0.249
## 67 bmi_T3 ~~ bmi_T3 1 1 27.964
## 68 hds_T3 ~1 1 1 0.000
## 69 foodst_01_T3 ~1 1 1 3.251
## 70 foodst_02_T3 ~1 1 1 2.538
## 71 foodst_03_T3 ~1 1 1 3.745
## 72 foodst_04_T3 ~1 1 1 3.343
## 73 foodst_05_T3 ~1 1 1 4.427
## 74 foodst_06_T3 ~1 1 1 1.452
## 75 foodst_07_T3 ~1 1 1 1.570
## 76 foodst_08_T3 ~1 1 1 3.424
## 77 isced_cat2011_T3 ~1 1 1 2.476
## 78 age_T3 ~1 1 1 41.900
## 79 sex_T3 ~1 1 1 1.855
## 80 bmi_T3 ~1 1 1 26.051
## 81 hds_T3 ~1 2 2 9.042
## 82 hds_T3 ~~ hds_T3 2 2 6.064
## 83 ebindfoodst_01_T3 := a1*b1 0 0 ebindfoodst_01_T3 0.061
## 84 ebindfoodst_02_T3 := a2*b2 0 0 ebindfoodst_02_T3 0.145
## 85 ebindfoodst_03_T3 := a3*b3 0 0 ebindfoodst_03_T3 0.027
## 86 ebindfoodst_04_T3 := a4*b4 0 0 ebindfoodst_04_T3 0.049
## 87 ebindfoodst_05_T3 := a5*b5 0 0 ebindfoodst_05_T3 0.003
## 88 ebindfoodst_06_T3 := a6*b6 0 0 ebindfoodst_06_T3 -0.122
## 89 ebindfoodst_07_T3 := a7*b7 0 0 ebindfoodst_07_T3 0.000
## 90 ebindfoodst_08_T3 := a8*b8 0 0 ebindfoodst_08_T3 -0.009
## se z pvalue ci.lower ci.upper
## 1 0.232 6.678 0.000 0.913 2.180
## 2 0.113 6.856 0.000 0.466 1.084
## 3 0.121 -4.627 0.000 -0.893 -0.229
## 4 0.146 2.084 0.037 -0.095 0.704
## 5 0.131 4.563 0.000 0.240 0.959
## 6 0.152 -0.679 0.497 -0.518 0.312
## 7 0.138 -4.651 0.000 -1.017 -0.264
## 8 0.162 -0.113 0.910 -0.463 0.426
## 9 0.098 3.495 0.000 0.074 0.607
## 10 0.024 3.846 0.000 0.027 0.158
## 11 0.383 4.290 0.000 0.595 2.687
## 12 0.025 2.963 0.003 0.006 0.144
## 13 0.035 2.253 0.024 -0.017 0.174
## 14 0.030 -8.739 0.000 -0.339 -0.178
## 15 0.030 2.916 0.004 0.006 0.172
## 16 0.034 2.395 0.017 -0.012 0.175
## 17 0.026 -0.939 0.348 -0.097 0.047
## 18 0.029 6.570 0.000 0.111 0.269
## 19 0.024 -0.064 0.949 -0.068 0.065
## 20 0.037 -0.739 0.460 -0.128 0.073
## 21 0.022 8.217 0.000 0.122 0.244
## 22 0.025 23.532 0.000 0.512 0.646
## 23 0.027 20.918 0.000 0.491 0.639
## 24 0.020 9.854 0.000 0.142 0.251
## 25 0.022 -4.959 0.000 -0.167 -0.048
## 26 0.018 -5.037 0.000 -0.142 -0.042
## 27 0.028 5.741 0.000 0.083 0.234
## 28 0.019 3.487 0.000 0.015 0.120
## 29 0.022 4.919 0.000 0.047 0.165
## 30 0.017 2.617 0.009 -0.002 0.089
## 31 0.018 3.811 0.000 0.020 0.120
## 32 0.015 4.029 0.000 0.020 0.104
## 33 0.023 -1.597 0.110 -0.101 0.027
## 34 0.025 29.849 0.000 0.684 0.822
## 35 0.018 19.979 0.000 0.312 0.411
## 36 0.019 -12.420 0.000 -0.291 -0.186
## 37 0.016 -11.819 0.000 -0.235 -0.147
## 38 0.024 7.244 0.000 0.109 0.241
## 39 0.021 23.720 0.000 0.434 0.547
## 40 0.021 -11.137 0.000 -0.297 -0.180
## 41 0.018 -10.732 0.000 -0.242 -0.144
## 42 0.027 7.737 0.000 0.135 0.283
## 43 0.017 -13.877 0.000 -0.277 -0.186
## 44 0.014 -12.449 0.000 -0.212 -0.136
## 45 0.021 7.877 0.000 0.107 0.221
## 46 0.016 24.472 0.000 0.355 0.445
## 47 0.023 -2.414 0.016 -0.117 0.007
## 48 0.019 -1.735 0.083 -0.086 0.019
## 49 1.483 44.289 0.000 61.629 69.740
## 50 0.037 44.334 0.000 1.539 1.742
## 51 0.026 44.334 0.000 1.101 1.246
## 52 0.028 44.334 0.000 1.169 1.323
## 53 0.035 44.334 0.000 1.458 1.650
## 54 0.021 44.334 0.000 0.870 0.984
## 55 0.025 44.334 0.000 1.051 1.189
## 56 0.018 44.334 0.000 0.746 0.845
## 57 0.041 44.334 0.000 1.706 1.930
## 58 0.000 NA NA 0.342 0.342
## 59 0.000 NA NA 0.306 0.306
## 60 0.000 NA NA -0.015 -0.015
## 61 0.000 NA NA -0.590 -0.590
## 62 0.000 NA NA 31.331 31.331
## 63 0.000 NA NA -0.444 -0.444
## 64 0.000 NA NA 2.395 2.395
## 65 0.000 NA NA 0.124 0.124
## 66 0.000 NA NA -0.249 -0.249
## 67 0.000 NA NA 27.964 27.964
## 68 0.000 NA NA 0.000 0.000
## 69 0.089 36.549 0.000 3.008 3.494
## 70 0.075 33.732 0.000 2.332 2.744
## 71 0.078 48.317 0.000 3.533 3.957
## 72 0.087 38.621 0.000 3.106 3.580
## 73 0.067 66.221 0.000 4.245 4.610
## 74 0.073 19.756 0.000 1.251 1.653
## 75 0.062 25.353 0.000 1.401 1.740
## 76 0.094 36.574 0.000 3.168 3.680
## 77 0.000 NA NA 2.476 2.476
## 78 0.000 NA NA 41.900 41.900
## 79 0.000 NA NA 1.855 1.855
## 80 0.000 NA NA 26.051 26.051
## 81 2.046 4.419 0.000 3.447 14.637
## 82 3.111 1.949 0.051 -2.443 14.570
## 83 0.029 2.141 0.032 -0.017 0.139
## 84 0.035 4.089 0.000 0.048 0.242
## 85 0.016 1.695 0.090 -0.017 0.071
## 86 0.023 2.121 0.034 -0.014 0.112
## 87 0.005 0.550 0.582 -0.010 0.015
## 88 0.032 -3.796 0.000 -0.209 -0.034
## 89 0.001 0.056 0.956 -0.001 0.001
## 90 0.013 -0.723 0.470 -0.044 0.026Controlled for age, sex, BMI
SEM_model_inc1 <- '
level: 1
hds_T3 ~ income_cat_T3 + a1*foodst_01_T3 + a2*foodst_02_T3 + a3*foodst_03_T3 + a4*foodst_04_T3 + a5*foodst_05_T3 + a6*foodst_06_T3 + a7*foodst_07_T3 + a8*foodst_08_T3 + age_T3 + sex_T3 + bmi_T3
foodst_01_T3 ~ b1*income_cat_T3
foodst_02_T3 ~ b2*income_cat_T3
foodst_03_T3 ~ b3*income_cat_T3
foodst_04_T3 ~ b4*income_cat_T3
foodst_05_T3 ~ b5*income_cat_T3
foodst_06_T3 ~ b6*income_cat_T3
foodst_07_T3 ~ b7*income_cat_T3
foodst_08_T3 ~ b8*income_cat_T3
foodst_01_T3 ~~ foodst_02_T3 + foodst_03_T3 + foodst_04_T3 + foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_02_T3 ~~ foodst_03_T3 + foodst_04_T3 + foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_03_T3 ~~ foodst_04_T3 + foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_04_T3 ~~ foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_05_T3 ~~ foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_06_T3 ~~ foodst_07_T3 + foodst_08_T3
foodst_07_T3 ~~ foodst_08_T3
ebindfoodst_01_T3 := a1*b1
ebindfoodst_02_T3 := a2*b2
ebindfoodst_03_T3 := a3*b3
ebindfoodst_04_T3 := a4*b4
ebindfoodst_05_T3 := a5*b5
ebindfoodst_06_T3 := a6*b6
ebindfoodst_07_T3 := a7*b7
ebindfoodst_08_T3 := a8*b8
level: 2
hds_T3 ~ 1
'
fit_SEM_inc1 <- sem(model = SEM_model_inc1, data = subset, cluster = "country")
summary(fit_SEM_inc1)## lavaan 0.6.16 ended normally after 99 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 67
##
## Used Total
## Number of observations 3825 4051
## Number of clusters [country] 8
##
## Model Test User Model:
##
## Test statistic 62.544
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
##
## Level 1 [within]:
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## hds_T3 ~
## incm_c_T3 0.543 0.097 5.586 0.000
## fds_01_T3 (a1) 0.816 0.115 7.113 0.000
## fds_02_T3 (a2) -0.532 0.123 -4.322 0.000
## fds_03_T3 (a3) 0.290 0.149 1.941 0.052
## fds_04_T3 (a4) 0.678 0.134 5.045 0.000
## fds_05_T3 (a5) -0.065 0.153 -0.426 0.670
## fds_06_T3 (a6) -0.551 0.138 -3.987 0.000
## fds_07_T3 (a7) -0.055 0.163 -0.338 0.735
## fds_08_T3 (a8) 0.343 0.099 3.443 0.001
## age_T3 0.104 0.024 4.308 0.000
## sex_T3 1.709 0.382 4.471 0.000
## bmi_T3 0.064 0.026 2.487 0.013
## foodst_01_T3 ~
## incm_c_T3 (b1) 0.002 0.015 0.127 0.899
## foodst_02_T3 ~
## incm_c_T3 (b2) -0.105 0.013 -8.316 0.000
## foodst_03_T3 ~
## incm_c_T3 (b3) -0.003 0.013 -0.267 0.789
## foodst_04_T3 ~
## incm_c_T3 (b4) -0.039 0.014 -2.665 0.008
## foodst_05_T3 ~
## incm_c_T3 (b5) -0.026 0.011 -2.351 0.019
## foodst_06_T3 ~
## incm_c_T3 (b6) 0.068 0.012 5.521 0.000
## foodst_07_T3 ~
## incm_c_T3 (b7) 0.011 0.010 1.040 0.298
## foodst_08_T3 ~
## incm_c_T3 (b8) -0.034 0.016 -2.170 0.030
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .foodst_01_T3 ~~
## .foodst_02_T3 0.188 0.023 8.285 0.000
## .foodst_03_T3 0.568 0.025 22.878 0.000
## .foodst_04_T3 0.569 0.027 20.787 0.000
## .foodst_05_T3 0.188 0.020 9.317 0.000
## .foodst_06_T3 -0.099 0.022 -4.497 0.000
## .foodst_07_T3 -0.085 0.019 -4.586 0.000
## .foodst_08_T3 0.141 0.028 5.054 0.000
## .foodst_02_T3 ~~
## .foodst_03_T3 0.068 0.020 3.482 0.000
## .foodst_04_T3 0.090 0.022 4.091 0.000
## .foodst_05_T3 0.041 0.017 2.410 0.016
## .foodst_06_T3 0.074 0.019 3.938 0.000
## .foodst_07_T3 0.064 0.016 4.088 0.000
## .foodst_08_T3 -0.037 0.024 -1.554 0.120
## .foodst_03_T3 ~~
## .foodst_04_T3 0.766 0.026 29.860 0.000
## .foodst_05_T3 0.361 0.018 19.660 0.000
## .foodst_06_T3 -0.238 0.020 -12.168 0.000
## .foodst_07_T3 -0.189 0.016 -11.503 0.000
## .foodst_08_T3 0.174 0.024 7.142 0.000
## .foodst_04_T3 ~~
## .foodst_05_T3 0.484 0.021 23.086 0.000
## .foodst_06_T3 -0.227 0.022 -10.459 0.000
## .foodst_07_T3 -0.190 0.018 -10.377 0.000
## .foodst_08_T3 0.199 0.027 7.305 0.000
## .foodst_05_T3 ~~
## .foodst_06_T3 -0.229 0.017 -13.471 0.000
## .foodst_07_T3 -0.172 0.014 -12.078 0.000
## .foodst_08_T3 0.164 0.021 7.796 0.000
## .foodst_06_T3 ~~
## .foodst_07_T3 0.389 0.017 23.426 0.000
## .foodst_08_T3 -0.067 0.023 -2.886 0.004
## .foodst_07_T3 ~~
## .foodst_08_T3 -0.035 0.019 -1.789 0.074
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 0.000
## .foodst_01_T3 3.437 0.050 68.690 0.000
## .foodst_02_T3 2.208 0.042 52.083 0.000
## .foodst_03_T3 3.979 0.044 91.256 0.000
## .foodst_04_T3 3.665 0.049 75.202 0.000
## .foodst_05_T3 4.446 0.038 117.889 0.000
## .foodst_06_T3 1.727 0.042 41.542 0.000
## .foodst_07_T3 1.534 0.035 43.834 0.000
## .foodst_08_T3 3.471 0.052 66.166 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 66.003 1.511 43.687 0.000
## .foodst_01_T3 1.638 0.037 43.732 0.000
## .foodst_02_T3 1.176 0.027 43.732 0.000
## .foodst_03_T3 1.243 0.028 43.732 0.000
## .foodst_04_T3 1.553 0.036 43.732 0.000
## .foodst_05_T3 0.930 0.021 43.732 0.000
## .foodst_06_T3 1.130 0.026 43.732 0.000
## .foodst_07_T3 0.801 0.018 43.732 0.000
## .foodst_08_T3 1.800 0.041 43.732 0.000
##
##
## Level 2 [country]:
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 10.195 2.004 5.088 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 5.915 3.038 1.947 0.052
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ebndfdst_01_T3 0.002 0.012 0.127 0.899
## ebndfdst_02_T3 0.056 0.015 3.835 0.000
## ebndfdst_03_T3 -0.001 0.004 -0.265 0.791
## ebndfdst_04_T3 -0.026 0.011 -2.357 0.018
## ebndfdst_05_T3 0.002 0.004 0.419 0.675
## ebndfdst_06_T3 -0.038 0.012 -3.232 0.001
## ebndfdst_07_T3 -0.001 0.002 -0.322 0.748
## ebndfdst_08_T3 -0.012 0.006 -1.836 0.066parameterEstimates(fit_SEM_inc1, ci=TRUE, level=.99375)## lhs op rhs block level label est
## 1 hds_T3 ~ income_cat_T3 1 1 0.543
## 2 hds_T3 ~ foodst_01_T3 1 1 a1 0.816
## 3 hds_T3 ~ foodst_02_T3 1 1 a2 -0.532
## 4 hds_T3 ~ foodst_03_T3 1 1 a3 0.290
## 5 hds_T3 ~ foodst_04_T3 1 1 a4 0.678
## 6 hds_T3 ~ foodst_05_T3 1 1 a5 -0.065
## 7 hds_T3 ~ foodst_06_T3 1 1 a6 -0.551
## 8 hds_T3 ~ foodst_07_T3 1 1 a7 -0.055
## 9 hds_T3 ~ foodst_08_T3 1 1 a8 0.343
## 10 hds_T3 ~ age_T3 1 1 0.104
## 11 hds_T3 ~ sex_T3 1 1 1.709
## 12 hds_T3 ~ bmi_T3 1 1 0.064
## 13 foodst_01_T3 ~ income_cat_T3 1 1 b1 0.002
## 14 foodst_02_T3 ~ income_cat_T3 1 1 b2 -0.105
## 15 foodst_03_T3 ~ income_cat_T3 1 1 b3 -0.003
## 16 foodst_04_T3 ~ income_cat_T3 1 1 b4 -0.039
## 17 foodst_05_T3 ~ income_cat_T3 1 1 b5 -0.026
## 18 foodst_06_T3 ~ income_cat_T3 1 1 b6 0.068
## 19 foodst_07_T3 ~ income_cat_T3 1 1 b7 0.011
## 20 foodst_08_T3 ~ income_cat_T3 1 1 b8 -0.034
## 21 foodst_01_T3 ~~ foodst_02_T3 1 1 0.188
## 22 foodst_01_T3 ~~ foodst_03_T3 1 1 0.568
## 23 foodst_01_T3 ~~ foodst_04_T3 1 1 0.569
## 24 foodst_01_T3 ~~ foodst_05_T3 1 1 0.188
## 25 foodst_01_T3 ~~ foodst_06_T3 1 1 -0.099
## 26 foodst_01_T3 ~~ foodst_07_T3 1 1 -0.085
## 27 foodst_01_T3 ~~ foodst_08_T3 1 1 0.141
## 28 foodst_02_T3 ~~ foodst_03_T3 1 1 0.068
## 29 foodst_02_T3 ~~ foodst_04_T3 1 1 0.090
## 30 foodst_02_T3 ~~ foodst_05_T3 1 1 0.041
## 31 foodst_02_T3 ~~ foodst_06_T3 1 1 0.074
## 32 foodst_02_T3 ~~ foodst_07_T3 1 1 0.064
## 33 foodst_02_T3 ~~ foodst_08_T3 1 1 -0.037
## 34 foodst_03_T3 ~~ foodst_04_T3 1 1 0.766
## 35 foodst_03_T3 ~~ foodst_05_T3 1 1 0.361
## 36 foodst_03_T3 ~~ foodst_06_T3 1 1 -0.238
## 37 foodst_03_T3 ~~ foodst_07_T3 1 1 -0.189
## 38 foodst_03_T3 ~~ foodst_08_T3 1 1 0.174
## 39 foodst_04_T3 ~~ foodst_05_T3 1 1 0.484
## 40 foodst_04_T3 ~~ foodst_06_T3 1 1 -0.227
## 41 foodst_04_T3 ~~ foodst_07_T3 1 1 -0.190
## 42 foodst_04_T3 ~~ foodst_08_T3 1 1 0.199
## 43 foodst_05_T3 ~~ foodst_06_T3 1 1 -0.229
## 44 foodst_05_T3 ~~ foodst_07_T3 1 1 -0.172
## 45 foodst_05_T3 ~~ foodst_08_T3 1 1 0.164
## 46 foodst_06_T3 ~~ foodst_07_T3 1 1 0.389
## 47 foodst_06_T3 ~~ foodst_08_T3 1 1 -0.067
## 48 foodst_07_T3 ~~ foodst_08_T3 1 1 -0.035
## 49 hds_T3 ~~ hds_T3 1 1 66.003
## 50 foodst_01_T3 ~~ foodst_01_T3 1 1 1.638
## 51 foodst_02_T3 ~~ foodst_02_T3 1 1 1.176
## 52 foodst_03_T3 ~~ foodst_03_T3 1 1 1.243
## 53 foodst_04_T3 ~~ foodst_04_T3 1 1 1.553
## 54 foodst_05_T3 ~~ foodst_05_T3 1 1 0.930
## 55 foodst_06_T3 ~~ foodst_06_T3 1 1 1.130
## 56 foodst_07_T3 ~~ foodst_07_T3 1 1 0.801
## 57 foodst_08_T3 ~~ foodst_08_T3 1 1 1.800
## 58 income_cat_T3 ~~ income_cat_T3 1 1 1.942
## 59 income_cat_T3 ~~ age_T3 1 1 0.418
## 60 income_cat_T3 ~~ sex_T3 1 1 -0.037
## 61 income_cat_T3 ~~ bmi_T3 1 1 -1.077
## 62 age_T3 ~~ age_T3 1 1 31.750
## 63 age_T3 ~~ sex_T3 1 1 -0.468
## 64 age_T3 ~~ bmi_T3 1 1 2.472
## 65 sex_T3 ~~ sex_T3 1 1 0.129
## 66 sex_T3 ~~ bmi_T3 1 1 -0.258
## 67 bmi_T3 ~~ bmi_T3 1 1 27.514
## 68 hds_T3 ~1 1 1 0.000
## 69 foodst_01_T3 ~1 1 1 3.437
## 70 foodst_02_T3 ~1 1 1 2.208
## 71 foodst_03_T3 ~1 1 1 3.979
## 72 foodst_04_T3 ~1 1 1 3.665
## 73 foodst_05_T3 ~1 1 1 4.446
## 74 foodst_06_T3 ~1 1 1 1.727
## 75 foodst_07_T3 ~1 1 1 1.534
## 76 foodst_08_T3 ~1 1 1 3.471
## 77 income_cat_T3 ~1 1 1 3.069
## 78 age_T3 ~1 1 1 41.890
## 79 sex_T3 ~1 1 1 1.848
## 80 bmi_T3 ~1 1 1 26.041
## 81 hds_T3 ~1 2 2 10.195
## 82 hds_T3 ~~ hds_T3 2 2 5.915
## 83 ebindfoodst_01_T3 := a1*b1 0 0 ebindfoodst_01_T3 0.002
## 84 ebindfoodst_02_T3 := a2*b2 0 0 ebindfoodst_02_T3 0.056
## 85 ebindfoodst_03_T3 := a3*b3 0 0 ebindfoodst_03_T3 -0.001
## 86 ebindfoodst_04_T3 := a4*b4 0 0 ebindfoodst_04_T3 -0.026
## 87 ebindfoodst_05_T3 := a5*b5 0 0 ebindfoodst_05_T3 0.002
## 88 ebindfoodst_06_T3 := a6*b6 0 0 ebindfoodst_06_T3 -0.038
## 89 ebindfoodst_07_T3 := a7*b7 0 0 ebindfoodst_07_T3 -0.001
## 90 ebindfoodst_08_T3 := a8*b8 0 0 ebindfoodst_08_T3 -0.012
## se z pvalue ci.lower ci.upper
## 1 0.097 5.586 0.000 0.277 0.809
## 2 0.115 7.113 0.000 0.502 1.130
## 3 0.123 -4.322 0.000 -0.869 -0.195
## 4 0.149 1.941 0.052 -0.119 0.699
## 5 0.134 5.045 0.000 0.310 1.045
## 6 0.153 -0.426 0.670 -0.485 0.354
## 7 0.138 -3.987 0.000 -0.929 -0.173
## 8 0.163 -0.338 0.735 -0.502 0.391
## 9 0.099 3.443 0.001 0.071 0.615
## 10 0.024 4.308 0.000 0.038 0.170
## 11 0.382 4.471 0.000 0.664 2.754
## 12 0.026 2.487 0.013 -0.006 0.135
## 13 0.015 0.127 0.899 -0.039 0.042
## 14 0.013 -8.316 0.000 -0.139 -0.070
## 15 0.013 -0.267 0.789 -0.039 0.032
## 16 0.014 -2.665 0.008 -0.078 0.001
## 17 0.011 -2.351 0.019 -0.057 0.004
## 18 0.012 5.521 0.000 0.034 0.102
## 19 0.010 1.040 0.298 -0.018 0.039
## 20 0.016 -2.170 0.030 -0.076 0.009
## 21 0.023 8.285 0.000 0.126 0.249
## 22 0.025 22.878 0.000 0.500 0.636
## 23 0.027 20.787 0.000 0.494 0.644
## 24 0.020 9.317 0.000 0.133 0.243
## 25 0.022 -4.497 0.000 -0.159 -0.039
## 26 0.019 -4.586 0.000 -0.136 -0.034
## 27 0.028 5.054 0.000 0.065 0.217
## 28 0.020 3.482 0.000 0.015 0.122
## 29 0.022 4.091 0.000 0.030 0.149
## 30 0.017 2.410 0.016 -0.005 0.087
## 31 0.019 3.938 0.000 0.022 0.125
## 32 0.016 4.088 0.000 0.021 0.107
## 33 0.024 -1.554 0.120 -0.101 0.028
## 34 0.026 29.860 0.000 0.696 0.836
## 35 0.018 19.660 0.000 0.310 0.411
## 36 0.020 -12.168 0.000 -0.291 -0.184
## 37 0.016 -11.503 0.000 -0.234 -0.144
## 38 0.024 7.142 0.000 0.107 0.241
## 39 0.021 23.086 0.000 0.426 0.541
## 40 0.022 -10.459 0.000 -0.287 -0.168
## 41 0.018 -10.377 0.000 -0.240 -0.140
## 42 0.027 7.305 0.000 0.124 0.273
## 43 0.017 -13.471 0.000 -0.275 -0.182
## 44 0.014 -12.078 0.000 -0.211 -0.133
## 45 0.021 7.796 0.000 0.107 0.222
## 46 0.017 23.426 0.000 0.344 0.435
## 47 0.023 -2.886 0.004 -0.130 -0.003
## 48 0.019 -1.789 0.074 -0.088 0.018
## 49 1.511 43.687 0.000 61.872 70.134
## 50 0.037 43.732 0.000 1.535 1.740
## 51 0.027 43.732 0.000 1.102 1.249
## 52 0.028 43.732 0.000 1.166 1.321
## 53 0.036 43.732 0.000 1.456 1.650
## 54 0.021 43.732 0.000 0.872 0.988
## 55 0.026 43.732 0.000 1.059 1.201
## 56 0.018 43.732 0.000 0.751 0.851
## 57 0.041 43.732 0.000 1.687 1.912
## 58 0.000 NA NA 1.942 1.942
## 59 0.000 NA NA 0.418 0.418
## 60 0.000 NA NA -0.037 -0.037
## 61 0.000 NA NA -1.077 -1.077
## 62 0.000 NA NA 31.750 31.750
## 63 0.000 NA NA -0.468 -0.468
## 64 0.000 NA NA 2.472 2.472
## 65 0.000 NA NA 0.129 0.129
## 66 0.000 NA NA -0.258 -0.258
## 67 0.000 NA NA 27.514 27.514
## 68 0.000 NA NA 0.000 0.000
## 69 0.050 68.690 0.000 3.301 3.574
## 70 0.042 52.083 0.000 2.092 2.324
## 71 0.044 91.256 0.000 3.860 4.098
## 72 0.049 75.202 0.000 3.532 3.798
## 73 0.038 117.889 0.000 4.343 4.549
## 74 0.042 41.542 0.000 1.613 1.841
## 75 0.035 43.834 0.000 1.438 1.630
## 76 0.052 66.166 0.000 3.328 3.615
## 77 0.000 NA NA 3.069 3.069
## 78 0.000 NA NA 41.890 41.890
## 79 0.000 NA NA 1.848 1.848
## 80 0.000 NA NA 26.041 26.041
## 81 2.004 5.088 0.000 4.716 15.675
## 82 3.038 1.947 0.052 -2.393 14.222
## 83 0.012 0.127 0.899 -0.032 0.035
## 84 0.015 3.835 0.000 0.016 0.095
## 85 0.004 -0.265 0.791 -0.011 0.009
## 86 0.011 -2.357 0.018 -0.056 0.004
## 87 0.004 0.419 0.675 -0.009 0.013
## 88 0.012 -3.232 0.001 -0.069 -0.006
## 89 0.002 -0.322 0.748 -0.006 0.004
## 90 0.006 -1.836 0.066 -0.029 0.006Controlled for age, sex, BMI
SEM_model_mig <- '
level: 1
hds_T3 ~ migration + a1*foodst_01_T3 + a2*foodst_02_T3 + a3*foodst_03_T3 + a4*foodst_04_T3 + a5*foodst_05_T3 + a6*foodst_06_T3 + a7*foodst_07_T3 + a8*foodst_08_T3 + age_T3 + sex_T3 + bmi_T3
foodst_01_T3 ~ b1*migration
foodst_02_T3 ~ b2*migration
foodst_03_T3 ~ b3*migration
foodst_04_T3 ~ b4*migration
foodst_05_T3 ~ b5*migration
foodst_06_T3 ~ b6*migration
foodst_07_T3 ~ b7*migration
foodst_08_T3 ~ b8*migration
foodst_01_T3 ~~ foodst_02_T3 + foodst_03_T3 + foodst_04_T3 + foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_02_T3 ~~ foodst_03_T3 + foodst_04_T3 + foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_03_T3 ~~ foodst_04_T3 + foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_04_T3 ~~ foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_05_T3 ~~ foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_06_T3 ~~ foodst_07_T3 + foodst_08_T3
foodst_07_T3 ~~ foodst_08_T3
ebindfoodst_01_T3 := a1*b1
ebindfoodst_02_T3 := a2*b2
ebindfoodst_03_T3 := a3*b3
ebindfoodst_04_T3 := a4*b4
ebindfoodst_05_T3 := a5*b5
ebindfoodst_06_T3 := a6*b6
ebindfoodst_07_T3 := a7*b7
ebindfoodst_08_T3 := a8*b8
level: 2
hds_T3 ~ 1
'
fit_SEM_mig <- sem(model = SEM_model_mig, data = subset, cluster = "country")
summary(fit_SEM_mig)## lavaan 0.6.16 ended normally after 112 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 67
##
## Number of observations 4051
## Number of clusters [country] 8
##
## Model Test User Model:
##
## Test statistic 100.641
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
##
## Level 1 [within]:
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## hds_T3 ~
## migration -0.550 0.386 -1.425 0.154
## fds_01_T3 (a1) 0.825 0.112 7.392 0.000
## fds_02_T3 (a2) -0.650 0.118 -5.497 0.000
## fds_03_T3 (a3) 0.342 0.144 2.372 0.018
## fds_04_T3 (a4) 0.615 0.130 4.729 0.000
## fds_05_T3 (a5) -0.109 0.150 -0.721 0.471
## fds_06_T3 (a6) -0.508 0.135 -3.770 0.000
## fds_07_T3 (a7) -0.056 0.160 -0.352 0.725
## fds_08_T3 (a8) 0.337 0.096 3.498 0.000
## age_T3 0.109 0.024 4.635 0.000
## sex_T3 1.424 0.375 3.795 0.000
## bmi_T3 0.046 0.025 1.880 0.060
## foodst_01_T3 ~
## migration (b1) 0.120 0.061 1.989 0.047
## foodst_02_T3 ~
## migration (b2) 0.142 0.052 2.739 0.006
## foodst_03_T3 ~
## migration (b3) 0.043 0.053 0.812 0.417
## foodst_04_T3 ~
## migration (b4) 0.169 0.059 2.873 0.004
## foodst_05_T3 ~
## migration (b5) -0.016 0.045 -0.343 0.731
## foodst_06_T3 ~
## migration (b6) -0.085 0.050 -1.696 0.090
## foodst_07_T3 ~
## migration (b7) 0.011 0.042 0.249 0.803
## foodst_08_T3 ~
## migration (b8) 0.093 0.064 1.459 0.145
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .foodst_01_T3 ~~
## .foodst_02_T3 0.184 0.022 8.222 0.000
## .foodst_03_T3 0.577 0.024 23.751 0.000
## .foodst_04_T3 0.568 0.027 21.304 0.000
## .foodst_05_T3 0.190 0.020 9.703 0.000
## .foodst_06_T3 -0.101 0.022 -4.678 0.000
## .foodst_07_T3 -0.090 0.018 -4.976 0.000
## .foodst_08_T3 0.145 0.027 5.301 0.000
## .foodst_02_T3 ~~
## .foodst_03_T3 0.061 0.019 3.131 0.002
## .foodst_04_T3 0.101 0.022 4.651 0.000
## .foodst_05_T3 0.049 0.017 2.955 0.003
## .foodst_06_T3 0.060 0.018 3.273 0.001
## .foodst_07_T3 0.065 0.015 4.213 0.000
## .foodst_08_T3 -0.040 0.023 -1.735 0.083
## .foodst_03_T3 ~~
## .foodst_04_T3 0.756 0.025 30.325 0.000
## .foodst_05_T3 0.356 0.018 20.078 0.000
## .foodst_06_T3 -0.231 0.019 -12.112 0.000
## .foodst_07_T3 -0.186 0.016 -11.657 0.000
## .foodst_08_T3 0.174 0.024 7.283 0.000
## .foodst_04_T3 ~~
## .foodst_05_T3 0.483 0.020 23.839 0.000
## .foodst_06_T3 -0.226 0.021 -10.689 0.000
## .foodst_07_T3 -0.192 0.018 -10.796 0.000
## .foodst_08_T3 0.204 0.027 7.674 0.000
## .foodst_05_T3 ~~
## .foodst_06_T3 -0.228 0.016 -13.849 0.000
## .foodst_07_T3 -0.170 0.014 -12.413 0.000
## .foodst_08_T3 0.166 0.020 8.121 0.000
## .foodst_06_T3 ~~
## .foodst_07_T3 0.396 0.016 24.468 0.000
## .foodst_08_T3 -0.057 0.023 -2.543 0.011
## .foodst_07_T3 ~~
## .foodst_08_T3 -0.035 0.019 -1.847 0.065
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 0.000
## .foodst_01_T3 3.314 0.071 46.550 0.000
## .foodst_02_T3 1.741 0.061 28.510 0.000
## .foodst_03_T3 3.920 0.062 63.206 0.000
## .foodst_04_T3 3.366 0.069 48.669 0.000
## .foodst_05_T3 4.391 0.053 82.550 0.000
## .foodst_06_T3 2.017 0.059 34.130 0.000
## .foodst_07_T3 1.551 0.050 31.332 0.000
## .foodst_08_T3 3.262 0.075 43.578 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 66.360 1.476 44.962 0.000
## .foodst_01_T3 1.647 0.037 45.005 0.000
## .foodst_02_T3 1.212 0.027 45.005 0.000
## .foodst_03_T3 1.250 0.028 45.005 0.000
## .foodst_04_T3 1.555 0.035 45.005 0.000
## .foodst_05_T3 0.920 0.020 45.006 0.000
## .foodst_06_T3 1.135 0.025 45.005 0.000
## .foodst_07_T3 0.797 0.018 45.006 0.000
## .foodst_08_T3 1.821 0.040 45.006 0.000
##
##
## Level 2 [country]:
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 13.554 1.974 6.867 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 6.250 3.204 1.951 0.051
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ebndfdst_01_T3 0.099 0.052 1.921 0.055
## ebndfdst_02_T3 -0.093 0.038 -2.452 0.014
## ebndfdst_03_T3 0.015 0.019 0.768 0.442
## ebndfdst_04_T3 0.104 0.042 2.455 0.014
## ebndfdst_05_T3 0.002 0.005 0.310 0.757
## ebndfdst_06_T3 0.043 0.028 1.547 0.122
## ebndfdst_07_T3 -0.001 0.003 -0.204 0.839
## ebndfdst_08_T3 0.031 0.023 1.346 0.178parameterEstimates(fit_SEM_mig, ci=TRUE, level=.99375)## lhs op rhs block level label est se
## 1 hds_T3 ~ migration 1 1 -0.550 0.386
## 2 hds_T3 ~ foodst_01_T3 1 1 a1 0.825 0.112
## 3 hds_T3 ~ foodst_02_T3 1 1 a2 -0.650 0.118
## 4 hds_T3 ~ foodst_03_T3 1 1 a3 0.342 0.144
## 5 hds_T3 ~ foodst_04_T3 1 1 a4 0.615 0.130
## 6 hds_T3 ~ foodst_05_T3 1 1 a5 -0.109 0.150
## 7 hds_T3 ~ foodst_06_T3 1 1 a6 -0.508 0.135
## 8 hds_T3 ~ foodst_07_T3 1 1 a7 -0.056 0.160
## 9 hds_T3 ~ foodst_08_T3 1 1 a8 0.337 0.096
## 10 hds_T3 ~ age_T3 1 1 0.109 0.024
## 11 hds_T3 ~ sex_T3 1 1 1.424 0.375
## 12 hds_T3 ~ bmi_T3 1 1 0.046 0.025
## 13 foodst_01_T3 ~ migration 1 1 b1 0.120 0.061
## 14 foodst_02_T3 ~ migration 1 1 b2 0.142 0.052
## 15 foodst_03_T3 ~ migration 1 1 b3 0.043 0.053
## 16 foodst_04_T3 ~ migration 1 1 b4 0.169 0.059
## 17 foodst_05_T3 ~ migration 1 1 b5 -0.016 0.045
## 18 foodst_06_T3 ~ migration 1 1 b6 -0.085 0.050
## 19 foodst_07_T3 ~ migration 1 1 b7 0.011 0.042
## 20 foodst_08_T3 ~ migration 1 1 b8 0.093 0.064
## 21 foodst_01_T3 ~~ foodst_02_T3 1 1 0.184 0.022
## 22 foodst_01_T3 ~~ foodst_03_T3 1 1 0.577 0.024
## 23 foodst_01_T3 ~~ foodst_04_T3 1 1 0.568 0.027
## 24 foodst_01_T3 ~~ foodst_05_T3 1 1 0.190 0.020
## 25 foodst_01_T3 ~~ foodst_06_T3 1 1 -0.101 0.022
## 26 foodst_01_T3 ~~ foodst_07_T3 1 1 -0.090 0.018
## 27 foodst_01_T3 ~~ foodst_08_T3 1 1 0.145 0.027
## 28 foodst_02_T3 ~~ foodst_03_T3 1 1 0.061 0.019
## 29 foodst_02_T3 ~~ foodst_04_T3 1 1 0.101 0.022
## 30 foodst_02_T3 ~~ foodst_05_T3 1 1 0.049 0.017
## 31 foodst_02_T3 ~~ foodst_06_T3 1 1 0.060 0.018
## 32 foodst_02_T3 ~~ foodst_07_T3 1 1 0.065 0.015
## 33 foodst_02_T3 ~~ foodst_08_T3 1 1 -0.040 0.023
## 34 foodst_03_T3 ~~ foodst_04_T3 1 1 0.756 0.025
## 35 foodst_03_T3 ~~ foodst_05_T3 1 1 0.356 0.018
## 36 foodst_03_T3 ~~ foodst_06_T3 1 1 -0.231 0.019
## 37 foodst_03_T3 ~~ foodst_07_T3 1 1 -0.186 0.016
## 38 foodst_03_T3 ~~ foodst_08_T3 1 1 0.174 0.024
## 39 foodst_04_T3 ~~ foodst_05_T3 1 1 0.483 0.020
## 40 foodst_04_T3 ~~ foodst_06_T3 1 1 -0.226 0.021
## 41 foodst_04_T3 ~~ foodst_07_T3 1 1 -0.192 0.018
## 42 foodst_04_T3 ~~ foodst_08_T3 1 1 0.204 0.027
## 43 foodst_05_T3 ~~ foodst_06_T3 1 1 -0.228 0.016
## 44 foodst_05_T3 ~~ foodst_07_T3 1 1 -0.170 0.014
## 45 foodst_05_T3 ~~ foodst_08_T3 1 1 0.166 0.020
## 46 foodst_06_T3 ~~ foodst_07_T3 1 1 0.396 0.016
## 47 foodst_06_T3 ~~ foodst_08_T3 1 1 -0.057 0.023
## 48 foodst_07_T3 ~~ foodst_08_T3 1 1 -0.035 0.019
## 49 hds_T3 ~~ hds_T3 1 1 66.360 1.476
## 50 foodst_01_T3 ~~ foodst_01_T3 1 1 1.647 0.037
## 51 foodst_02_T3 ~~ foodst_02_T3 1 1 1.212 0.027
## 52 foodst_03_T3 ~~ foodst_03_T3 1 1 1.250 0.028
## 53 foodst_04_T3 ~~ foodst_04_T3 1 1 1.555 0.035
## 54 foodst_05_T3 ~~ foodst_05_T3 1 1 0.920 0.020
## 55 foodst_06_T3 ~~ foodst_06_T3 1 1 1.135 0.025
## 56 foodst_07_T3 ~~ foodst_07_T3 1 1 0.797 0.018
## 57 foodst_08_T3 ~~ foodst_08_T3 1 1 1.821 0.040
## 58 migration ~~ migration 1 1 0.111 0.000
## 59 migration ~~ age_T3 1 1 0.032 0.000
## 60 migration ~~ sex_T3 1 1 -0.004 0.000
## 61 migration ~~ bmi_T3 1 1 0.073 0.000
## 62 age_T3 ~~ age_T3 1 1 31.686 0.000
## 63 age_T3 ~~ sex_T3 1 1 -0.456 0.000
## 64 age_T3 ~~ bmi_T3 1 1 2.483 0.000
## 65 sex_T3 ~~ sex_T3 1 1 0.126 0.000
## 66 sex_T3 ~~ bmi_T3 1 1 -0.249 0.000
## 67 bmi_T3 ~~ bmi_T3 1 1 27.722 0.000
## 68 hds_T3 ~1 1 1 0.000 0.000
## 69 foodst_01_T3 ~1 1 1 3.314 0.071
## 70 foodst_02_T3 ~1 1 1 1.741 0.061
## 71 foodst_03_T3 ~1 1 1 3.920 0.062
## 72 foodst_04_T3 ~1 1 1 3.366 0.069
## 73 foodst_05_T3 ~1 1 1 4.391 0.053
## 74 foodst_06_T3 ~1 1 1 2.017 0.059
## 75 foodst_07_T3 ~1 1 1 1.551 0.050
## 76 foodst_08_T3 ~1 1 1 3.262 0.075
## 77 migration ~1 1 1 1.127 0.000
## 78 age_T3 ~1 1 1 41.916 0.000
## 79 sex_T3 ~1 1 1 1.852 0.000
## 80 bmi_T3 ~1 1 1 26.058 0.000
## 81 hds_T3 ~1 2 2 13.554 1.974
## 82 hds_T3 ~~ hds_T3 2 2 6.250 3.204
## 83 ebindfoodst_01_T3 := a1*b1 0 0 ebindfoodst_01_T3 0.099 0.052
## 84 ebindfoodst_02_T3 := a2*b2 0 0 ebindfoodst_02_T3 -0.093 0.038
## 85 ebindfoodst_03_T3 := a3*b3 0 0 ebindfoodst_03_T3 0.015 0.019
## 86 ebindfoodst_04_T3 := a4*b4 0 0 ebindfoodst_04_T3 0.104 0.042
## 87 ebindfoodst_05_T3 := a5*b5 0 0 ebindfoodst_05_T3 0.002 0.005
## 88 ebindfoodst_06_T3 := a6*b6 0 0 ebindfoodst_06_T3 0.043 0.028
## 89 ebindfoodst_07_T3 := a7*b7 0 0 ebindfoodst_07_T3 -0.001 0.003
## 90 ebindfoodst_08_T3 := a8*b8 0 0 ebindfoodst_08_T3 0.031 0.023
## z pvalue ci.lower ci.upper
## 1 -1.425 0.154 -1.606 0.506
## 2 7.392 0.000 0.520 1.130
## 3 -5.497 0.000 -0.973 -0.327
## 4 2.372 0.018 -0.052 0.737
## 5 4.729 0.000 0.259 0.971
## 6 -0.721 0.471 -0.520 0.303
## 7 -3.770 0.000 -0.877 -0.140
## 8 -0.352 0.725 -0.494 0.382
## 9 3.498 0.000 0.074 0.601
## 10 4.635 0.000 0.045 0.173
## 11 3.795 0.000 0.398 2.449
## 12 1.880 0.060 -0.021 0.114
## 13 1.989 0.047 -0.045 0.286
## 14 2.739 0.006 0.000 0.284
## 15 0.812 0.417 -0.101 0.187
## 16 2.873 0.004 0.008 0.330
## 17 -0.343 0.731 -0.139 0.108
## 18 -1.696 0.090 -0.223 0.052
## 19 0.249 0.803 -0.105 0.126
## 20 1.459 0.145 -0.081 0.267
## 21 8.222 0.000 0.123 0.245
## 22 23.751 0.000 0.511 0.644
## 23 21.304 0.000 0.495 0.641
## 24 9.703 0.000 0.136 0.243
## 25 -4.678 0.000 -0.160 -0.042
## 26 -4.976 0.000 -0.139 -0.040
## 27 5.301 0.000 0.070 0.219
## 28 3.131 0.002 0.008 0.114
## 29 4.651 0.000 0.041 0.160
## 30 2.955 0.003 0.004 0.094
## 31 3.273 0.001 0.010 0.111
## 32 4.213 0.000 0.023 0.107
## 33 -1.735 0.083 -0.104 0.023
## 34 30.325 0.000 0.687 0.824
## 35 20.078 0.000 0.308 0.405
## 36 -12.112 0.000 -0.283 -0.179
## 37 -11.657 0.000 -0.230 -0.142
## 38 7.283 0.000 0.109 0.239
## 39 23.839 0.000 0.428 0.538
## 40 -10.689 0.000 -0.284 -0.168
## 41 -10.796 0.000 -0.240 -0.143
## 42 7.674 0.000 0.132 0.277
## 43 -13.849 0.000 -0.273 -0.183
## 44 -12.413 0.000 -0.208 -0.133
## 45 8.121 0.000 0.110 0.223
## 46 24.468 0.000 0.352 0.440
## 47 -2.543 0.011 -0.119 0.004
## 48 -1.847 0.065 -0.087 0.017
## 49 44.962 0.000 62.324 70.396
## 50 45.005 0.000 1.547 1.747
## 51 45.005 0.000 1.138 1.285
## 52 45.005 0.000 1.174 1.326
## 53 45.005 0.000 1.460 1.649
## 54 45.006 0.000 0.864 0.976
## 55 45.005 0.000 1.066 1.204
## 56 45.006 0.000 0.748 0.845
## 57 45.006 0.000 1.710 1.932
## 58 NA NA 0.111 0.111
## 59 NA NA 0.032 0.032
## 60 NA NA -0.004 -0.004
## 61 NA NA 0.073 0.073
## 62 NA NA 31.686 31.686
## 63 NA NA -0.456 -0.456
## 64 NA NA 2.483 2.483
## 65 NA NA 0.126 0.126
## 66 NA NA -0.249 -0.249
## 67 NA NA 27.722 27.722
## 68 NA NA 0.000 0.000
## 69 46.550 0.000 3.119 3.508
## 70 28.510 0.000 1.574 1.908
## 71 63.206 0.000 3.751 4.090
## 72 48.669 0.000 3.177 3.555
## 73 82.550 0.000 4.246 4.536
## 74 34.130 0.000 1.855 2.178
## 75 31.332 0.000 1.416 1.686
## 76 43.578 0.000 3.057 3.466
## 77 NA NA 1.127 1.127
## 78 NA NA 41.916 41.916
## 79 NA NA 1.852 1.852
## 80 NA NA 26.058 26.058
## 81 6.867 0.000 8.157 18.950
## 82 1.951 0.051 -2.511 15.011
## 83 1.921 0.055 -0.042 0.241
## 84 -2.452 0.014 -0.196 0.011
## 85 0.768 0.442 -0.038 0.067
## 86 2.455 0.014 -0.012 0.220
## 87 0.310 0.757 -0.013 0.017
## 88 1.547 0.122 -0.033 0.120
## 89 -0.204 0.839 -0.009 0.007
## 90 1.346 0.178 -0.032 0.095Controlled for age, sex, BMI
SEM_model_une <- '
level: 1
hds_T3 ~ unemploy + a1*foodst_01_T3 + a2*foodst_02_T3 + a3*foodst_03_T3 + a4*foodst_04_T3 + a5*foodst_05_T3 + a6*foodst_06_T3 + a7*foodst_07_T3 + a8*foodst_08_T3 + age_T3 + sex_T3 + bmi_T3
foodst_01_T3 ~ b1*unemploy
foodst_02_T3 ~ b2*unemploy
foodst_03_T3 ~ b3*unemploy
foodst_04_T3 ~ b4*unemploy
foodst_05_T3 ~ b5*unemploy
foodst_06_T3 ~ b6*unemploy
foodst_07_T3 ~ b7*unemploy
foodst_08_T3 ~ b8*unemploy
foodst_01_T3 ~~ foodst_02_T3 + foodst_03_T3 + foodst_04_T3 + foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_02_T3 ~~ foodst_03_T3 + foodst_04_T3 + foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_03_T3 ~~ foodst_04_T3 + foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_04_T3 ~~ foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_05_T3 ~~ foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_06_T3 ~~ foodst_07_T3 + foodst_08_T3
foodst_07_T3 ~~ foodst_08_T3
ebindfoodst_01_T3 := a1*b1
ebindfoodst_02_T3 := a2*b2
ebindfoodst_03_T3 := a3*b3
ebindfoodst_04_T3 := a4*b4
ebindfoodst_05_T3 := a5*b5
ebindfoodst_06_T3 := a6*b6
ebindfoodst_07_T3 := a7*b7
ebindfoodst_08_T3 := a8*b8
level: 2
hds_T3 ~ 1
'
fit_SEM_une <- sem(model = SEM_model_une, data = subset, cluster = "country")
summary(fit_SEM_une)## lavaan 0.6.16 ended normally after 131 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 67
##
## Number of observations 4051
## Number of clusters [country] 8
##
## Model Test User Model:
##
## Test statistic 98.855
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
##
## Level 1 [within]:
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## hds_T3 ~
## unemploy -1.408 0.454 -3.104 0.002
## fds_01_T3 (a1) 0.819 0.111 7.349 0.000
## fds_02_T3 (a2) -0.649 0.118 -5.496 0.000
## fds_03_T3 (a3) 0.338 0.144 2.344 0.019
## fds_04_T3 (a4) 0.619 0.130 4.768 0.000
## fds_05_T3 (a5) -0.096 0.150 -0.640 0.522
## fds_06_T3 (a6) -0.514 0.135 -3.819 0.000
## fds_07_T3 (a7) -0.064 0.160 -0.401 0.688
## fds_08_T3 (a8) 0.331 0.096 3.437 0.001
## age_T3 0.107 0.023 4.577 0.000
## sex_T3 1.469 0.375 3.919 0.000
## bmi_T3 0.052 0.025 2.084 0.037
## foodst_01_T3 ~
## unemploy (b1) 0.002 0.071 0.024 0.981
## foodst_02_T3 ~
## unemploy (b2) 0.099 0.061 1.626 0.104
## foodst_03_T3 ~
## unemploy (b3) 0.019 0.062 0.309 0.757
## foodst_04_T3 ~
## unemploy (b4) 0.151 0.069 2.187 0.029
## foodst_05_T3 ~
## unemploy (b5) 0.097 0.053 1.829 0.067
## foodst_06_T3 ~
## unemploy (b6) -0.162 0.059 -2.743 0.006
## foodst_07_T3 ~
## unemploy (b7) -0.080 0.049 -1.610 0.107
## foodst_08_T3 ~
## unemploy (b8) -0.012 0.075 -0.156 0.876
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .foodst_01_T3 ~~
## .foodst_02_T3 0.186 0.022 8.296 0.000
## .foodst_03_T3 0.578 0.024 23.760 0.000
## .foodst_04_T3 0.571 0.027 21.361 0.000
## .foodst_05_T3 0.190 0.020 9.691 0.000
## .foodst_06_T3 -0.102 0.022 -4.730 0.000
## .foodst_07_T3 -0.090 0.018 -4.967 0.000
## .foodst_08_T3 0.146 0.027 5.342 0.000
## .foodst_02_T3 ~~
## .foodst_03_T3 0.061 0.019 3.156 0.002
## .foodst_04_T3 0.102 0.022 4.713 0.000
## .foodst_05_T3 0.048 0.017 2.893 0.004
## .foodst_06_T3 0.060 0.018 3.271 0.001
## .foodst_07_T3 0.066 0.015 4.264 0.000
## .foodst_08_T3 -0.039 0.023 -1.667 0.096
## .foodst_03_T3 ~~
## .foodst_04_T3 0.756 0.025 30.331 0.000
## .foodst_05_T3 0.356 0.018 20.072 0.000
## .foodst_06_T3 -0.231 0.019 -12.126 0.000
## .foodst_07_T3 -0.186 0.016 -11.649 0.000
## .foodst_08_T3 0.174 0.024 7.300 0.000
## .foodst_04_T3 ~~
## .foodst_05_T3 0.482 0.020 23.776 0.000
## .foodst_06_T3 -0.226 0.021 -10.673 0.000
## .foodst_07_T3 -0.190 0.018 -10.731 0.000
## .foodst_08_T3 0.206 0.027 7.738 0.000
## .foodst_05_T3 ~~
## .foodst_06_T3 -0.226 0.016 -13.779 0.000
## .foodst_07_T3 -0.170 0.014 -12.379 0.000
## .foodst_08_T3 0.166 0.020 8.118 0.000
## .foodst_06_T3 ~~
## .foodst_07_T3 0.395 0.016 24.427 0.000
## .foodst_08_T3 -0.059 0.023 -2.589 0.010
## .foodst_07_T3 ~~
## .foodst_08_T3 -0.035 0.019 -1.846 0.065
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 0.000
## .foodst_01_T3 3.447 0.080 43.082 0.000
## .foodst_02_T3 1.793 0.069 26.122 0.000
## .foodst_03_T3 3.948 0.070 56.643 0.000
## .foodst_04_T3 3.392 0.078 43.631 0.000
## .foodst_05_T3 4.268 0.060 71.434 0.000
## .foodst_06_T3 2.097 0.066 31.599 0.000
## .foodst_07_T3 1.649 0.056 29.664 0.000
## .foodst_08_T3 3.379 0.084 40.169 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 66.236 1.473 44.962 0.000
## .foodst_01_T3 1.649 0.037 45.006 0.000
## .foodst_02_T3 1.213 0.027 45.006 0.000
## .foodst_03_T3 1.251 0.028 45.005 0.000
## .foodst_04_T3 1.556 0.035 45.005 0.000
## .foodst_05_T3 0.919 0.020 45.006 0.000
## .foodst_06_T3 1.134 0.025 45.005 0.000
## .foodst_07_T3 0.796 0.018 45.006 0.000
## .foodst_08_T3 1.822 0.040 45.006 0.000
##
##
## Level 2 [country]:
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 14.320 1.978 7.239 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 6.259 3.213 1.948 0.051
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ebndfdst_01_T3 0.001 0.058 0.024 0.981
## ebndfdst_02_T3 -0.064 0.041 -1.559 0.119
## ebndfdst_03_T3 0.006 0.021 0.306 0.759
## ebndfdst_04_T3 0.094 0.047 1.988 0.047
## ebndfdst_05_T3 -0.009 0.015 -0.604 0.546
## ebndfdst_06_T3 0.083 0.037 2.228 0.026
## ebndfdst_07_T3 0.005 0.013 0.389 0.697
## ebndfdst_08_T3 -0.004 0.025 -0.156 0.876parameterEstimates(fit_SEM_une, ci=TRUE, level=.99375)## lhs op rhs block level label est se
## 1 hds_T3 ~ unemploy 1 1 -1.408 0.454
## 2 hds_T3 ~ foodst_01_T3 1 1 a1 0.819 0.111
## 3 hds_T3 ~ foodst_02_T3 1 1 a2 -0.649 0.118
## 4 hds_T3 ~ foodst_03_T3 1 1 a3 0.338 0.144
## 5 hds_T3 ~ foodst_04_T3 1 1 a4 0.619 0.130
## 6 hds_T3 ~ foodst_05_T3 1 1 a5 -0.096 0.150
## 7 hds_T3 ~ foodst_06_T3 1 1 a6 -0.514 0.135
## 8 hds_T3 ~ foodst_07_T3 1 1 a7 -0.064 0.160
## 9 hds_T3 ~ foodst_08_T3 1 1 a8 0.331 0.096
## 10 hds_T3 ~ age_T3 1 1 0.107 0.023
## 11 hds_T3 ~ sex_T3 1 1 1.469 0.375
## 12 hds_T3 ~ bmi_T3 1 1 0.052 0.025
## 13 foodst_01_T3 ~ unemploy 1 1 b1 0.002 0.071
## 14 foodst_02_T3 ~ unemploy 1 1 b2 0.099 0.061
## 15 foodst_03_T3 ~ unemploy 1 1 b3 0.019 0.062
## 16 foodst_04_T3 ~ unemploy 1 1 b4 0.151 0.069
## 17 foodst_05_T3 ~ unemploy 1 1 b5 0.097 0.053
## 18 foodst_06_T3 ~ unemploy 1 1 b6 -0.162 0.059
## 19 foodst_07_T3 ~ unemploy 1 1 b7 -0.080 0.049
## 20 foodst_08_T3 ~ unemploy 1 1 b8 -0.012 0.075
## 21 foodst_01_T3 ~~ foodst_02_T3 1 1 0.186 0.022
## 22 foodst_01_T3 ~~ foodst_03_T3 1 1 0.578 0.024
## 23 foodst_01_T3 ~~ foodst_04_T3 1 1 0.571 0.027
## 24 foodst_01_T3 ~~ foodst_05_T3 1 1 0.190 0.020
## 25 foodst_01_T3 ~~ foodst_06_T3 1 1 -0.102 0.022
## 26 foodst_01_T3 ~~ foodst_07_T3 1 1 -0.090 0.018
## 27 foodst_01_T3 ~~ foodst_08_T3 1 1 0.146 0.027
## 28 foodst_02_T3 ~~ foodst_03_T3 1 1 0.061 0.019
## 29 foodst_02_T3 ~~ foodst_04_T3 1 1 0.102 0.022
## 30 foodst_02_T3 ~~ foodst_05_T3 1 1 0.048 0.017
## 31 foodst_02_T3 ~~ foodst_06_T3 1 1 0.060 0.018
## 32 foodst_02_T3 ~~ foodst_07_T3 1 1 0.066 0.015
## 33 foodst_02_T3 ~~ foodst_08_T3 1 1 -0.039 0.023
## 34 foodst_03_T3 ~~ foodst_04_T3 1 1 0.756 0.025
## 35 foodst_03_T3 ~~ foodst_05_T3 1 1 0.356 0.018
## 36 foodst_03_T3 ~~ foodst_06_T3 1 1 -0.231 0.019
## 37 foodst_03_T3 ~~ foodst_07_T3 1 1 -0.186 0.016
## 38 foodst_03_T3 ~~ foodst_08_T3 1 1 0.174 0.024
## 39 foodst_04_T3 ~~ foodst_05_T3 1 1 0.482 0.020
## 40 foodst_04_T3 ~~ foodst_06_T3 1 1 -0.226 0.021
## 41 foodst_04_T3 ~~ foodst_07_T3 1 1 -0.190 0.018
## 42 foodst_04_T3 ~~ foodst_08_T3 1 1 0.206 0.027
## 43 foodst_05_T3 ~~ foodst_06_T3 1 1 -0.226 0.016
## 44 foodst_05_T3 ~~ foodst_07_T3 1 1 -0.170 0.014
## 45 foodst_05_T3 ~~ foodst_08_T3 1 1 0.166 0.020
## 46 foodst_06_T3 ~~ foodst_07_T3 1 1 0.395 0.016
## 47 foodst_06_T3 ~~ foodst_08_T3 1 1 -0.059 0.023
## 48 foodst_07_T3 ~~ foodst_08_T3 1 1 -0.035 0.019
## 49 hds_T3 ~~ hds_T3 1 1 66.236 1.473
## 50 foodst_01_T3 ~~ foodst_01_T3 1 1 1.649 0.037
## 51 foodst_02_T3 ~~ foodst_02_T3 1 1 1.213 0.027
## 52 foodst_03_T3 ~~ foodst_03_T3 1 1 1.251 0.028
## 53 foodst_04_T3 ~~ foodst_04_T3 1 1 1.556 0.035
## 54 foodst_05_T3 ~~ foodst_05_T3 1 1 0.919 0.020
## 55 foodst_06_T3 ~~ foodst_06_T3 1 1 1.134 0.025
## 56 foodst_07_T3 ~~ foodst_07_T3 1 1 0.796 0.018
## 57 foodst_08_T3 ~~ foodst_08_T3 1 1 1.822 0.040
## 58 unemploy ~~ unemploy 1 1 0.080 0.000
## 59 unemploy ~~ age_T3 1 1 -0.023 0.000
## 60 unemploy ~~ sex_T3 1 1 0.002 0.000
## 61 unemploy ~~ bmi_T3 1 1 0.120 0.000
## 62 age_T3 ~~ age_T3 1 1 31.686 0.000
## 63 age_T3 ~~ sex_T3 1 1 -0.456 0.000
## 64 age_T3 ~~ bmi_T3 1 1 2.483 0.000
## 65 sex_T3 ~~ sex_T3 1 1 0.126 0.000
## 66 sex_T3 ~~ bmi_T3 1 1 -0.249 0.000
## 67 bmi_T3 ~~ bmi_T3 1 1 27.722 0.000
## 68 hds_T3 ~1 1 1 0.000 0.000
## 69 foodst_01_T3 ~1 1 1 3.447 0.080
## 70 foodst_02_T3 ~1 1 1 1.793 0.069
## 71 foodst_03_T3 ~1 1 1 3.948 0.070
## 72 foodst_04_T3 ~1 1 1 3.392 0.078
## 73 foodst_05_T3 ~1 1 1 4.268 0.060
## 74 foodst_06_T3 ~1 1 1 2.097 0.066
## 75 foodst_07_T3 ~1 1 1 1.649 0.056
## 76 foodst_08_T3 ~1 1 1 3.379 0.084
## 77 unemploy ~1 1 1 1.088 0.000
## 78 age_T3 ~1 1 1 41.916 0.000
## 79 sex_T3 ~1 1 1 1.852 0.000
## 80 bmi_T3 ~1 1 1 26.058 0.000
## 81 hds_T3 ~1 2 2 14.320 1.978
## 82 hds_T3 ~~ hds_T3 2 2 6.259 3.213
## 83 ebindfoodst_01_T3 := a1*b1 0 0 ebindfoodst_01_T3 0.001 0.058
## 84 ebindfoodst_02_T3 := a2*b2 0 0 ebindfoodst_02_T3 -0.064 0.041
## 85 ebindfoodst_03_T3 := a3*b3 0 0 ebindfoodst_03_T3 0.006 0.021
## 86 ebindfoodst_04_T3 := a4*b4 0 0 ebindfoodst_04_T3 0.094 0.047
## 87 ebindfoodst_05_T3 := a5*b5 0 0 ebindfoodst_05_T3 -0.009 0.015
## 88 ebindfoodst_06_T3 := a6*b6 0 0 ebindfoodst_06_T3 0.083 0.037
## 89 ebindfoodst_07_T3 := a7*b7 0 0 ebindfoodst_07_T3 0.005 0.013
## 90 ebindfoodst_08_T3 := a8*b8 0 0 ebindfoodst_08_T3 -0.004 0.025
## z pvalue ci.lower ci.upper
## 1 -3.104 0.002 -2.649 -0.168
## 2 7.349 0.000 0.514 1.124
## 3 -5.496 0.000 -0.972 -0.326
## 4 2.344 0.019 -0.056 0.732
## 5 4.768 0.000 0.264 0.974
## 6 -0.640 0.522 -0.507 0.315
## 7 -3.819 0.000 -0.882 -0.146
## 8 -0.401 0.688 -0.502 0.373
## 9 3.437 0.001 0.068 0.595
## 10 4.577 0.000 0.043 0.172
## 11 3.919 0.000 0.444 2.494
## 12 2.084 0.037 -0.016 0.119
## 13 0.024 0.981 -0.193 0.196
## 14 1.626 0.104 -0.068 0.266
## 15 0.309 0.757 -0.150 0.189
## 16 2.187 0.029 -0.038 0.340
## 17 1.829 0.067 -0.048 0.242
## 18 -2.743 0.006 -0.323 -0.001
## 19 -1.610 0.107 -0.215 0.056
## 20 -0.156 0.876 -0.216 0.193
## 21 8.296 0.000 0.125 0.247
## 22 23.760 0.000 0.511 0.644
## 23 21.361 0.000 0.498 0.644
## 24 9.691 0.000 0.136 0.243
## 25 -4.730 0.000 -0.161 -0.043
## 26 -4.967 0.000 -0.139 -0.040
## 27 5.342 0.000 0.071 0.221
## 28 3.156 0.002 0.008 0.114
## 29 4.713 0.000 0.043 0.161
## 30 2.893 0.004 0.003 0.093
## 31 3.271 0.001 0.010 0.111
## 32 4.264 0.000 0.024 0.108
## 33 -1.667 0.096 -0.103 0.025
## 34 30.331 0.000 0.688 0.824
## 35 20.072 0.000 0.308 0.405
## 36 -12.126 0.000 -0.283 -0.179
## 37 -11.649 0.000 -0.229 -0.142
## 38 7.300 0.000 0.109 0.240
## 39 23.776 0.000 0.426 0.537
## 40 -10.673 0.000 -0.284 -0.168
## 41 -10.731 0.000 -0.239 -0.142
## 42 7.738 0.000 0.133 0.279
## 43 -13.779 0.000 -0.271 -0.181
## 44 -12.379 0.000 -0.207 -0.132
## 45 8.118 0.000 0.110 0.222
## 46 24.427 0.000 0.351 0.439
## 47 -2.589 0.010 -0.120 0.003
## 48 -1.846 0.065 -0.087 0.017
## 49 44.962 0.000 62.207 70.264
## 50 45.006 0.000 1.548 1.749
## 51 45.006 0.000 1.139 1.287
## 52 45.005 0.000 1.175 1.326
## 53 45.005 0.000 1.462 1.651
## 54 45.006 0.000 0.863 0.975
## 55 45.005 0.000 1.065 1.203
## 56 45.006 0.000 0.748 0.844
## 57 45.006 0.000 1.711 1.933
## 58 NA NA 0.080 0.080
## 59 NA NA -0.023 -0.023
## 60 NA NA 0.002 0.002
## 61 NA NA 0.120 0.120
## 62 NA NA 31.686 31.686
## 63 NA NA -0.456 -0.456
## 64 NA NA 2.483 2.483
## 65 NA NA 0.126 0.126
## 66 NA NA -0.249 -0.249
## 67 NA NA 27.722 27.722
## 68 NA NA 0.000 0.000
## 69 43.082 0.000 3.229 3.666
## 70 26.122 0.000 1.605 1.981
## 71 56.643 0.000 3.757 4.138
## 72 43.631 0.000 3.180 3.605
## 73 71.434 0.000 4.104 4.431
## 74 31.599 0.000 1.915 2.278
## 75 29.664 0.000 1.497 1.801
## 76 40.169 0.000 3.149 3.609
## 77 NA NA 1.088 1.088
## 78 NA NA 41.916 41.916
## 79 NA NA 1.852 1.852
## 80 NA NA 26.058 26.058
## 81 7.239 0.000 8.911 19.728
## 82 1.948 0.051 -2.526 15.044
## 83 0.024 0.981 -0.158 0.161
## 84 -1.559 0.119 -0.177 0.049
## 85 0.306 0.759 -0.051 0.064
## 86 1.988 0.047 -0.035 0.222
## 87 -0.604 0.546 -0.052 0.033
## 88 2.228 0.026 -0.019 0.185
## 89 0.389 0.697 -0.031 0.041
## 90 -0.156 0.876 -0.072 0.064Controlled for age, sex, BMI
SEM_model_sin <- '
level: 1
hds_T3 ~ singlepar + a1*foodst_01_T3 + a2*foodst_02_T3 + a3*foodst_03_T3 + a4*foodst_04_T3 + a5*foodst_05_T3 + a6*foodst_06_T3 + a7*foodst_07_T3 + a8*foodst_08_T3 + age_T3 + sex_T3 + bmi_T3
foodst_01_T3 ~ b1*singlepar
foodst_02_T3 ~ b2*singlepar
foodst_03_T3 ~ b3*singlepar
foodst_04_T3 ~ b4*singlepar
foodst_05_T3 ~ b5*singlepar
foodst_06_T3 ~ b6*singlepar
foodst_07_T3 ~ b7*singlepar
foodst_08_T3 ~ b8*singlepar
foodst_01_T3 ~~ foodst_02_T3 + foodst_03_T3 + foodst_04_T3 + foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_02_T3 ~~ foodst_03_T3 + foodst_04_T3 + foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_03_T3 ~~ foodst_04_T3 + foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_04_T3 ~~ foodst_05_T3 + foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_05_T3 ~~ foodst_06_T3 + foodst_07_T3 + foodst_08_T3
foodst_06_T3 ~~ foodst_07_T3 + foodst_08_T3
foodst_07_T3 ~~ foodst_08_T3
ebindfoodst_01_T3 := a1*b1
ebindfoodst_02_T3 := a2*b2
ebindfoodst_03_T3 := a3*b3
ebindfoodst_04_T3 := a4*b4
ebindfoodst_05_T3 := a5*b5
ebindfoodst_06_T3 := a6*b6
ebindfoodst_07_T3 := a7*b7
ebindfoodst_08_T3 := a8*b8
level: 2
hds_T3 ~ 1
'
fit_SEM_sin <- sem(model = SEM_model_sin, data = subset, cluster = "country")
summary(fit_SEM_sin)## lavaan 0.6.16 ended normally after 111 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 67
##
## Used Total
## Number of observations 4035 4051
## Number of clusters [country] 8
##
## Model Test User Model:
##
## Test statistic 105.711
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
##
## Level 1 [within]:
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## hds_T3 ~
## singlepar 0.291 0.427 0.680 0.496
## fds_01_T3 (a1) 0.826 0.112 7.385 0.000
## fds_02_T3 (a2) -0.650 0.118 -5.500 0.000
## fds_03_T3 (a3) 0.337 0.145 2.333 0.020
## fds_04_T3 (a4) 0.610 0.130 4.690 0.000
## fds_05_T3 (a5) -0.071 0.151 -0.472 0.637
## fds_06_T3 (a6) -0.516 0.135 -3.819 0.000
## fds_07_T3 (a7) -0.047 0.161 -0.294 0.769
## fds_08_T3 (a8) 0.330 0.096 3.421 0.001
## age_T3 0.109 0.024 4.618 0.000
## sex_T3 1.415 0.376 3.762 0.000
## bmi_T3 0.046 0.025 1.850 0.064
## foodst_01_T3 ~
## singlepar (b1) -0.271 0.067 -4.076 0.000
## foodst_02_T3 ~
## singlepar (b2) -0.099 0.057 -1.724 0.085
## foodst_03_T3 ~
## singlepar (b3) -0.236 0.058 -4.069 0.000
## foodst_04_T3 ~
## singlepar (b4) -0.324 0.065 -4.997 0.000
## foodst_05_T3 ~
## singlepar (b5) -0.199 0.050 -3.993 0.000
## foodst_06_T3 ~
## singlepar (b6) 0.252 0.055 4.559 0.000
## foodst_07_T3 ~
## singlepar (b7) 0.104 0.046 2.250 0.024
## foodst_08_T3 ~
## singlepar (b8) 0.107 0.070 1.521 0.128
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .foodst_01_T3 ~~
## .foodst_02_T3 0.184 0.022 8.229 0.000
## .foodst_03_T3 0.573 0.024 23.634 0.000
## .foodst_04_T3 0.564 0.027 21.181 0.000
## .foodst_05_T3 0.185 0.020 9.495 0.000
## .foodst_06_T3 -0.099 0.021 -4.587 0.000
## .foodst_07_T3 -0.087 0.018 -4.846 0.000
## .foodst_08_T3 0.148 0.027 5.425 0.000
## .foodst_02_T3 ~~
## .foodst_03_T3 0.059 0.019 3.045 0.002
## .foodst_04_T3 0.101 0.022 4.642 0.000
## .foodst_05_T3 0.047 0.017 2.835 0.005
## .foodst_06_T3 0.061 0.018 3.279 0.001
## .foodst_07_T3 0.065 0.015 4.231 0.000
## .foodst_08_T3 -0.038 0.023 -1.621 0.105
## .foodst_03_T3 ~~
## .foodst_04_T3 0.751 0.025 30.193 0.000
## .foodst_05_T3 0.353 0.018 19.931 0.000
## .foodst_06_T3 -0.227 0.019 -11.962 0.000
## .foodst_07_T3 -0.183 0.016 -11.511 0.000
## .foodst_08_T3 0.177 0.024 7.413 0.000
## .foodst_04_T3 ~~
## .foodst_05_T3 0.478 0.020 23.634 0.000
## .foodst_06_T3 -0.222 0.021 -10.530 0.000
## .foodst_07_T3 -0.191 0.018 -10.765 0.000
## .foodst_08_T3 0.210 0.027 7.850 0.000
## .foodst_05_T3 ~~
## .foodst_06_T3 -0.223 0.016 -13.609 0.000
## .foodst_07_T3 -0.169 0.014 -12.346 0.000
## .foodst_08_T3 0.169 0.021 8.211 0.000
## .foodst_06_T3 ~~
## .foodst_07_T3 0.391 0.016 24.282 0.000
## .foodst_08_T3 -0.064 0.023 -2.808 0.005
## .foodst_07_T3 ~~
## .foodst_08_T3 -0.038 0.019 -2.019 0.043
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 0.000
## .foodst_01_T3 3.749 0.076 49.266 0.000
## .foodst_02_T3 2.011 0.066 30.689 0.000
## .foodst_03_T3 4.229 0.066 63.788 0.000
## .foodst_04_T3 3.912 0.074 52.851 0.000
## .foodst_05_T3 4.592 0.057 80.687 0.000
## .foodst_06_T3 1.642 0.063 26.011 0.000
## .foodst_07_T3 1.447 0.053 27.366 0.000
## .foodst_08_T3 3.248 0.080 40.456 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 66.216 1.476 44.872 0.000
## .foodst_01_T3 1.640 0.037 44.917 0.000
## .foodst_02_T3 1.216 0.027 44.917 0.000
## .foodst_03_T3 1.245 0.028 44.917 0.000
## .foodst_04_T3 1.551 0.035 44.916 0.000
## .foodst_05_T3 0.917 0.020 44.917 0.000
## .foodst_06_T3 1.128 0.025 44.916 0.000
## .foodst_07_T3 0.792 0.018 44.917 0.000
## .foodst_08_T3 1.825 0.041 44.917 0.000
##
##
## Level 2 [country]:
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 12.545 1.978 6.344 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 6.197 3.179 1.950 0.051
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ebndfdst_01_T3 -0.224 0.063 -3.569 0.000
## ebndfdst_02_T3 0.064 0.039 1.645 0.100
## ebndfdst_03_T3 -0.080 0.039 -2.024 0.043
## ebndfdst_04_T3 -0.197 0.058 -3.420 0.001
## ebndfdst_05_T3 0.014 0.030 0.469 0.639
## ebndfdst_06_T3 -0.130 0.044 -2.927 0.003
## ebndfdst_07_T3 -0.005 0.017 -0.292 0.771
## ebndfdst_08_T3 0.035 0.025 1.390 0.165parameterEstimates(fit_SEM_sin, ci=TRUE, level=.99375)## lhs op rhs block level label est se
## 1 hds_T3 ~ singlepar 1 1 0.291 0.427
## 2 hds_T3 ~ foodst_01_T3 1 1 a1 0.826 0.112
## 3 hds_T3 ~ foodst_02_T3 1 1 a2 -0.650 0.118
## 4 hds_T3 ~ foodst_03_T3 1 1 a3 0.337 0.145
## 5 hds_T3 ~ foodst_04_T3 1 1 a4 0.610 0.130
## 6 hds_T3 ~ foodst_05_T3 1 1 a5 -0.071 0.151
## 7 hds_T3 ~ foodst_06_T3 1 1 a6 -0.516 0.135
## 8 hds_T3 ~ foodst_07_T3 1 1 a7 -0.047 0.161
## 9 hds_T3 ~ foodst_08_T3 1 1 a8 0.330 0.096
## 10 hds_T3 ~ age_T3 1 1 0.109 0.024
## 11 hds_T3 ~ sex_T3 1 1 1.415 0.376
## 12 hds_T3 ~ bmi_T3 1 1 0.046 0.025
## 13 foodst_01_T3 ~ singlepar 1 1 b1 -0.271 0.067
## 14 foodst_02_T3 ~ singlepar 1 1 b2 -0.099 0.057
## 15 foodst_03_T3 ~ singlepar 1 1 b3 -0.236 0.058
## 16 foodst_04_T3 ~ singlepar 1 1 b4 -0.324 0.065
## 17 foodst_05_T3 ~ singlepar 1 1 b5 -0.199 0.050
## 18 foodst_06_T3 ~ singlepar 1 1 b6 0.252 0.055
## 19 foodst_07_T3 ~ singlepar 1 1 b7 0.104 0.046
## 20 foodst_08_T3 ~ singlepar 1 1 b8 0.107 0.070
## 21 foodst_01_T3 ~~ foodst_02_T3 1 1 0.184 0.022
## 22 foodst_01_T3 ~~ foodst_03_T3 1 1 0.573 0.024
## 23 foodst_01_T3 ~~ foodst_04_T3 1 1 0.564 0.027
## 24 foodst_01_T3 ~~ foodst_05_T3 1 1 0.185 0.020
## 25 foodst_01_T3 ~~ foodst_06_T3 1 1 -0.099 0.021
## 26 foodst_01_T3 ~~ foodst_07_T3 1 1 -0.087 0.018
## 27 foodst_01_T3 ~~ foodst_08_T3 1 1 0.148 0.027
## 28 foodst_02_T3 ~~ foodst_03_T3 1 1 0.059 0.019
## 29 foodst_02_T3 ~~ foodst_04_T3 1 1 0.101 0.022
## 30 foodst_02_T3 ~~ foodst_05_T3 1 1 0.047 0.017
## 31 foodst_02_T3 ~~ foodst_06_T3 1 1 0.061 0.018
## 32 foodst_02_T3 ~~ foodst_07_T3 1 1 0.065 0.015
## 33 foodst_02_T3 ~~ foodst_08_T3 1 1 -0.038 0.023
## 34 foodst_03_T3 ~~ foodst_04_T3 1 1 0.751 0.025
## 35 foodst_03_T3 ~~ foodst_05_T3 1 1 0.353 0.018
## 36 foodst_03_T3 ~~ foodst_06_T3 1 1 -0.227 0.019
## 37 foodst_03_T3 ~~ foodst_07_T3 1 1 -0.183 0.016
## 38 foodst_03_T3 ~~ foodst_08_T3 1 1 0.177 0.024
## 39 foodst_04_T3 ~~ foodst_05_T3 1 1 0.478 0.020
## 40 foodst_04_T3 ~~ foodst_06_T3 1 1 -0.222 0.021
## 41 foodst_04_T3 ~~ foodst_07_T3 1 1 -0.191 0.018
## 42 foodst_04_T3 ~~ foodst_08_T3 1 1 0.210 0.027
## 43 foodst_05_T3 ~~ foodst_06_T3 1 1 -0.223 0.016
## 44 foodst_05_T3 ~~ foodst_07_T3 1 1 -0.169 0.014
## 45 foodst_05_T3 ~~ foodst_08_T3 1 1 0.169 0.021
## 46 foodst_06_T3 ~~ foodst_07_T3 1 1 0.391 0.016
## 47 foodst_06_T3 ~~ foodst_08_T3 1 1 -0.064 0.023
## 48 foodst_07_T3 ~~ foodst_08_T3 1 1 -0.038 0.019
## 49 hds_T3 ~~ hds_T3 1 1 66.216 1.476
## 50 foodst_01_T3 ~~ foodst_01_T3 1 1 1.640 0.037
## 51 foodst_02_T3 ~~ foodst_02_T3 1 1 1.216 0.027
## 52 foodst_03_T3 ~~ foodst_03_T3 1 1 1.245 0.028
## 53 foodst_04_T3 ~~ foodst_04_T3 1 1 1.551 0.035
## 54 foodst_05_T3 ~~ foodst_05_T3 1 1 0.917 0.020
## 55 foodst_06_T3 ~~ foodst_06_T3 1 1 1.128 0.025
## 56 foodst_07_T3 ~~ foodst_07_T3 1 1 0.792 0.018
## 57 foodst_08_T3 ~~ foodst_08_T3 1 1 1.825 0.041
## 58 singlepar ~~ singlepar 1 1 0.092 0.000
## 59 singlepar ~~ age_T3 1 1 0.015 0.000
## 60 singlepar ~~ sex_T3 1 1 0.007 0.000
## 61 singlepar ~~ bmi_T3 1 1 -0.060 0.000
## 62 age_T3 ~~ age_T3 1 1 31.762 0.000
## 63 age_T3 ~~ sex_T3 1 1 -0.458 0.000
## 64 age_T3 ~~ bmi_T3 1 1 2.509 0.000
## 65 sex_T3 ~~ sex_T3 1 1 0.126 0.000
## 66 sex_T3 ~~ bmi_T3 1 1 -0.249 0.000
## 67 bmi_T3 ~~ bmi_T3 1 1 27.754 0.000
## 68 hds_T3 ~1 1 1 0.000 0.000
## 69 foodst_01_T3 ~1 1 1 3.749 0.076
## 70 foodst_02_T3 ~1 1 1 2.011 0.066
## 71 foodst_03_T3 ~1 1 1 4.229 0.066
## 72 foodst_04_T3 ~1 1 1 3.912 0.074
## 73 foodst_05_T3 ~1 1 1 4.592 0.057
## 74 foodst_06_T3 ~1 1 1 1.642 0.063
## 75 foodst_07_T3 ~1 1 1 1.447 0.053
## 76 foodst_08_T3 ~1 1 1 3.248 0.080
## 77 singlepar ~1 1 1 1.102 0.000
## 78 age_T3 ~1 1 1 41.905 0.000
## 79 sex_T3 ~1 1 1 1.852 0.000
## 80 bmi_T3 ~1 1 1 26.068 0.000
## 81 hds_T3 ~1 2 2 12.545 1.978
## 82 hds_T3 ~~ hds_T3 2 2 6.197 3.179
## 83 ebindfoodst_01_T3 := a1*b1 0 0 ebindfoodst_01_T3 -0.224 0.063
## 84 ebindfoodst_02_T3 := a2*b2 0 0 ebindfoodst_02_T3 0.064 0.039
## 85 ebindfoodst_03_T3 := a3*b3 0 0 ebindfoodst_03_T3 -0.080 0.039
## 86 ebindfoodst_04_T3 := a4*b4 0 0 ebindfoodst_04_T3 -0.197 0.058
## 87 ebindfoodst_05_T3 := a5*b5 0 0 ebindfoodst_05_T3 0.014 0.030
## 88 ebindfoodst_06_T3 := a6*b6 0 0 ebindfoodst_06_T3 -0.130 0.044
## 89 ebindfoodst_07_T3 := a7*b7 0 0 ebindfoodst_07_T3 -0.005 0.017
## 90 ebindfoodst_08_T3 := a8*b8 0 0 ebindfoodst_08_T3 0.035 0.025
## z pvalue ci.lower ci.upper
## 1 0.680 0.496 -0.878 1.460
## 2 7.385 0.000 0.520 1.132
## 3 -5.500 0.000 -0.973 -0.327
## 4 2.333 0.020 -0.058 0.733
## 5 4.690 0.000 0.254 0.966
## 6 -0.472 0.637 -0.483 0.341
## 7 -3.819 0.000 -0.885 -0.146
## 8 -0.294 0.769 -0.486 0.392
## 9 3.421 0.001 0.066 0.594
## 10 4.618 0.000 0.044 0.173
## 11 3.762 0.000 0.387 2.444
## 12 1.850 0.064 -0.022 0.113
## 13 -4.076 0.000 -0.453 -0.089
## 14 -1.724 0.085 -0.256 0.058
## 15 -4.069 0.000 -0.395 -0.077
## 16 -4.997 0.000 -0.501 -0.147
## 17 -3.993 0.000 -0.335 -0.063
## 18 4.559 0.000 0.101 0.403
## 19 2.250 0.024 -0.022 0.231
## 20 1.521 0.128 -0.085 0.299
## 21 8.229 0.000 0.123 0.246
## 22 23.634 0.000 0.506 0.639
## 23 21.181 0.000 0.491 0.637
## 24 9.495 0.000 0.132 0.239
## 25 -4.587 0.000 -0.157 -0.040
## 26 -4.846 0.000 -0.136 -0.038
## 27 5.425 0.000 0.074 0.223
## 28 3.045 0.002 0.006 0.112
## 29 4.642 0.000 0.041 0.160
## 30 2.835 0.005 0.002 0.093
## 31 3.279 0.001 0.010 0.111
## 32 4.231 0.000 0.023 0.108
## 33 -1.621 0.105 -0.102 0.026
## 34 30.193 0.000 0.683 0.819
## 35 19.931 0.000 0.305 0.402
## 36 -11.962 0.000 -0.279 -0.175
## 37 -11.511 0.000 -0.226 -0.139
## 38 7.413 0.000 0.112 0.242
## 39 23.634 0.000 0.423 0.533
## 40 -10.530 0.000 -0.280 -0.165
## 41 -10.765 0.000 -0.239 -0.142
## 42 7.850 0.000 0.137 0.283
## 43 -13.609 0.000 -0.268 -0.178
## 44 -12.346 0.000 -0.206 -0.131
## 45 8.211 0.000 0.112 0.225
## 46 24.282 0.000 0.347 0.435
## 47 -2.808 0.005 -0.125 -0.002
## 48 -2.019 0.043 -0.090 0.014
## 49 44.872 0.000 62.181 70.251
## 50 44.917 0.000 1.540 1.740
## 51 44.917 0.000 1.142 1.290
## 52 44.917 0.000 1.169 1.321
## 53 44.916 0.000 1.457 1.646
## 54 44.917 0.000 0.861 0.973
## 55 44.916 0.000 1.060 1.197
## 56 44.917 0.000 0.744 0.840
## 57 44.917 0.000 1.714 1.936
## 58 NA NA 0.092 0.092
## 59 NA NA 0.015 0.015
## 60 NA NA 0.007 0.007
## 61 NA NA -0.060 -0.060
## 62 NA NA 31.762 31.762
## 63 NA NA -0.458 -0.458
## 64 NA NA 2.509 2.509
## 65 NA NA 0.126 0.126
## 66 NA NA -0.249 -0.249
## 67 NA NA 27.754 27.754
## 68 NA NA 0.000 0.000
## 69 49.266 0.000 3.541 3.957
## 70 30.689 0.000 1.832 2.190
## 71 63.788 0.000 4.048 4.411
## 72 52.851 0.000 3.709 4.114
## 73 80.687 0.000 4.436 4.747
## 74 26.011 0.000 1.469 1.815
## 75 27.366 0.000 1.303 1.592
## 76 40.456 0.000 3.028 3.467
## 77 NA NA 1.102 1.102
## 78 NA NA 41.905 41.905
## 79 NA NA 1.852 1.852
## 80 NA NA 26.068 26.068
## 81 6.344 0.000 7.137 17.952
## 82 1.950 0.051 -2.495 14.889
## 83 -3.569 0.000 -0.396 -0.052
## 84 1.645 0.100 -0.043 0.171
## 85 -2.024 0.043 -0.187 0.028
## 86 -3.420 0.001 -0.355 -0.040
## 87 0.469 0.639 -0.068 0.097
## 88 -2.927 0.003 -0.251 -0.009
## 89 -0.292 0.771 -0.051 0.041
## 90 1.390 0.165 -0.034 0.105