Here we present the syntax for the sensitivity analysis excluding participants from Belgium and Spain. In these countries, the answer categories for the questionnaire asking about consumer attitudes were reversed.
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", country != "4", country != "9") %>%
#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 == "6" ~ "Sweden",
country == "7" ~ "Germany",
country == "8" ~ "Hungary",
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$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, excluding Belgium and Spain"
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)| Cyprus (N=773) |
Estonia (N=595) |
Germany (N=509) |
Hungary (N=661) |
Italy (N=748) |
Sweden (N=439) |
Overall (N=3725) |
|
|---|---|---|---|---|---|---|---|
Abbreviations: HDAS = Healthy Dietary Adherence Score ; SD = standard deviation ; BMI = body mass index | |||||||
| HDAS | |||||||
| Mean (SD) | 25.0 (9.11) | 27.7 (7.89) | 23.0 (8.89) | 23.2 (8.88) | 24.3 (8.03) | 30.7 (8.06) | 25.4 (8.87) |
| Median [Min, Max] | 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] | 32.0 [7.00, 48.0] | 26.0 [0, 48.0] |
| Highest education level in household | |||||||
| low | 9 (1.2%) | 0 (0%) | 32 (6.3%) | 14 (2.1%) | 118 (15.8%) | 0 (0%) | 173 (4.6%) |
| medium | 283 (36.6%) | 163 (27.4%) | 293 (57.6%) | 315 (47.7%) | 453 (60.6%) | 105 (23.9%) | 1612 (43.3%) |
| high | 437 (56.5%) | 428 (71.9%) | 181 (35.6%) | 308 (46.6%) | 144 (19.3%) | 327 (74.5%) | 1825 (49.0%) |
| Missing | 44 (5.7%) | 4 (0.7%) | 3 (0.6%) | 24 (3.6%) | 33 (4.4%) | 7 (1.6%) | 115 (3.1%) |
| Household income | |||||||
| low | 210 (27.2%) | 71 (11.9%) | 85 (16.7%) | 93 (14.1%) | 311 (41.6%) | 10 (2.3%) | 780 (20.9%) |
| low/medium | 75 (9.7%) | 23 (3.9%) | 54 (10.6%) | 46 (7.0%) | 112 (15.0%) | 12 (2.7%) | 322 (8.6%) |
| medium | 287 (37.1%) | 148 (24.9%) | 233 (45.8%) | 240 (36.3%) | 160 (21.4%) | 146 (33.3%) | 1214 (32.6%) |
| medium/high | 80 (10.3%) | 57 (9.6%) | 59 (11.6%) | 92 (13.9%) | 11 (1.5%) | 119 (27.1%) | 418 (11.2%) |
| high | 97 (12.5%) | 284 (47.7%) | 48 (9.4%) | 149 (22.5%) | 59 (7.9%) | 146 (33.3%) | 783 (21.0%) |
| Missing | 24 (3.1%) | 12 (2.0%) | 30 (5.9%) | 41 (6.2%) | 95 (12.7%) | 6 (1.4%) | 208 (5.6%) |
| Migrant background | |||||||
| Yes | 152 (19.7%) | 20 (3.4%) | 100 (19.6%) | 21 (3.2%) | 132 (17.6%) | 69 (15.7%) | 494 (13.3%) |
| No | 621 (80.3%) | 575 (96.6%) | 409 (80.4%) | 640 (96.8%) | 616 (82.4%) | 370 (84.3%) | 3231 (86.7%) |
| Unemployment in household | |||||||
| Yes | 116 (15.0%) | 22 (3.7%) | 38 (7.5%) | 46 (7.0%) | 104 (13.9%) | 10 (2.3%) | 336 (9.0%) |
| No | 657 (85.0%) | 573 (96.3%) | 471 (92.5%) | 615 (93.0%) | 644 (86.1%) | 429 (97.7%) | 3389 (91.0%) |
| Single parenthood | |||||||
| Yes | 52 (6.7%) | 70 (11.8%) | 77 (15.1%) | 96 (14.5%) | 27 (3.6%) | 58 (13.2%) | 380 (10.2%) |
| No | 721 (93.3%) | 525 (88.2%) | 431 (84.7%) | 565 (85.5%) | 721 (96.4%) | 381 (86.8%) | 3344 (89.8%) |
| Missing | 0 (0%) | 0 (0%) | 1 (0.2%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.0%) |
| BMI (kg/m^2) | |||||||
| Mean (SD) | 26.2 (5.11) | 25.4 (5.37) | 26.7 (5.68) | 26.1 (5.57) | 27.4 (5.38) | 24.7 (3.90) | 26.2 (5.31) |
| Median [Min, Max] | 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] | 23.9 [17.1, 50.3] | 25.1 [15.5, 55.6] |
| Age (years) | |||||||
| Mean (SD) | 41.5 (5.91) | 39.6 (5.34) | 42.9 (5.87) | 40.8 (5.12) | 42.7 (5.53) | 43.8 (5.26) | 41.8 (5.68) |
| Median [Min, Max] | 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] | 43.7 [30.6, 65.4] | 41.5 [24.0, 75.9] |
| Sex | |||||||
| Male | 168 (21.7%) | 56 (9.4%) | 60 (11.8%) | 80 (12.1%) | 92 (12.3%) | 94 (21.4%) | 550 (14.8%) |
| Female | 605 (78.3%) | 539 (90.6%) | 449 (88.2%) | 581 (87.9%) | 656 (87.7%) | 345 (78.6%) | 3175 (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, excluding Belgium and Spain"
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)| Cyprus (N=773) |
Estonia (N=595) |
Germany (N=509) |
Hungary (N=661) |
Italy (N=748) |
Sweden (N=439) |
Overall (N=3725) |
|
|---|---|---|---|---|---|---|---|
| Comparing food labels | |||||||
| Disagree | 58 (7.5%) | 53 (8.9%) | 40 (7.9%) | 107 (16.2%) | 66 (8.8%) | 61 (13.9%) | 385 (10.3%) |
| Moderately disagree | 74 (9.6%) | 85 (14.3%) | 147 (28.9%) | 138 (20.9%) | 58 (7.8%) | 87 (19.8%) | 589 (15.8%) |
| Unsure | 96 (12.4%) | 100 (16.8%) | 65 (12.8%) | 106 (16.0%) | 99 (13.2%) | 24 (5.5%) | 490 (13.2%) |
| Moderately agree | 292 (37.8%) | 253 (42.5%) | 203 (39.9%) | 195 (29.5%) | 291 (38.9%) | 201 (45.8%) | 1435 (38.5%) |
| Agree | 253 (32.7%) | 104 (17.5%) | 54 (10.6%) | 115 (17.4%) | 234 (31.3%) | 66 (15.0%) | 826 (22.2%) |
| Trusting food advertisements | |||||||
| Disagree | 336 (43.5%) | 294 (49.4%) | 230 (45.2%) | 358 (54.2%) | 260 (34.8%) | 303 (69.0%) | 1781 (47.8%) |
| Moderately disagree | 189 (24.5%) | 228 (38.3%) | 211 (41.5%) | 202 (30.6%) | 169 (22.6%) | 101 (23.0%) | 1100 (29.5%) |
| Unsure | 88 (11.4%) | 52 (8.7%) | 39 (7.7%) | 58 (8.8%) | 112 (15.0%) | 26 (5.9%) | 375 (10.1%) |
| Moderately agree | 121 (15.7%) | 20 (3.4%) | 25 (4.9%) | 34 (5.1%) | 151 (20.2%) | 9 (2.1%) | 360 (9.7%) |
| Agree | 39 (5.0%) | 1 (0.2%) | 4 (0.8%) | 9 (1.4%) | 56 (7.5%) | 0 (0%) | 109 (2.9%) |
| Avoiding food additives | |||||||
| Disagree | 22 (2.8%) | 17 (2.9%) | 27 (5.3%) | 25 (3.8%) | 20 (2.7%) | 31 (7.1%) | 142 (3.8%) |
| Moderately disagree | 38 (4.9%) | 35 (5.9%) | 97 (19.1%) | 42 (6.4%) | 33 (4.4%) | 78 (17.8%) | 323 (8.7%) |
| Unsure | 69 (8.9%) | 79 (13.3%) | 73 (14.3%) | 75 (11.3%) | 108 (14.4%) | 44 (10.0%) | 448 (12.0%) |
| Moderately agree | 212 (27.4%) | 260 (43.7%) | 227 (44.6%) | 218 (33.0%) | 191 (25.5%) | 189 (43.1%) | 1297 (34.8%) |
| Agree | 432 (55.9%) | 204 (34.3%) | 85 (16.7%) | 301 (45.5%) | 396 (52.9%) | 97 (22.1%) | 1515 (40.7%) |
| Valuing ecological products | |||||||
| Disagree | 25 (3.2%) | 60 (10.1%) | 94 (18.5%) | 46 (7.0%) | 32 (4.3%) | 44 (10.0%) | 301 (8.1%) |
| Moderately disagree | 39 (5.0%) | 136 (22.9%) | 194 (38.1%) | 67 (10.1%) | 58 (7.8%) | 109 (24.8%) | 603 (16.2%) |
| Unsure | 80 (10.3%) | 149 (25.0%) | 69 (13.6%) | 84 (12.7%) | 78 (10.4%) | 43 (9.8%) | 503 (13.5%) |
| Moderately agree | 309 (40.0%) | 183 (30.8%) | 136 (26.7%) | 249 (37.7%) | 288 (38.5%) | 192 (43.7%) | 1357 (36.4%) |
| Agree | 320 (41.4%) | 67 (11.3%) | 16 (3.1%) | 215 (32.5%) | 292 (39.0%) | 51 (11.6%) | 961 (25.8%) |
| Preferring fresh meat and vegetables | |||||||
| Disagree | 13 (1.7%) | 10 (1.7%) | 22 (4.3%) | 4 (0.6%) | 11 (1.5%) | 19 (4.3%) | 79 (2.1%) |
| Moderately disagree | 15 (1.9%) | 27 (4.5%) | 86 (16.9%) | 20 (3.0%) | 25 (3.3%) | 51 (11.6%) | 224 (6.0%) |
| Unsure | 16 (2.1%) | 39 (6.6%) | 41 (8.1%) | 13 (2.0%) | 10 (1.3%) | 24 (5.5%) | 143 (3.8%) |
| Moderately agree | 100 (12.9%) | 261 (43.9%) | 237 (46.6%) | 165 (25.0%) | 170 (22.7%) | 152 (34.6%) | 1085 (29.1%) |
| Agree | 629 (81.4%) | 258 (43.4%) | 123 (24.2%) | 459 (69.4%) | 532 (71.1%) | 193 (44.0%) | 2194 (58.9%) |
| Frequently using ready-to-eat foods | |||||||
| Disagree | 341 (44.1%) | 182 (30.6%) | 123 (24.2%) | 230 (34.8%) | 587 (78.5%) | 125 (28.5%) | 1588 (42.6%) |
| Moderately disagree | 237 (30.7%) | 311 (52.3%) | 289 (56.8%) | 309 (46.7%) | 131 (17.5%) | 169 (38.5%) | 1446 (38.8%) |
| Unsure | 41 (5.3%) | 56 (9.4%) | 45 (8.8%) | 53 (8.0%) | 9 (1.2%) | 23 (5.2%) | 227 (6.1%) |
| Moderately agree | 117 (15.1%) | 44 (7.4%) | 49 (9.6%) | 51 (7.7%) | 12 (1.6%) | 98 (22.3%) | 371 (10.0%) |
| Agree | 37 (4.8%) | 2 (0.3%) | 3 (0.6%) | 18 (2.7%) | 9 (1.2%) | 24 (5.5%) | 93 (2.5%) |
| Frequently using pre-made mixes | |||||||
| Disagree | 505 (65.3%) | 290 (48.7%) | 164 (32.2%) | 405 (61.3%) | 629 (84.1%) | 322 (73.3%) | 2315 (62.1%) |
| Moderately disagree | 153 (19.8%) | 223 (37.5%) | 261 (51.3%) | 199 (30.1%) | 91 (12.2%) | 87 (19.8%) | 1014 (27.2%) |
| Unsure | 34 (4.4%) | 43 (7.2%) | 33 (6.5%) | 28 (4.2%) | 5 (0.7%) | 9 (2.1%) | 152 (4.1%) |
| Moderately agree | 59 (7.6%) | 36 (6.1%) | 47 (9.2%) | 23 (3.5%) | 20 (2.7%) | 17 (3.9%) | 202 (5.4%) |
| Agree | 22 (2.8%) | 3 (0.5%) | 4 (0.8%) | 6 (0.9%) | 3 (0.4%) | 4 (0.9%) | 42 (1.1%) |
| Having children help in the kitchen | |||||||
| Disagree | 100 (12.9%) | 56 (9.4%) | 25 (4.9%) | 50 (7.6%) | 236 (31.6%) | 32 (7.3%) | 499 (13.4%) |
| Moderately disagree | 105 (13.6%) | 164 (27.6%) | 147 (28.9%) | 91 (13.8%) | 76 (10.2%) | 112 (25.5%) | 695 (18.7%) |
| Unsure | 61 (7.9%) | 86 (14.5%) | 33 (6.5%) | 46 (7.0%) | 36 (4.8%) | 28 (6.4%) | 290 (7.8%) |
| Moderately agree | 279 (36.1%) | 229 (38.5%) | 217 (42.6%) | 246 (37.2%) | 260 (34.8%) | 196 (44.6%) | 1427 (38.3%) |
| Agree | 228 (29.5%) | 60 (10.1%) | 87 (17.1%) | 228 (34.5%) | 140 (18.7%) | 71 (16.2%) | 814 (21.9%) |
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
## 1.412392 1.263649 1.020964 1.054861
## unemploy
## 1.080933We 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 0.120259 0.052574 2.2874
## age_T3 0.009579 0.005519 1.7357
## sex_T3 0.057953 0.088249 0.6567
## bmi_T3 -0.011225 0.005759 -1.9489
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.6686 0.3817 -4.3720
## 2|3 -0.5315 0.3796 -1.4002
## 3|4 0.0759 0.3793 0.2001
## 4|5 1.7807 0.3807 4.6779
##
## Residual Deviance: 10770.13
## AIC: 10786.13
## (115 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 0.120258749 0.052573761 2.2874291 0.0222
## age_T3 0.009579035 0.005518926 1.7356702 0.0826
## sex_T3 0.057952655 0.088248615 0.6566976 0.5114
## bmi_T3 -0.011224508 0.005759457 -1.9488831 0.0513
## 1|2 -1.668597646 0.381651188 -4.3720489 0.0000
## 2|3 -0.531536419 0.379614842 -1.4001993 0.1615
## 3|4 0.075910267 0.379298690 0.2001332 0.8414
## 4|5 1.780740129 0.380673187 4.6778712 0.0000(ci_edu1 <- confint(edu1, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## isced_cat2011_T3 -0.023449158 0.264122418
## age_T3 -0.005512975 0.024673652
## sex_T3 -0.183636041 0.299127006
## bmi_T3 -0.026969021 0.004539792exp(cbind(OR = coef(edu1), ci_edu1))## OR 0.3 % 99.7 %
## isced_cat2011_T3 1.1277886 0.9768236 1.302288
## age_T3 1.0096251 0.9945022 1.024981
## sex_T3 1.0596648 0.8322386 1.348681
## bmi_T3 0.9888383 0.9733914 1.004550edu2 <- 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 -0.3708374 0.054976 -6.74549
## age_T3 0.0001831 0.005716 0.03202
## sex_T3 -0.0644322 0.091721 -0.70248
## bmi_T3 0.0100237 0.006015 1.66651
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.8667 0.3911 -2.2158
## 2|3 0.4817 0.3909 1.2322
## 3|4 1.2164 0.3918 3.1044
## 4|5 2.7997 0.4010 6.9816
##
## Residual Deviance: 9123.511
## AIC: 9139.511
## (115 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 -0.3708373753 0.054975631 -6.74548642 0.0000
## age_T3 0.0001830579 0.005716401 0.03202329 0.9745
## sex_T3 -0.0644322374 0.091720942 -0.70248120 0.4824
## bmi_T3 0.0100237244 0.006014792 1.66651235 0.0956
## 1|2 -0.8666555914 0.391122556 -2.21581594 0.0267
## 2|3 0.4817050842 0.390939471 1.23217306 0.2179
## 3|4 1.2164051552 0.391829629 3.10442362 0.0019
## 4|5 2.7997129248 0.401011838 6.98162163 0.0000(ci_edu2 <- confint(edu2, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## isced_cat2011_T3 -0.521276853 -0.22055802
## age_T3 -0.015461876 0.01580728
## sex_T3 -0.314340620 0.18753239
## bmi_T3 -0.006457386 0.02645140exp(cbind(OR = coef(edu2), ci_edu2))## OR 0.3 % 99.7 %
## isced_cat2011_T3 0.6901562 0.5937619 0.8020711
## age_T3 1.0001831 0.9846570 1.0159329
## sex_T3 0.9375996 0.7302702 1.2062693
## bmi_T3 1.0100741 0.9935634 1.0268043edu3 <- 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 0.13057 0.054203 2.409
## age_T3 0.02183 0.005591 3.904
## sex_T3 0.30909 0.089585 3.450
## bmi_T3 -0.01349 0.005892 -2.290
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.7886 0.3916 -4.5679
## 2|3 -0.5085 0.3857 -1.3184
## 3|4 0.3210 0.3850 0.8338
## 4|5 1.8494 0.3863 4.7880
##
## Residual Deviance: 9527.228
## AIC: 9543.228
## (115 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 0.13057391 0.054203182 2.4089712 0.0160
## age_T3 0.02182955 0.005591238 3.9042428 0.0001
## sex_T3 0.30908699 0.089584593 3.4502249 0.0006
## bmi_T3 -0.01349002 0.005892001 -2.2895478 0.0220
## 1|2 -1.78864415 0.391566786 -4.5679159 0.0000
## 2|3 -0.50847029 0.385670042 -1.3184075 0.1874
## 3|4 0.32104928 0.385025156 0.8338398 0.4044
## 4|5 1.84944104 0.386268785 4.7879640 0.0000(ci_edu3 <- confint(edu3, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## isced_cat2011_T3 -0.017679403 0.278812780
## age_T3 0.006567108 0.037150189
## sex_T3 0.063756288 0.553867207
## bmi_T3 -0.029579659 0.002654445exp(cbind(OR = coef(edu3), ci_edu3))## OR 0.3 % 99.7 %
## isced_cat2011_T3 1.1394822 0.9824760 1.321560
## age_T3 1.0220696 1.0065887 1.037849
## sex_T3 1.3621809 1.0658326 1.739969
## bmi_T3 0.9866006 0.9708535 1.002658edu4 <- 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 0.042842 0.053429 0.8018
## age_T3 0.025158 0.005552 4.5315
## sex_T3 0.058070 0.088485 0.6563
## bmi_T3 -0.006029 0.005786 -1.0420
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.3253 0.3839 -3.4519
## 2|3 -0.0226 0.3818 -0.0592
## 3|4 0.6222 0.3818 1.6296
## 4|5 2.1932 0.3835 5.7193
##
## Residual Deviance: 10722.18
## AIC: 10738.18
## (115 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 0.042841761 0.053428787 0.80184791 0.4226
## age_T3 0.025157729 0.005551796 4.53145792 0.0000
## sex_T3 0.058069535 0.088485132 0.65626319 0.5117
## bmi_T3 -0.006029155 0.005786238 -1.04198177 0.2974
## 1|2 -1.325306718 0.383933536 -3.45191705 0.0006
## 2|3 -0.022613739 0.381774348 -0.05923326 0.9528
## 3|4 0.622233057 0.381840191 1.62956407 0.1032
## 4|5 2.193184141 0.383467777 5.71934404 0.0000(ci_edu4 <- confint(edu4, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## isced_cat2011_T3 -0.103242669 0.189002555
## age_T3 0.009992149 0.040359298
## sex_T3 -0.184178594 0.299887914
## bmi_T3 -0.021842968 0.009811726exp(cbind(OR = coef(edu4), ci_edu4))## OR 0.3 % 99.7 %
## isced_cat2011_T3 1.043773 0.9019081 1.208044
## age_T3 1.025477 1.0100422 1.041185
## sex_T3 1.059789 0.8317872 1.349708
## bmi_T3 0.993989 0.9783939 1.009860edu5 <- 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 -0.137315 0.058058 -2.3651
## age_T3 0.005336 0.006048 0.8824
## sex_T3 0.073003 0.096771 0.7544
## bmi_T3 0.003560 0.006363 0.5595
##
## Intercepts:
## Value Std. Error t value
## 1|2 -3.7034 0.4292 -8.6287
## 2|3 -2.2991 0.4181 -5.4991
## 3|4 -1.8758 0.4170 -4.4988
## 4|5 -0.2285 0.4154 -0.5500
##
## Residual Deviance: 7588.362
## AIC: 7604.362
## (115 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 -0.137315018 0.058058306 -2.3651227 0.0180
## age_T3 0.005336195 0.006047701 0.8823510 0.3776
## sex_T3 0.073002501 0.096771248 0.7543821 0.4506
## bmi_T3 0.003560140 0.006362737 0.5595297 0.5758
## 1|2 -3.703413169 0.429198140 -8.6286795 0.0000
## 2|3 -2.299147700 0.418094111 -5.4991153 0.0000
## 3|4 -1.875789907 0.416957372 -4.4987570 0.0000
## 4|5 -0.228492221 0.415449648 -0.5499878 0.5823(ci_edu5 <- confint(edu5, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## isced_cat2011_T3 -0.29655696 0.02106225
## age_T3 -0.01116872 0.02191590
## sex_T3 -0.19366757 0.33593132
## bmi_T3 -0.01374106 0.02107930exp(cbind(OR = coef(edu5), ci_edu5))## OR 0.3 % 99.7 %
## isced_cat2011_T3 0.8716956 0.7433733 1.021286
## age_T3 1.0053505 0.9888934 1.022158
## sex_T3 1.0757332 0.8239318 1.399243
## bmi_T3 1.0035665 0.9863529 1.021303edu6 <- 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 0.484022 0.056265 8.6026
## age_T3 -0.030104 0.005775 -5.2130
## sex_T3 -0.323526 0.091277 -3.5444
## bmi_T3 -0.004284 0.006073 -0.7054
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.0892 0.3966 -2.7463
## 2|3 0.7466 0.3964 1.8833
## 3|4 1.2156 0.3971 3.0610
## 4|5 2.9275 0.4076 7.1814
##
## Residual Deviance: 8713.257
## AIC: 8729.257
## (115 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 0.484021633 0.056264633 8.6025912 0.0000
## age_T3 -0.030103550 0.005774698 -5.2130087 0.0000
## sex_T3 -0.323525631 0.091277088 -3.5444342 0.0004
## bmi_T3 -0.004284136 0.006073209 -0.7054155 0.4806
## 1|2 -1.089234878 0.396625154 -2.7462577 0.0060
## 2|3 0.746603500 0.396427191 1.8833307 0.0597
## 3|4 1.215638548 0.397131675 3.0610466 0.0022
## 4|5 2.927472826 0.407646562 7.1813995 0.0000(ci_edu6 <- confint(edu6, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## isced_cat2011_T3 0.33070479 0.63849107
## age_T3 -0.04594495 -0.01435716
## sex_T3 -0.57292998 -0.07356158
## bmi_T3 -0.02094860 0.01227982exp(cbind(OR = coef(edu6), ci_edu6))## OR 0.3 % 99.7 %
## isced_cat2011_T3 1.6225867 1.3919488 1.8936214
## age_T3 0.9703450 0.9550945 0.9857454
## sex_T3 0.7235934 0.5638709 0.9290789
## bmi_T3 0.9957250 0.9792693 1.0123555edu7 <- 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 0.13459 0.059453 2.264
## age_T3 -0.02415 0.006200 -3.895
## sex_T3 -0.34425 0.096100 -3.582
## bmi_T3 0.01371 0.006459 2.122
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.4741 0.4220 -1.1233
## 2|3 1.1833 0.4232 2.7964
## 3|4 1.7151 0.4249 4.0366
## 4|5 3.5774 0.4492 7.9642
##
## Residual Deviance: 7129.302
## AIC: 7145.302
## (115 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 0.13458917 0.059453079 2.263788 0.0236
## age_T3 -0.02415071 0.006199767 -3.895423 0.0001
## sex_T3 -0.34424894 0.096100143 -3.582190 0.0003
## bmi_T3 0.01370835 0.006459202 2.122298 0.0338
## 1|2 -0.47405092 0.422008807 -1.123320 0.2613
## 2|3 1.18331282 0.423152335 2.796423 0.0052
## 3|4 1.71514788 0.424899252 4.036599 0.0001
## 4|5 3.57736923 0.449179281 7.964235 0.0000(ci_edu7 <- confint(edu7, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## isced_cat2011_T3 -0.027462617 0.297794251
## age_T3 -0.041179131 -0.007261695
## sex_T3 -0.605738681 -0.079828953
## bmi_T3 -0.004057472 0.031288064exp(cbind(OR = coef(edu7), ci_edu7))## OR 0.3 % 99.7 %
## isced_cat2011_T3 1.1440667 0.9729111 1.3468846
## age_T3 0.9761386 0.9596572 0.9927646
## sex_T3 0.7087525 0.5456712 0.9232743
## bmi_T3 1.0138027 0.9959507 1.0317827edu8 <- 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 -0.029869 0.053379 -0.55957
## age_T3 0.000124 0.005504 0.02254
## sex_T3 0.360188 0.088231 4.08231
## bmi_T3 0.012314 0.005758 2.13876
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.9468 0.3808 -2.4864
## 2|3 0.1772 0.3800 0.4664
## 3|4 0.5197 0.3800 1.3677
## 4|5 2.2254 0.3817 5.8309
##
## Residual Deviance: 10677.23
## AIC: 10693.23
## (115 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 -0.0298693777 0.053379088 -0.55957078 0.5758
## age_T3 0.0001240483 0.005503825 0.02253855 0.9820
## sex_T3 0.3601880741 0.088231384 4.08231243 0.0000
## bmi_T3 0.0123142500 0.005757662 2.13875887 0.0325
## 1|2 -0.9467850166 0.380778186 -2.48644762 0.0129
## 2|3 0.1772316166 0.379976189 0.46642822 0.6409
## 3|4 0.5197375861 0.380019573 1.36766004 0.1714
## 4|5 2.2254389036 0.381662936 5.83090128 0.0000(ci_edu8 <- confint(edu8, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## isced_cat2011_T3 -0.175912893 0.11605763
## age_T3 -0.014928988 0.01517625
## sex_T3 0.118829154 0.60150985
## bmi_T3 -0.003409189 0.02808833exp(cbind(OR = coef(edu8), ci_edu8))## OR 0.3 % 99.7 %
## isced_cat2011_T3 0.9705723 0.8386910 1.123061
## age_T3 1.0001241 0.9851819 1.015292
## sex_T3 1.4335990 1.1261775 1.824872
## bmi_T3 1.0123904 0.9965966 1.028487# 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 -0.012083 0.021957 -0.5503
## age_T3 0.007974 0.005546 1.4379
## sex_T3 0.016510 0.088002 0.1876
## bmi_T3 -0.013042 0.005838 -2.2340
##
## Intercepts:
## Value Std. Error t value
## 1|2 -2.1795 0.3658 -5.9587
## 2|3 -1.0566 0.3634 -2.9072
## 3|4 -0.4481 0.3630 -1.2345
## 4|5 1.2718 0.3637 3.4970
##
## Residual Deviance: 10490.96
## AIC: 10506.96
## (208 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 -0.012083070 0.021956761 -0.5503121 0.5821
## age_T3 0.007973897 0.005545569 1.4378863 0.1505
## sex_T3 0.016510070 0.088002279 0.1876096 0.8512
## bmi_T3 -0.013042262 0.005838023 -2.2340205 0.0255
## 1|2 -2.179529003 0.365771389 -5.9587192 0.0000
## 2|3 -1.056589022 0.363442053 -2.9071733 0.0036
## 3|4 -0.448074071 0.362956549 -1.2345116 0.2170
## 4|5 1.271794585 0.363683134 3.4969853 0.0005(ci_inc1 <- confint(inc1, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## income_cat_T3 -0.072146059 0.047950839
## age_T3 -0.007190767 0.023141822
## sex_T3 -0.224428797 0.256981135
## bmi_T3 -0.029003247 0.002935149exp(cbind(OR = coef(inc1), ci_inc1))## OR 0.3 % 99.7 %
## income_cat_T3 0.9879896 0.9303950 1.049119
## age_T3 1.0080058 0.9928350 1.023412
## sex_T3 1.0166471 0.7989725 1.293021
## bmi_T3 0.9870424 0.9714133 1.002939inc2 <- 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 -0.144289 0.022920 -6.2955
## age_T3 -0.006046 0.005756 -1.0506
## sex_T3 -0.073190 0.091548 -0.7995
## bmi_T3 0.012897 0.006104 2.1128
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.5599 0.3750 -1.4932
## 2|3 0.7702 0.3751 2.0535
## 3|4 1.4820 0.3761 3.9406
## 4|5 3.0864 0.3863 7.9897
##
## Residual Deviance: 8838.307
## AIC: 8854.307
## (208 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 -0.144289167 0.022919532 -6.2954675 0.0000
## age_T3 -0.006046471 0.005755510 -1.0505535 0.2935
## sex_T3 -0.073190401 0.091547907 -0.7994765 0.4240
## bmi_T3 0.012897264 0.006104309 2.1128129 0.0346
## 1|2 -0.559925571 0.374985434 -1.4931929 0.1354
## 2|3 0.770203310 0.375064482 2.0535224 0.0400
## 3|4 1.482032372 0.376094206 3.9405882 0.0001
## 4|5 3.086383565 0.386294276 7.9897212 0.0000(ci_inc2 <- confint(inc2, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## income_cat_T3 -0.207083583 -0.081712376
## age_T3 -0.021806121 0.009677565
## sex_T3 -0.322628498 0.178295041
## bmi_T3 -0.003830274 0.029570386exp(cbind(OR = coef(inc2), ci_inc2))## OR 0.3 % 99.7 %
## income_cat_T3 0.8656374 0.8129517 0.921537
## age_T3 0.9939718 0.9784299 1.009725
## sex_T3 0.9294239 0.7242429 1.195178
## bmi_T3 1.0129808 0.9961771 1.030012inc3 <- 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 -0.03247 0.022519 -1.442
## age_T3 0.02230 0.005645 3.951
## sex_T3 0.27552 0.089383 3.082
## bmi_T3 -0.01905 0.006002 -3.174
##
## Intercepts:
## Value Std. Error t value
## 1|2 -2.4079 0.3763 -6.3985
## 2|3 -1.1262 0.3697 -3.0463
## 3|4 -0.3010 0.3688 -0.8162
## 4|5 1.2315 0.3694 3.3336
##
## Residual Deviance: 9263.745
## AIC: 9279.745
## (208 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 -0.03247337 0.022518654 -1.4420655 0.1493
## age_T3 0.02230213 0.005644972 3.9507949 0.0001
## sex_T3 0.27551715 0.089382809 3.0824401 0.0021
## bmi_T3 -0.01905061 0.006001785 -3.1741569 0.0015
## 1|2 -2.40788331 0.376321908 -6.3984670 0.0000
## 2|3 -1.12616075 0.369686946 -3.0462551 0.0023
## 3|4 -0.30099783 0.368774230 -0.8162117 0.4144
## 4|5 1.23145788 0.369405821 3.3336180 0.0009(ci_inc3 <- confint(inc3, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## income_cat_T3 -0.094104475 0.029070873
## age_T3 0.006893104 0.037770534
## sex_T3 0.030718942 0.519723986
## bmi_T3 -0.035447285 -0.002612261exp(cbind(OR = coef(inc3), ci_inc3))## OR 0.3 % 99.7 %
## income_cat_T3 0.9680482 0.9101877 1.0294976
## age_T3 1.0225527 1.0069169 1.0384929
## sex_T3 1.3172117 1.0311956 1.6815635
## bmi_T3 0.9811297 0.9651736 0.9973911inc4 <- 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 -0.101588 0.021990 -4.61971
## age_T3 0.026449 0.005585 4.73574
## sex_T3 -0.007861 0.088319 -0.08901
## bmi_T3 -0.012832 0.005892 -2.17807
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.9937 0.3678 -5.4213
## 2|3 -0.6968 0.3649 -1.9093
## 3|4 -0.0458 0.3647 -0.1256
## 4|5 1.5318 0.3656 4.1892
##
## Residual Deviance: 10426.75
## AIC: 10442.75
## (208 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 -0.101587563 0.021990019 -4.61971240 0.0000
## age_T3 0.026449360 0.005585050 4.73574290 0.0000
## sex_T3 -0.007860828 0.088318656 -0.08900529 0.9291
## bmi_T3 -0.012832453 0.005891657 -2.17807183 0.0294
## 1|2 -1.993721713 0.367753925 -5.42134720 0.0000
## 2|3 -0.696775395 0.364939992 -1.90928758 0.0562
## 3|4 -0.045817688 0.364669703 -0.12564161 0.9000
## 4|5 1.531782270 0.365649919 4.18920446 0.0000(ci_inc4 <- confint(inc4, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## income_cat_T3 -0.16179493 -0.041515705
## age_T3 0.01119711 0.041746513
## sex_T3 -0.24968543 0.233468226
## bmi_T3 -0.02893839 0.003293283exp(cbind(OR = coef(inc4), ci_inc4))## OR 0.3 % 99.7 %
## income_cat_T3 0.9034021 0.8506156 0.9593343
## age_T3 1.0268022 1.0112600 1.0426302
## sex_T3 0.9921700 0.7790458 1.2629727
## bmi_T3 0.9872495 0.9714763 1.0032987inc5 <- 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 -0.100654 0.024016 -4.1912
## age_T3 0.001421 0.006100 0.2330
## sex_T3 0.028990 0.096532 0.3003
## bmi_T3 0.002593 0.006467 0.4009
##
## Intercepts:
## Value Std. Error t value
## 1|2 -3.9473 0.4134 -9.5478
## 2|3 -2.5401 0.4015 -6.3270
## 3|4 -2.1108 0.4002 -5.2737
## 4|5 -0.4682 0.3985 -1.1751
##
## Residual Deviance: 7394.743
## AIC: 7410.743
## (208 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 -0.100654373 0.024015509 -4.1912238 0.0000
## age_T3 0.001421442 0.006100199 0.2330157 0.8157
## sex_T3 0.028989546 0.096531556 0.3003116 0.7639
## bmi_T3 0.002592536 0.006467351 0.4008652 0.6885
## 1|2 -3.947271217 0.413423266 -9.5477723 0.0000
## 2|3 -2.540146480 0.401474381 -6.3270450 0.0000
## 3|4 -2.110752110 0.400241491 -5.2736964 0.0000
## 4|5 -0.468245191 0.398462399 -1.1751302 0.2399(ci_inc5 <- confint(inc5, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## income_cat_T3 -0.16647361 -0.03509944
## age_T3 -0.01524000 0.01813133
## sex_T3 -0.23706078 0.29121611
## bmi_T3 -0.01499495 0.02039925exp(cbind(OR = coef(inc5), ci_inc5))## OR 0.3 % 99.7 %
## income_cat_T3 0.9042455 0.8466452 0.9655094
## age_T3 1.0014225 0.9848755 1.0182967
## sex_T3 1.0294138 0.7889433 1.3380537
## bmi_T3 1.0025959 0.9851169 1.0206087inc6 <- 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 0.170066 0.023002 7.3935
## age_T3 -0.023367 0.005777 -4.0450
## sex_T3 -0.331224 0.090488 -3.6604
## bmi_T3 -0.005518 0.006144 -0.8981
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.5531 0.3779 -4.1099
## 2|3 0.2666 0.3770 0.7071
## 3|4 0.7439 0.3776 1.9698
## 4|5 2.4761 0.3888 6.3681
##
## Residual Deviance: 8580.854
## AIC: 8596.854
## (208 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 0.170066128 0.023002260 7.3934530 0.0000
## age_T3 -0.023366656 0.005776639 -4.0450264 0.0001
## sex_T3 -0.331224384 0.090487570 -3.6604407 0.0003
## bmi_T3 -0.005517753 0.006144074 -0.8980609 0.3692
## 1|2 -1.553138712 0.377900729 -4.1099119 0.0000
## 2|3 0.266569391 0.377010755 0.7070604 0.4795
## 3|4 0.743862068 0.377637178 1.9697798 0.0489
## 4|5 2.476092904 0.388830282 6.3680557 0.0000(ci_inc6 <- confint(inc6, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## income_cat_T3 0.10730403 0.233126006
## age_T3 -0.03920266 -0.007604275
## sex_T3 -0.57846281 -0.083418241
## bmi_T3 -0.02237209 0.011244504exp(cbind(OR = coef(inc6), ci_inc6))## OR 0.3 % 99.7 %
## income_cat_T3 1.1853832 1.1132727 1.2625406
## age_T3 0.9769042 0.9615558 0.9924246
## sex_T3 0.7180440 0.5607597 0.9199663
## bmi_T3 0.9944974 0.9778763 1.0113080inc7 <- 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 0.08652 0.024725 3.499
## age_T3 -0.02153 0.006235 -3.452
## sex_T3 -0.30587 0.095801 -3.193
## bmi_T3 0.01639 0.006541 2.506
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.2855 0.4045 -0.7057
## 2|3 1.3543 0.4058 3.3373
## 3|4 1.8892 0.4077 4.6338
## 4|5 3.7390 0.4330 8.6347
##
## Residual Deviance: 6961.806
## AIC: 6977.806
## (208 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 0.08651568 0.024724966 3.4991223 0.0005
## age_T3 -0.02152608 0.006235175 -3.4523619 0.0006
## sex_T3 -0.30587229 0.095800910 -3.1927911 0.0014
## bmi_T3 0.01639281 0.006541040 2.5061466 0.0122
## 1|2 -0.28548891 0.404524431 -0.7057396 0.4804
## 2|3 1.35434761 0.405825535 3.3372656 0.0008
## 3|4 1.88923741 0.407710060 4.6337768 0.0000
## 4|5 3.73904655 0.433023246 8.6347479 0.0000(ci_inc7 <- confint(inc7, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## income_cat_T3 0.019019589 0.154278734
## age_T3 -0.038647622 -0.004535784
## sex_T3 -0.566517319 -0.042252762
## bmi_T3 -0.001596579 0.034197819exp(cbind(OR = coef(inc7), ci_inc7))## OR 0.3 % 99.7 %
## income_cat_T3 1.0903685 1.0192016 1.1668161
## age_T3 0.9787040 0.9620897 0.9954745
## sex_T3 0.7364807 0.5674984 0.9586274
## bmi_T3 1.0165279 0.9984047 1.0347893inc8 <- 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 -0.05702 0.022136 -2.5761
## age_T3 -0.00140 0.005532 -0.2532
## sex_T3 0.32031 0.088115 3.6351
## bmi_T3 0.00983 0.005839 1.6834
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.2936 0.3644 -3.5503
## 2|3 -0.1456 0.3631 -0.4010
## 3|4 0.2014 0.3631 0.5546
## 4|5 1.9153 0.3646 5.2525
##
## Residual Deviance: 10381.71
## AIC: 10397.71
## (208 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 -0.057024998 0.022136041 -2.5761154 0.0100
## age_T3 -0.001400393 0.005531730 -0.2531564 0.8001
## sex_T3 0.320309233 0.088115312 3.6351143 0.0003
## bmi_T3 0.009830070 0.005839466 1.6833851 0.0923
## 1|2 -1.293569729 0.364352545 -3.5503244 0.0004
## 2|3 -0.145611720 0.363094945 -0.4010293 0.6884
## 3|4 0.201369011 0.363092633 0.5545940 0.5792
## 4|5 1.915309823 0.364648343 5.2524846 0.0000(ci_inc8 <- confint(inc8, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## income_cat_T3 -0.117596672 0.003480945
## age_T3 -0.016524339 0.013734062
## sex_T3 0.079261689 0.561302986
## bmi_T3 -0.006124005 0.025822014exp(cbind(OR = coef(inc8), ci_inc8))## OR 0.3 % 99.7 %
## income_cat_T3 0.9445705 0.8890546 1.003487
## age_T3 0.9986006 0.9836114 1.013829
## sex_T3 1.3775537 1.0824876 1.752955
## bmi_T3 1.0098785 0.9938947 1.026158# 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 0.14225 0.087590 1.6241
## age_T3 0.01067 0.005382 1.9816
## sex_T3 0.05783 0.086063 0.6719
## bmi_T3 -0.01279 0.005578 -2.2930
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.7860 0.3575 -4.9954
## 2|3 -0.6633 0.3554 -1.8660
## 3|4 -0.0585 0.3551 -0.1649
## 4|5 1.6364 0.3562 4.5934
##
## Residual Deviance: 11122.62
## AIC: 11138.62ctable_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 0.14225437 0.087589535 1.6241024 0.1044
## age_T3 0.01066567 0.005382312 1.9816150 0.0475
## sex_T3 0.05782506 0.086062613 0.6718952 0.5017
## bmi_T3 -0.01278937 0.005577503 -2.2930275 0.0218
## 1|2 -1.78602209 0.357530995 -4.9954329 0.0000
## 2|3 -0.66326085 0.355445093 -1.8660009 0.0620
## 3|4 -0.05854195 0.355058695 -0.1648796 0.8690
## 4|5 1.63639847 0.356246245 4.5934476 0.0000(ci_mig1 <- confint(mig1, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## migration -0.09693562 0.382239485
## age_T3 -0.00405177 0.025387704
## sex_T3 -0.17777357 0.293022683
## bmi_T3 -0.02803663 0.002476255exp(cbind(OR = coef(mig1), ci_mig1))## OR 0.3 % 99.7 %
## migration 1.1528699 0.9076144 1.465563
## age_T3 1.0107228 0.9959564 1.025713
## sex_T3 1.0595296 0.8371320 1.340473
## bmi_T3 0.9872921 0.9723528 1.002479mig2 <- 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 0.143410 0.091075 1.57463
## age_T3 -0.003914 0.005583 -0.70098
## sex_T3 -0.006873 0.089087 -0.07715
## bmi_T3 0.019130 0.005798 3.29926
##
## Intercepts:
## Value Std. Error t value
## 1|2 0.3974 0.3644 1.0906
## 2|3 1.7161 0.3653 4.6976
## 3|4 2.4277 0.3667 6.6195
## 4|5 3.9941 0.3767 10.6035
##
## Residual Deviance: 9472.062
## AIC: 9488.062ctable_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 0.143409711 0.091075063 1.57463202 0.1153
## age_T3 -0.003913733 0.005583226 -0.70098052 0.4833
## sex_T3 -0.006872909 0.089087058 -0.07714823 0.9385
## bmi_T3 0.019129851 0.005798227 3.29925863 0.0010
## 1|2 0.397446861 0.364417995 1.09063456 0.2754
## 2|3 1.716052155 0.365300875 4.69764042 0.0000
## 3|4 2.427677925 0.366748059 6.61947041 0.0000
## 4|5 3.994119591 0.376679208 10.60350427 0.0000(ci_mig2 <- confint(mig2, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## migration -0.106789251 0.39158218
## age_T3 -0.019198993 0.01134175
## sex_T3 -0.249574245 0.23787948
## bmi_T3 0.003254186 0.03497847exp(cbind(OR = coef(mig2), ci_mig2))## OR 0.3 % 99.7 %
## migration 1.1542026 0.8987151 1.479319
## age_T3 0.9960939 0.9809841 1.011406
## sex_T3 0.9931507 0.7791324 1.268556
## bmi_T3 1.0193140 1.0032595 1.035597mig3 <- 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 0.11329 0.090872 1.247
## age_T3 0.02216 0.005481 4.043
## sex_T3 0.31618 0.087345 3.620
## bmi_T3 -0.01742 0.005709 -3.051
##
## Intercepts:
## Value Std. Error t value
## 1|2 -2.0602 0.3663 -5.6245
## 2|3 -0.7762 0.3599 -2.1565
## 3|4 0.0518 0.3590 0.1442
## 4|5 1.5663 0.3601 4.3503
##
## Residual Deviance: 9831.888
## AIC: 9847.888ctable_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 0.11329325 0.090872045 1.2467338 0.2125
## age_T3 0.02216009 0.005481224 4.0429084 0.0001
## sex_T3 0.31617688 0.087344580 3.6198797 0.0003
## bmi_T3 -0.01742061 0.005709251 -3.0512950 0.0023
## 1|2 -2.06017672 0.366288686 -5.6244618 0.0000
## 2|3 -0.77622104 0.359947269 -2.1564854 0.0310
## 3|4 0.05176081 0.359039619 0.1441646 0.8854
## 4|5 1.56634091 0.360050062 4.3503420 0.0000(ci_mig3 <- confint(mig3, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## migration -0.13436833 0.362863958
## age_T3 0.00719821 0.037179012
## sex_T3 0.07699292 0.554841624
## bmi_T3 -0.03301220 -0.001777988exp(cbind(OR = coef(mig3), ci_mig3))## OR 0.3 % 99.7 %
## migration 1.1199603 0.8742680 1.4374403
## age_T3 1.0224074 1.0072242 1.0378788
## sex_T3 1.3718729 1.0800344 1.7416651
## bmi_T3 0.9827303 0.9675268 0.9982236mig4 <- 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 0.266473 0.088131 3.0236
## age_T3 0.025747 0.005415 4.7551
## sex_T3 0.077738 0.086345 0.9003
## bmi_T3 -0.007121 0.005605 -1.2704
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.1091 0.3573 -3.1044
## 2|3 0.1874 0.3549 0.5282
## 3|4 0.8302 0.3549 2.3390
## 4|5 2.3982 0.3571 6.7154
##
## Residual Deviance: 11036.34
## AIC: 11052.34ctable_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 0.266472688 0.088130726 3.0236071 0.0025
## age_T3 0.025746608 0.005414580 4.7550516 0.0000
## sex_T3 0.077738455 0.086344989 0.9003239 0.3679
## bmi_T3 -0.007120702 0.005605106 -1.2703955 0.2039
## 1|2 -1.109138035 0.357274323 -3.1044437 0.0019
## 2|3 0.187427534 0.354856922 0.5281778 0.5974
## 3|4 0.830237907 0.354948027 2.3390408 0.0193
## 4|5 2.398240329 0.357127802 6.7153560 0.0000(ci_mig4 <- confint(mig4, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## migration 0.02582332 0.50797918
## age_T3 0.01095821 0.04057482
## sex_T3 -0.15862396 0.31373006
## bmi_T3 -0.02243710 0.00822682exp(cbind(OR = coef(mig4), ci_mig4))## OR 0.3 % 99.7 %
## migration 1.3053519 1.0261596 1.661929
## age_T3 1.0260809 1.0110185 1.041409
## sex_T3 1.0808399 0.8533172 1.368520
## bmi_T3 0.9929046 0.9778127 1.008261mig5 <- 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 0.065858 0.097368 0.6764
## age_T3 0.003254 0.005911 0.5504
## sex_T3 0.092398 0.094399 0.9788
## bmi_T3 0.006082 0.006183 0.9836
##
## Intercepts:
## Value Std. Error t value
## 1|2 -3.2923 0.4010 -8.2107
## 2|3 -1.8847 0.3891 -4.8435
## 3|4 -1.4554 0.3879 -3.7521
## 4|5 0.1807 0.3868 0.4671
##
## Residual Deviance: 7797.598
## AIC: 7813.598ctable_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 0.065857637 0.097368184 0.6763774 0.4988
## age_T3 0.003253809 0.005911270 0.5504417 0.5820
## sex_T3 0.092397950 0.094399260 0.9787995 0.3277
## bmi_T3 0.006081789 0.006183386 0.9835693 0.3253
## 1|2 -3.292315474 0.400978153 -8.2107104 0.0000
## 2|3 -1.884715341 0.389119839 -4.8435344 0.0000
## 3|4 -1.455363841 0.387878832 -3.7521095 0.0002
## 4|5 0.180667436 0.386786206 0.4670990 0.6404(ci_mig5 <- confint(mig5, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## migration -0.19805187 0.33491849
## age_T3 -0.01288550 0.01945189
## sex_T3 -0.16779979 0.34881562
## bmi_T3 -0.01072761 0.02311072exp(cbind(OR = coef(mig5), ci_mig5))## OR 0.3 % 99.7 %
## migration 1.068075 0.8203273 1.397826
## age_T3 1.003259 0.9871972 1.019642
## sex_T3 1.096801 0.8455231 1.417388
## bmi_T3 1.006100 0.9893297 1.023380mig6 <- 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 -0.30179 0.093504 -3.228
## age_T3 -0.02317 0.005597 -4.139
## sex_T3 -0.40863 0.088494 -4.618
## bmi_T3 -0.01204 0.005883 -2.047
##
## Intercepts:
## Value Std. Error t value
## 1|2 -2.6793 0.3689 -7.2620
## 2|3 -0.8853 0.3664 -2.4161
## 3|4 -0.4130 0.3670 -1.1254
## 4|5 1.3046 0.3779 3.4522
##
## Residual Deviance: 9066.911
## AIC: 9082.911ctable_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 -0.30178643 0.093503572 -3.227539 0.0012
## age_T3 -0.02316661 0.005596737 -4.139306 0.0000
## sex_T3 -0.40863070 0.088494012 -4.617608 0.0000
## bmi_T3 -0.01204080 0.005883420 -2.046564 0.0407
## 1|2 -2.67926101 0.368940217 -7.262046 0.0000
## 2|3 -0.88533595 0.366438712 -2.416055 0.0157
## 3|4 -0.41299521 0.366983560 -1.125378 0.2604
## 4|5 1.30461309 0.377904959 3.452225 0.0006(ci_mig6 <- confint(mig6, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## migration -0.55900873 -0.047324271
## age_T3 -0.03850850 -0.007894863
## sex_T3 -0.65046701 -0.166333509
## bmi_T3 -0.02818922 0.004001486exp(cbind(OR = coef(mig6), ci_mig6))## OR 0.3 % 99.7 %
## migration 0.7394960 0.5717756 0.9537781
## age_T3 0.9770997 0.9622235 0.9921362
## sex_T3 0.6645596 0.5218020 0.8467638
## bmi_T3 0.9880314 0.9722044 1.0040095mig7 <- 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 -0.08936 0.099471 -0.8984
## age_T3 -0.02184 0.006058 -3.6050
## sex_T3 -0.35245 0.093662 -3.7629
## bmi_T3 0.01098 0.006278 1.7488
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.8807 0.3930 -2.2407
## 2|3 0.7619 0.3937 1.9354
## 3|4 1.2925 0.3955 3.2681
## 4|5 3.1094 0.4195 7.4114
##
## Residual Deviance: 7341.113
## AIC: 7357.113ctable_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 -0.08936108 0.099471149 -0.8983618 0.3690
## age_T3 -0.02183853 0.006057864 -3.6049895 0.0003
## sex_T3 -0.35244505 0.093662031 -3.7629448 0.0002
## bmi_T3 0.01097808 0.006277536 1.7487879 0.0803
## 1|2 -0.88068641 0.393035571 -2.2407295 0.0250
## 2|3 0.76189348 0.393664833 1.9353862 0.0529
## 3|4 1.29245015 0.395475428 3.2680922 0.0011
## 4|5 3.10941182 0.419542815 7.4114291 0.0000(ci_mig7 <- confint(mig7, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## migration -0.364824803 0.179701966
## age_T3 -0.038471461 -0.005330629
## sex_T3 -0.607290238 -0.094732346
## bmi_T3 -0.006294427 0.028057384exp(cbind(OR = coef(mig7), ci_mig7))## OR 0.3 % 99.7 %
## migration 0.9145153 0.6943183 1.1968606
## age_T3 0.9783982 0.9622592 0.9946836
## sex_T3 0.7029672 0.5448252 0.9096164
## bmi_T3 1.0110386 0.9937253 1.0284547mig8 <- 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 0.152613 0.088003 1.7342
## age_T3 -0.001321 0.005378 -0.2456
## sex_T3 0.355322 0.086033 4.1300
## bmi_T3 0.010936 0.005569 1.9635
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.8128 0.3538 -2.2971
## 2|3 0.3060 0.3528 0.8672
## 3|4 0.6474 0.3530 1.8342
## 4|5 2.3433 0.3554 6.5935
##
## Residual Deviance: 11011.50
## AIC: 11027.50ctable_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 0.152612992 0.088002814 1.7341831 0.0829
## age_T3 -0.001321032 0.005377791 -0.2456459 0.8060
## sex_T3 0.355322287 0.086033460 4.1300476 0.0000
## bmi_T3 0.010935765 0.005569417 1.9635388 0.0496
## 1|2 -0.812780250 0.353822828 -2.2971391 0.0216
## 2|3 0.305950385 0.352786852 0.8672386 0.3858
## 3|4 0.647391973 0.352964050 1.8341584 0.0666
## 4|5 2.343317429 0.355396445 6.5935309 0.0000(ci_mig8 <- confint(mig8, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## migration -0.087839557 0.39360074
## age_T3 -0.016025417 0.01339023
## sex_T3 0.119985038 0.59063568
## bmi_T3 -0.004278438 0.02618946exp(cbind(OR = coef(mig8), ci_mig8))## OR 0.3 % 99.7 %
## migration 1.1648741 0.9159078 1.482309
## age_T3 0.9986798 0.9841023 1.013480
## sex_T3 1.4266404 1.1274800 1.805136
## bmi_T3 1.0109958 0.9957307 1.026535# 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 0.03701 0.103936 0.3561
## age_T3 0.01073 0.005383 1.9939
## sex_T3 0.05221 0.086038 0.6068
## bmi_T3 -0.01276 0.005599 -2.2780
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.9124 0.3573 -5.3522
## 2|3 -0.7901 0.3551 -2.2248
## 3|4 -0.1857 0.3547 -0.5236
## 4|5 1.5084 0.3557 4.2408
##
## Residual Deviance: 11125.14
## AIC: 11141.14ctable_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 0.03700718 0.103935756 0.3560582 0.7218
## age_T3 0.01073305 0.005382889 1.9939204 0.0462
## sex_T3 0.05220766 0.086037873 0.6067986 0.5440
## bmi_T3 -0.01275549 0.005599469 -2.2779817 0.0227
## 1|2 -1.91244692 0.357317966 -5.3522272 0.0000
## 2|3 -0.79010738 0.355142629 -2.2247608 0.0261
## 3|4 -0.18569258 0.354677431 -0.5235534 0.6006
## 4|5 1.50836036 0.355674984 4.2408390 0.0000(ci_une1 <- confint(une1, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## unemploy -0.246777102 0.321921670
## age_T3 -0.003984288 0.025458268
## sex_T3 -0.183336660 0.287325557
## bmi_T3 -0.028066607 0.002566376exp(cbind(OR = coef(une1), ci_une1))## OR 0.3 % 99.7 %
## unemploy 1.0377005 0.7813148 1.379777
## age_T3 1.0107909 0.9960236 1.025785
## sex_T3 1.0535945 0.8324878 1.332858
## bmi_T3 0.9873255 0.9723236 1.002570une2 <- 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 0.09266 0.107297 0.8636
## age_T3 -0.00377 0.005583 -0.6752
## sex_T3 -0.01378 0.089090 -0.1547
## bmi_T3 0.01892 0.005819 3.2517
##
## Intercepts:
## Value Std. Error t value
## 1|2 0.3244 0.3644 0.8902
## 2|3 1.6429 0.3653 4.4978
## 3|4 2.3542 0.3667 6.4206
## 4|5 3.9198 0.3764 10.4130
##
## Residual Deviance: 9473.786
## AIC: 9489.786ctable_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 0.092661621 0.107297137 0.8635983 0.3878
## age_T3 -0.003769783 0.005583043 -0.6752201 0.4995
## sex_T3 -0.013784843 0.089090418 -0.1547287 0.8770
## bmi_T3 0.018923199 0.005819495 3.2516907 0.0011
## 1|2 0.324381675 0.364384174 0.8902189 0.3733
## 2|3 1.642917774 0.365274712 4.4977594 0.0000
## 3|4 2.354183941 0.366662227 6.4205794 0.0000
## 4|5 3.919772184 0.376432048 10.4129609 0.0000(ci_une2 <- confint(une2, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## unemploy -0.202766242 0.38456545
## age_T3 -0.019055053 0.01148472
## sex_T3 -0.256505194 0.23096596
## bmi_T3 0.002989459 0.03482951exp(cbind(OR = coef(une2), ci_une2))## OR 0.3 % 99.7 %
## unemploy 1.0970904 0.8164691 1.468976
## age_T3 0.9962373 0.9811253 1.011551
## sex_T3 0.9863097 0.7737510 1.259816
## bmi_T3 1.0191034 1.0029939 1.035443une3 <- 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 0.18248 0.109480 1.667
## age_T3 0.02231 0.005479 4.072
## sex_T3 0.30745 0.087379 3.519
## bmi_T3 -0.01805 0.005731 -3.150
##
## Intercepts:
## Value Std. Error t value
## 1|2 -2.0165 0.3667 -5.4997
## 2|3 -0.7327 0.3603 -2.0336
## 3|4 0.0951 0.3593 0.2647
## 4|5 1.6100 0.3604 4.4677
##
## Residual Deviance: 9830.652
## AIC: 9846.652ctable_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 0.18247568 0.109479650 1.6667544 0.0956
## age_T3 0.02230621 0.005478532 4.0715677 0.0000
## sex_T3 0.30745145 0.087378692 3.5186090 0.0004
## bmi_T3 -0.01805235 0.005731359 -3.1497512 0.0016
## 1|2 -2.01646831 0.366652565 -5.4996706 0.0000
## 2|3 -0.73268747 0.360282576 -2.0336467 0.0420
## 3|4 0.09511216 0.359335000 0.2646894 0.7912
## 4|5 1.61003868 0.360369154 4.4677483 0.0000(ci_une3 <- confint(une3, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## unemploy -0.115402299 0.483865258
## age_T3 0.007351508 0.037317585
## sex_T3 0.068162618 0.546197121
## bmi_T3 -0.033706013 -0.002351058exp(cbind(OR = coef(une3), ci_une3))## OR 0.3 % 99.7 %
## unemploy 1.2001850 0.8910076 1.6223330
## age_T3 1.0225569 1.0073786 1.0380226
## sex_T3 1.3599548 1.0705394 1.7266742
## bmi_T3 0.9821096 0.9668557 0.9976517une4 <- 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 0.315912 0.105817 2.9855
## age_T3 0.026109 0.005415 4.8214
## sex_T3 0.063729 0.086387 0.7377
## bmi_T3 -0.008177 0.005636 -1.4509
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.1050 0.3582 -3.0847
## 2|3 0.1916 0.3558 0.5386
## 3|4 0.8340 0.3559 2.3436
## 4|5 2.4015 0.3580 6.7079
##
## Residual Deviance: 11036.56
## AIC: 11052.56ctable_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 0.315911832 0.105816605 2.9854656 0.0028
## age_T3 0.026109294 0.005415325 4.8213715 0.0000
## sex_T3 0.063728606 0.086387261 0.7377084 0.4607
## bmi_T3 -0.008177233 0.005636128 -1.4508601 0.1468
## 1|2 -1.105016186 0.358228858 -3.0846655 0.0020
## 2|3 0.191643369 0.355815067 0.5386039 0.5902
## 3|4 0.834033229 0.355872723 2.3436279 0.0191
## 4|5 2.401486191 0.358009550 6.7078830 0.0000(ci_une4 <- confint(une4, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## unemploy 0.02712398 0.60616972
## age_T3 0.01131797 0.04093855
## sex_T3 -0.17276002 0.29982367
## bmi_T3 -0.02358084 0.00725252exp(cbind(OR = coef(une4), ci_une4))## OR 0.3 % 99.7 %
## unemploy 1.3715093 1.0274952 1.833396
## age_T3 1.0264531 1.0113823 1.041788
## sex_T3 1.0658031 0.8413395 1.349621
## bmi_T3 0.9918561 0.9766950 1.007279une5 <- 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 0.387744 0.121785 3.1838
## age_T3 0.003679 0.005919 0.6215
## sex_T3 0.083409 0.094500 0.8826
## bmi_T3 0.004672 0.006208 0.7526
##
## Intercepts:
## Value Std. Error t value
## 1|2 -2.9853 0.4052 -7.3674
## 2|3 -1.5781 0.3935 -4.0101
## 3|4 -1.1488 0.3924 -2.9278
## 4|5 0.4908 0.3916 1.2535
##
## Residual Deviance: 7787.503
## AIC: 7803.503ctable_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 0.387743851 0.121785323 3.1838307 0.0015
## age_T3 0.003678732 0.005919414 0.6214689 0.5343
## sex_T3 0.083409119 0.094499852 0.8826376 0.3774
## bmi_T3 0.004671696 0.006207585 0.7525787 0.4517
## 1|2 -2.985285355 0.405201986 -7.3674006 0.0000
## 2|3 -1.578132865 0.393538806 -4.0101074 0.0001
## 3|4 -1.148764020 0.392365537 -2.9277903 0.0034
## 4|5 0.490815664 0.391561436 1.2534832 0.2100(ci_une5 <- confint(une5, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## unemploy 0.06044451 0.72767444
## age_T3 -0.01249169 0.01989003
## sex_T3 -0.17715211 0.34000784
## bmi_T3 -0.01220143 0.02176921exp(cbind(OR = coef(une5), ci_une5))## OR 0.3 % 99.7 %
## unemploy 1.473652 1.0623087 2.070260
## age_T3 1.003686 0.9875860 1.020089
## sex_T3 1.086986 0.8376524 1.404959
## bmi_T3 1.004683 0.9878727 1.022008une6 <- 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 -0.46702 0.113874 -4.101
## age_T3 -0.02359 0.005602 -4.211
## sex_T3 -0.39443 0.088553 -4.454
## bmi_T3 -0.01086 0.005904 -1.840
##
## Intercepts:
## Value Std. Error t value
## 1|2 -2.8066 0.3723 -7.5395
## 2|3 -1.0102 0.3696 -2.7332
## 3|4 -0.5378 0.3701 -1.4529
## 4|5 1.1797 0.3810 3.0968
##
## Residual Deviance: 9060.253
## AIC: 9076.253ctable_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 -0.46701545 0.113873831 -4.101166 0.0000
## age_T3 -0.02359312 0.005602386 -4.211263 0.0000
## sex_T3 -0.39443384 0.088552502 -4.454237 0.0000
## bmi_T3 -0.01086392 0.005904052 -1.840078 0.0658
## 1|2 -2.80664980 0.372258144 -7.539526 0.0000
## 2|3 -1.01020484 0.369609586 -2.733167 0.0063
## 3|4 -0.53775566 0.370135583 -1.452861 0.1463
## 4|5 1.17972892 0.380956872 3.096752 0.0020(ci_une6 <- confint(une6, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## unemploy -0.78154630 -0.158045665
## age_T3 -0.03894996 -0.008305295
## sex_T3 -0.63642245 -0.151968958
## bmi_T3 -0.02706731 0.005235633exp(cbind(OR = coef(une6), ci_une6))## OR 0.3 % 99.7 %
## unemploy 0.6268704 0.4576977 0.8538108
## age_T3 0.9766830 0.9617988 0.9917291
## sex_T3 0.6740616 0.5291822 0.8590149
## bmi_T3 0.9891949 0.9732957 1.0052494une7 <- 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 -0.33640 0.123615 -2.721
## age_T3 -0.02213 0.006063 -3.650
## sex_T3 -0.34250 0.093718 -3.655
## bmi_T3 0.01240 0.006310 1.966
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.1009 0.3958 -2.7815
## 2|3 0.5437 0.3963 1.3721
## 3|4 1.0744 0.3981 2.6990
## 4|5 2.8912 0.4220 6.8516
##
## Residual Deviance: 7334.24
## AIC: 7350.24ctable_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 -0.33640093 0.123614921 -2.721362 0.0065
## age_T3 -0.02212881 0.006062540 -3.650089 0.0003
## sex_T3 -0.34250455 0.093717698 -3.654641 0.0003
## bmi_T3 0.01240285 0.006309748 1.965664 0.0493
## 1|2 -1.10093668 0.395807872 -2.781493 0.0054
## 2|3 0.54370261 0.396269729 1.372052 0.1700
## 3|4 1.07435726 0.398058075 2.698996 0.0070
## 4|5 2.89121893 0.421975745 6.851623 0.0000(ci_une7 <- confint(une7, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## unemploy -0.681808938 -0.004537173
## age_T3 -0.038774800 -0.005608209
## sex_T3 -0.597490627 -0.084629391
## bmi_T3 -0.004956371 0.029571043exp(cbind(OR = coef(une7), ci_une7))## OR 0.3 % 99.7 %
## unemploy 0.7143366 0.5057014 0.9954731
## age_T3 0.9781142 0.9619673 0.9944075
## sex_T3 0.7099899 0.5501905 0.9188528
## bmi_T3 1.0124801 0.9950559 1.0300126une8 <- 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 0.001331 0.106241 0.01253
## age_T3 -0.001317 0.005378 -0.24493
## sex_T3 0.351523 0.085982 4.08831
## bmi_T3 0.011215 0.005587 2.00724
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.9823 0.3546 -2.7702
## 2|3 0.1362 0.3535 0.3854
## 3|4 0.4773 0.3536 1.3499
## 4|5 2.1714 0.3557 6.1048
##
## Residual Deviance: 11014.51
## AIC: 11030.51ctable_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 0.001330807 0.106241031 0.0125263 0.9900
## age_T3 -0.001317175 0.005377669 -0.2449342 0.8065
## sex_T3 0.351522598 0.085982442 4.0883067 0.0000
## bmi_T3 0.011215123 0.005587341 2.0072379 0.0447
## 1|2 -0.982296638 0.354589500 -2.7702361 0.0056
## 2|3 0.136238424 0.353462025 0.3854401 0.6999
## 3|4 0.477269765 0.353566022 1.3498745 0.1771
## 4|5 2.171367121 0.355682205 6.1047955 0.0000(ci_une8 <- confint(une8, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## unemploy -0.288962994 0.29236508
## age_T3 -0.016021329 0.01339375
## sex_T3 0.116316538 0.58668873
## bmi_T3 -0.004050391 0.02651554exp(cbind(OR = coef(une8), ci_une8))## OR 0.3 % 99.7 %
## unemploy 1.0013317 0.7490399 1.339592
## age_T3 0.9986837 0.9841063 1.013484
## sex_T3 1.4212299 1.1233514 1.798025
## bmi_T3 1.0112782 0.9959578 1.026870# 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 -0.39825 0.097522 -4.0837
## age_T3 0.01104 0.005381 2.0524
## sex_T3 0.08168 0.086286 0.9467
## bmi_T3 -0.01320 0.005587 -2.3623
##
## Intercepts:
## Value Std. Error t value
## 1|2 -2.3412 0.3570 -6.5572
## 2|3 -1.2175 0.3546 -3.4335
## 3|4 -0.6108 0.3540 -1.7257
## 4|5 1.0886 0.3545 3.0712
##
## Residual Deviance: 11104.86
## AIC: 11120.86
## (1 observation 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 -0.39825003 0.097521739 -4.0837051 0.0000
## age_T3 0.01104385 0.005380999 2.0523786 0.0401
## sex_T3 0.08168469 0.086285581 0.9466784 0.3438
## bmi_T3 -0.01319926 0.005587476 -2.3622939 0.0182
## 1|2 -2.34121135 0.357045785 -6.5571740 0.0000
## 2|3 -1.21750677 0.354597852 -3.4334860 0.0006
## 3|4 -0.61084100 0.353960800 -1.7257306 0.0844
## 4|5 1.08858980 0.354456137 3.0711552 0.0021(ci_sin1 <- confint(sin1, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## singlepar -0.664935431 -0.131357712
## age_T3 -0.003666897 0.025765270
## sex_T3 -0.154528202 0.317487302
## bmi_T3 -0.028479767 0.002087691exp(cbind(OR = coef(sin1), ci_sin1))## OR 0.3 % 99.7 %
## singlepar 0.6714941 0.5143067 0.876904
## age_T3 1.0111051 0.9963398 1.026100
## sex_T3 1.0851136 0.8568193 1.373672
## bmi_T3 0.9868875 0.9719220 1.002090sin2 <- 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 -0.065951 0.100045 -0.65921
## age_T3 -0.003781 0.005586 -0.67697
## sex_T3 -0.007921 0.089289 -0.08871
## bmi_T3 0.019324 0.005800 3.33169
##
## Intercepts:
## Value Std. Error t value
## 1|2 0.1720 0.3629 0.4740
## 2|3 1.4910 0.3637 4.1001
## 3|4 2.2008 0.3650 6.0305
## 4|5 3.7666 0.3747 10.0529
##
## Residual Deviance: 9469.397
## AIC: 9485.397
## (1 observation 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 -0.065950635 0.100045060 -0.65920931 0.5098
## age_T3 -0.003781274 0.005585562 -0.67697277 0.4984
## sex_T3 -0.007920863 0.089288629 -0.08871077 0.9293
## bmi_T3 0.019324264 0.005800140 3.33168920 0.0009
## 1|2 0.172004408 0.362894570 0.47397901 0.6355
## 2|3 1.491041583 0.363658183 4.10011833 0.0000
## 3|4 2.200835363 0.364951974 6.03047941 0.0000
## 4|5 3.766608279 0.374677263 10.05294062 0.0000(ci_sin2 <- confint(sin2, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## singlepar -0.341625913 0.20596425
## age_T3 -0.019073128 0.01148042
## sex_T3 -0.251184564 0.23736988
## bmi_T3 0.003443435 0.03517791exp(cbind(OR = coef(sin2), ci_sin2))## OR 0.3 % 99.7 %
## singlepar 0.9361771 0.7106140 1.228709
## age_T3 0.9962259 0.9811076 1.011547
## sex_T3 0.9921104 0.7778788 1.267910
## bmi_T3 1.0195122 1.0034494 1.035804sin3 <- 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 -0.45144 0.099659 -4.530
## age_T3 0.02286 0.005485 4.167
## sex_T3 0.34396 0.087613 3.926
## bmi_T3 -0.01810 0.005718 -3.166
##
## Intercepts:
## Value Std. Error t value
## 1|2 -2.6313 0.3661 -7.1874
## 2|3 -1.3470 0.3596 -3.7463
## 3|4 -0.5148 0.3584 -1.4365
## 4|5 1.0062 0.3586 2.8057
##
## Residual Deviance: 9807.962
## AIC: 9823.962
## (1 observation 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 -0.45144450 0.099658811 -4.529901 0.0000
## age_T3 0.02285686 0.005484776 4.167328 0.0000
## sex_T3 0.34395521 0.087612523 3.925868 0.0001
## bmi_T3 -0.01810334 0.005717691 -3.166198 0.0015
## 1|2 -2.63128993 0.366098095 -7.187390 0.0000
## 2|3 -1.34699776 0.359555875 -3.746282 0.0002
## 3|4 -0.51478126 0.358358772 -1.436497 0.1509
## 4|5 1.00616944 0.358611994 2.805733 0.0050(ci_sin3 <- confint(sin3, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## singlepar -0.723661727 -0.178345425
## age_T3 0.007885957 0.037886132
## sex_T3 0.104048990 0.583361378
## bmi_T3 -0.033719590 -0.002439417exp(cbind(OR = coef(sin3), ci_sin3))## OR 0.3 % 99.7 %
## singlepar 0.6367078 0.4849732 0.8366534
## age_T3 1.0231201 1.0079171 1.0386130
## sex_T3 1.4105155 1.1096548 1.7920521
## bmi_T3 0.9820595 0.9668426 0.9975636sin4 <- 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 -0.520571 0.096617 -5.388
## age_T3 0.026586 0.005417 4.908
## sex_T3 0.104919 0.086533 1.212
## bmi_T3 -0.007879 0.005614 -1.403
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.9269 0.3575 -5.3906
## 2|3 -0.6259 0.3545 -1.7655
## 3|4 0.0193 0.3542 0.0546
## 4|5 1.5914 0.3553 4.4790
##
## Residual Deviance: 11014.61
## AIC: 11030.61
## (1 observation 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 -0.520571118 0.096617363 -5.38796654 0.0000
## age_T3 0.026586074 0.005417061 4.90784073 0.0000
## sex_T3 0.104918871 0.086533115 1.21247075 0.2253
## bmi_T3 -0.007878871 0.005614494 -1.40330919 0.1605
## 1|2 -1.926879018 0.357452616 -5.39058586 0.0000
## 2|3 -0.625941662 0.354541855 -1.76549441 0.0775
## 3|4 0.019345643 0.354205244 0.05461704 0.9564
## 4|5 1.591413713 0.355303014 4.47903240 0.0000(ci_sin4 <- confint(sin4, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## singlepar -0.78485130 -0.256216873
## age_T3 0.01178968 0.041419715
## sex_T3 -0.13195501 0.341426182
## bmi_T3 -0.02322169 0.007493114exp(cbind(OR = coef(sin4), ci_sin4))## OR 0.3 % 99.7 %
## singlepar 0.5941811 0.4561875 0.7739741
## age_T3 1.0269426 1.0118595 1.0422895
## sex_T3 1.1106205 0.8763804 1.4069527
## bmi_T3 0.9921521 0.9770459 1.0075213sin5 <- 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 -0.403369 0.104407 -3.8634
## age_T3 0.003895 0.005914 0.6586
## sex_T3 0.116150 0.094704 1.2264
## bmi_T3 0.005623 0.006183 0.9093
##
## Intercepts:
## Value Std. Error t value
## 1|2 -3.7601 0.4004 -9.3908
## 2|3 -2.3507 0.3884 -6.0520
## 3|4 -1.9201 0.3871 -4.9601
## 4|5 -0.2789 0.3854 -0.7237
##
## Residual Deviance: 7782.388
## AIC: 7798.388
## (1 observation 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 -0.403369368 0.104406959 -3.8634338 0.0001
## age_T3 0.003894729 0.005913564 0.6586095 0.5101
## sex_T3 0.116149847 0.094704331 1.2264470 0.2200
## bmi_T3 0.005622534 0.006183089 0.9093407 0.3632
## 1|2 -3.760147384 0.400409626 -9.3907517 0.0000
## 2|3 -2.350726121 0.388419731 -6.0520255 0.0000
## 3|4 -1.920128884 0.387115824 -4.9600889 0.0000
## 4|5 -0.278926990 0.385444318 -0.7236505 0.4693(ci_sin5 <- confint(sin5, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## singlepar -0.68716174 -0.11564650
## age_T3 -0.01225901 0.02009068
## sex_T3 -0.14476671 0.37350557
## bmi_T3 -0.01119294 0.02264379exp(cbind(OR = coef(sin5), ci_sin5))## OR 0.3 % 99.7 %
## singlepar 0.6680653 0.5030017 0.8907901
## age_T3 1.0039023 0.9878158 1.0202939
## sex_T3 1.1231642 0.8652241 1.4528187
## bmi_T3 1.0056384 0.9888695 1.0229021sin6 <- 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 0.57108 0.099518 5.739
## age_T3 -0.02414 0.005598 -4.313
## sex_T3 -0.44212 0.088867 -4.975
## bmi_T3 -0.01176 0.005866 -2.004
##
## Intercepts:
## Value Std. Error t value
## 1|2 -1.8063 0.3662 -4.9327
## 2|3 -0.0025 0.3651 -0.0069
## 3|4 0.4702 0.3658 1.2853
## 4|5 2.1911 0.3770 5.8119
##
## Residual Deviance: 9039.094
## AIC: 9055.094
## (1 observation 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 0.571084266 0.099517860 5.738510328 0.0000
## age_T3 -0.024144202 0.005598470 -4.312642812 0.0000
## sex_T3 -0.442123989 0.088867447 -4.975095005 0.0000
## bmi_T3 -0.011756578 0.005866112 -2.004151666 0.0451
## 1|2 -1.806283734 0.366184288 -4.932717741 0.0000
## 2|3 -0.002519133 0.365071553 -0.006900382 0.9945
## 3|4 0.470150678 0.365790297 1.285301118 0.1987
## 4|5 2.191072186 0.376995512 5.811931755 0.0000(ci_sin6 <- confint(sin6, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## singlepar 0.29863923 0.843153297
## age_T3 -0.03948982 -0.008866610
## sex_T3 -0.68500723 -0.198832097
## bmi_T3 -0.02786254 0.004233229exp(cbind(OR = coef(sin6), ci_sin6))## OR 0.3 % 99.7 %
## singlepar 1.7701854 1.3480232 2.3236827
## age_T3 0.9761449 0.9612797 0.9911726
## sex_T3 0.6426699 0.5040866 0.8196875
## bmi_T3 0.9883123 0.9725220 1.0042422sin7 <- 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 0.29069 0.106488 2.730
## age_T3 -0.02242 0.006060 -3.700
## sex_T3 -0.37123 0.094019 -3.949
## bmi_T3 0.01150 0.006277 1.833
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.5032 0.3911 -1.2865
## 2|3 1.1431 0.3922 2.9146
## 3|4 1.6756 0.3941 4.2519
## 4|5 3.4887 0.4182 8.3416
##
## Residual Deviance: 7328.548
## AIC: 7344.548
## (1 observation 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 0.29068524 0.106488019 2.729746 0.0063
## age_T3 -0.02242343 0.006060010 -3.700230 0.0002
## sex_T3 -0.37123436 0.094019031 -3.948502 0.0001
## bmi_T3 0.01150410 0.006276543 1.832871 0.0668
## 1|2 -0.50317875 0.391131256 -1.286470 0.1983
## 2|3 1.14313430 0.392214919 2.914561 0.0036
## 3|4 1.67557189 0.394075412 4.251907 0.0000
## 4|5 3.48874411 0.418236559 8.341557 0.0000(ci_sin7 <- confint(sin7, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## singlepar -0.003457421 0.579529295
## age_T3 -0.039063515 -0.005910811
## sex_T3 -0.627091824 -0.112581007
## bmi_T3 -0.005765970 0.028580420exp(cbind(OR = coef(sin7), ci_sin7))## OR 0.3 % 99.7 %
## singlepar 1.3373436 0.9965485 1.7851979
## age_T3 0.9778261 0.9616896 0.9941066
## sex_T3 0.6898822 0.5341429 0.8935250
## bmi_T3 1.0115705 0.9942506 1.0289928sin8 <- 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 0.170496 0.098500 1.7309
## age_T3 -0.001526 0.005381 -0.2836
## sex_T3 0.340349 0.086189 3.9489
## bmi_T3 0.011571 0.005574 2.0760
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.8164 0.3534 -2.3101
## 2|3 0.3026 0.3524 0.8587
## 3|4 0.6437 0.3525 1.8261
## 4|5 2.3379 0.3548 6.5899
##
## Residual Deviance: 11009.60
## AIC: 11025.60
## (1 observation 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 0.170495523 0.098499879 1.7309212 0.0835
## age_T3 -0.001526053 0.005380920 -0.2836045 0.7767
## sex_T3 0.340349220 0.086188519 3.9488928 0.0001
## bmi_T3 0.011571068 0.005573648 2.0760315 0.0379
## 1|2 -0.816362017 0.353390734 -2.3100833 0.0209
## 2|3 0.302563576 0.352369784 0.8586536 0.3905
## 3|4 0.643673739 0.352486173 1.8260964 0.0678
## 4|5 2.337860211 0.354761730 6.5899448 0.0000(ci_sin8 <- confint(sin8, level=0.99375))## Waiting for profiling to be done...## 0.3 % 99.7 %
## singlepar -0.098349153 0.44057052
## age_T3 -0.016239857 0.01319293
## sex_T3 0.104585092 0.57608235
## bmi_T3 -0.003655103 0.02683596exp(cbind(OR = coef(sin8), ci_sin8))## OR 0.3 % 99.7 %
## singlepar 1.1858923 0.9063324 1.553593
## age_T3 0.9984751 0.9838913 1.013280
## sex_T3 1.4054383 1.1102499 1.779055
## bmi_T3 1.0116383 0.9963516 1.027199subset$foodst_01_T3 <- as.numeric(subset$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
## -24.9212 -5.9251 0.2444 6.2289 22.8926
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.93866 1.67784 8.307 < 2e-16 ***
## foodst_01_T3 1.19573 0.11177 10.698 < 2e-16 ***
## age_T3 0.13976 0.02583 5.412 6.64e-08 ***
## sex_T3 1.14778 0.41526 2.764 0.00574 **
## bmi_T3 -0.02549 0.02709 -0.941 0.34681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.698 on 3720 degrees of freedom
## Multiple R-squared: 0.03893, Adjusted R-squared: 0.0379
## F-statistic: 37.68 on 4 and 3720 DF, p-value: < 2.2e-16confint(reg1, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 9.34820879 18.52910907
## foodst_01_T3 0.88993977 1.50152597
## age_T3 0.06910105 0.21041989
## sex_T3 0.01166354 2.28389821
## bmi_T3 -0.09961339 0.04863098reg2 <- 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
## -24.5355 -6.0166 0.2925 6.3629 25.1775
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.35137 1.66383 11.631 < 2e-16 ***
## foodst_02_T3 -0.84334 0.13055 -6.460 1.18e-10 ***
## age_T3 0.14588 0.02607 5.596 2.35e-08 ***
## sex_T3 1.17753 0.41924 2.809 0.005 **
## bmi_T3 -0.02457 0.02739 -0.897 0.370
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.782 on 3720 degrees of freedom
## Multiple R-squared: 0.02036, Adjusted R-squared: 0.0193
## F-statistic: 19.32 on 4 and 3720 DF, p-value: 9.48e-16confint(reg2, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 14.79925946 23.90347496
## foodst_02_T3 -1.20051569 -0.48616954
## age_T3 0.07455782 0.21719591
## sex_T3 0.03051884 2.32453244
## bmi_T3 -0.09950607 0.05035761reg3 <- 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
## -26.3961 -6.0264 0.2004 6.2839 24.2117
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.32492 1.70279 8.413 < 2e-16 ***
## foodst_03_T3 1.08251 0.13062 8.287 < 2e-16 ***
## age_T3 0.13238 0.02603 5.086 3.84e-07 ***
## sex_T3 0.96909 0.41859 2.315 0.0207 *
## bmi_T3 -0.02296 0.02728 -0.842 0.4000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.75 on 3720 degrees of freedom
## Multiple R-squared: 0.02732, Adjusted R-squared: 0.02628
## F-statistic: 26.12 on 4 and 3720 DF, p-value: < 2.2e-16confint(reg3, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 9.66622798 18.98362173
## foodst_03_T3 0.72513319 1.43988832
## age_T3 0.06116827 0.20359715
## sex_T3 -0.17613749 2.11431820
## bmi_T3 -0.09759953 0.05167655reg4 <- 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
## -25.1415 -6.0146 0.3127 6.3840 24.4674
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.18600 1.67653 9.058 < 2e-16 ***
## foodst_04_T3 0.97311 0.11465 8.487 < 2e-16 ***
## age_T3 0.13002 0.02603 4.994 6.18e-07 ***
## sex_T3 1.13010 0.41762 2.706 0.00684 **
## bmi_T3 -0.03035 0.02723 -1.114 0.26518
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.746 on 3720 degrees of freedom
## Multiple R-squared: 0.02818, Adjusted R-squared: 0.02714
## F-statistic: 26.97 on 4 and 3720 DF, p-value: < 2.2e-16confint(reg4, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 10.59913418 19.77285585
## foodst_04_T3 0.65942895 1.28680080
## age_T3 0.05879596 0.20125246
## sex_T3 -0.01246348 2.27266552
## bmi_T3 -0.10485899 0.04416061reg5 <- 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
## -24.537 -6.083 0.270 6.378 23.177
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.95155 1.77341 8.995 < 2e-16 ***
## foodst_05_T3 0.48456 0.15032 3.223 0.00128 **
## age_T3 0.14614 0.02618 5.583 2.53e-08 ***
## sex_T3 1.16103 0.42109 2.757 0.00586 **
## bmi_T3 -0.03612 0.02745 -1.316 0.18837
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.818 on 3720 degrees of freedom
## Multiple R-squared: 0.01213, Adjusted R-squared: 0.01106
## F-statistic: 11.41 on 4 and 3720 DF, p-value: 3.292e-09confint(reg5, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 11.099636578 20.80346511
## foodst_05_T3 0.073285799 0.89582467
## age_T3 0.074526793 0.21776270
## sex_T3 0.008965655 2.31308761
## bmi_T3 -0.111225009 0.03898994reg6 <- 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
## -25.8900 -6.0522 0.1795 6.3574 23.1628
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.60374 1.70435 11.502 < 2e-16 ***
## foodst_06_T3 -0.55399 0.13806 -4.013 6.12e-05 ***
## age_T3 0.14005 0.02620 5.345 9.60e-08 ***
## sex_T3 1.06887 0.42175 2.534 0.0113 *
## bmi_T3 -0.03817 0.02744 -1.391 0.1643
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.812 on 3720 degrees of freedom
## Multiple R-squared: 0.01364, Adjusted R-squared: 0.01257
## F-statistic: 12.86 on 4 and 3720 DF, p-value: 2.141e-10confint(reg6, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 14.94074983 24.26673193
## foodst_06_T3 -0.93170271 -0.17627624
## age_T3 0.06835824 0.21174294
## sex_T3 -0.08499784 2.22274000
## bmi_T3 -0.11323655 0.03690599reg7 <- 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
## -25.8325 -6.0728 0.3457 6.2775 23.8623
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.74511 1.68659 11.707 < 2e-16 ***
## foodst_07_T3 -0.86040 0.16288 -5.283 1.35e-07 ***
## age_T3 0.13824 0.02616 5.284 1.33e-07 ***
## sex_T3 1.07237 0.42059 2.550 0.0108 *
## bmi_T3 -0.02999 0.02741 -1.094 0.2739
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.798 on 3720 degrees of freedom
## Multiple R-squared: 0.01674, Adjusted R-squared: 0.01568
## F-statistic: 15.83 on 4 and 3720 DF, p-value: 7.392e-13confint(reg7, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 15.13073562 24.35948834
## foodst_07_T3 -1.30601504 -0.41477923
## age_T3 0.06666782 0.20981571
## sex_T3 -0.07834581 2.22308521
## bmi_T3 -0.10497465 0.04498852reg8 <- 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
## -26.0725 -6.0653 0.3154 6.3930 22.5974
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.75870 1.68098 9.970 < 2e-16 ***
## foodst_08_T3 0.44964 0.10654 4.220 2.50e-05 ***
## age_T3 0.14722 0.02615 5.629 1.94e-08 ***
## sex_T3 1.07052 0.42152 2.540 0.0111 *
## bmi_T3 -0.03924 0.02744 -1.430 0.1528
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.81 on 3720 degrees of freedom
## Multiple R-squared: 0.01409, Adjusted R-squared: 0.01303
## F-statistic: 13.29 on 4 and 3720 DF, p-value: 9.434e-11confint(reg8, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 12.15966708 21.35773947
## foodst_08_T3 0.15815180 0.74112046
## age_T3 0.07567061 0.21876805
## sex_T3 -0.08272724 2.22377599
## bmi_T3 -0.11431447 0.03582908# 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
## -26.2289 -5.9992 0.2969 6.2489 23.4079
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.74141 1.81855 5.357 9.01e-08 ***
## isced_cat2011_T3 2.93866 0.25291 11.620 < 2e-16 ***
## age_T3 0.11323 0.02633 4.300 1.75e-05 ***
## sex_T3 1.57587 0.42543 3.704 0.000215 ***
## bmi_T3 0.03052 0.02787 1.095 0.273561
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.669 on 3605 degrees of freedom
## (115 observations deleted due to missingness)
## Multiple R-squared: 0.04447, Adjusted R-squared: 0.04341
## F-statistic: 41.95 on 4 and 3605 DF, p-value: < 2.2e-16confint(edu_total, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 4.76590546 14.7169110
## isced_cat2011_T3 2.24671962 3.6306080
## age_T3 0.04118358 0.1852853
## sex_T3 0.41191943 2.7398300
## bmi_T3 -0.04573917 0.1067873# 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
## -25.2770 -5.9623 0.2383 6.2759 24.4759
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.496084 1.751118 7.707 1.66e-14 ***
## income_cat_T3 1.018351 0.106146 9.594 < 2e-16 ***
## age_T3 0.139841 0.026700 5.237 1.72e-07 ***
## sex_T3 1.607880 0.426090 3.774 0.000164 ***
## bmi_T3 0.004805 0.028471 0.169 0.865991
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.729 on 3512 degrees of freedom
## (208 observations deleted due to missingness)
## Multiple R-squared: 0.03515, Adjusted R-squared: 0.03405
## F-statistic: 31.99 on 4 and 3512 DF, p-value: < 2.2e-16confint(inc_total, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 8.70499194 18.28717595
## income_cat_T3 0.72793419 1.30876727
## age_T3 0.06678826 0.21289322
## sex_T3 0.44208922 2.77367053
## bmi_T3 -0.07309216 0.08270197# 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
## -25.4316 -6.0393 0.2648 6.3942 22.8526
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.43309 1.72390 10.693 < 2e-16 ***
## migration -0.40839 0.42700 -0.956 0.33892
## age_T3 0.14668 0.02621 5.596 2.36e-08 ***
## sex_T3 1.18010 0.42165 2.799 0.00516 **
## bmi_T3 -0.03461 0.02750 -1.259 0.20824
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.83 on 3720 degrees of freedom
## Multiple R-squared: 0.00961, Adjusted R-squared: 0.008545
## F-statistic: 9.024 on 4 and 3720 DF, p-value: 2.991e-07confint(mig_total, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 13.71663059 23.14955210
## migration -1.57662389 0.75984447
## age_T3 0.07496093 0.21838956
## sex_T3 0.02650089 2.33369772
## bmi_T3 -0.10984526 0.04062334# 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
## -25.5230 -6.0019 0.2671 6.3495 22.6487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.96283 1.72643 11.563 < 2e-16 ***
## unemploy -2.04773 0.50587 -4.048 5.27e-05 ***
## age_T3 0.14456 0.02616 5.526 3.50e-08 ***
## sex_T3 1.23793 0.42082 2.942 0.00328 **
## bmi_T3 -0.02616 0.02752 -0.950 0.34201
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.811 on 3720 degrees of freedom
## Multiple R-squared: 0.01371, Adjusted R-squared: 0.01265
## F-statistic: 12.93 on 4 and 3720 DF, p-value: 1.869e-10confint(une_total, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 15.23943207 24.68622614
## unemploy -3.43175973 -0.66369488
## age_T3 0.07299188 0.21613309
## sex_T3 0.08659327 2.38927280
## bmi_T3 -0.10146119 0.04914724# 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
## -25.4025 -6.0357 0.2519 6.3964 22.8090
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.14382 1.72343 10.528 < 2e-16 ***
## singlepar -0.16379 0.47928 -0.342 0.73257
## age_T3 0.14691 0.02621 5.606 2.22e-08 ***
## sex_T3 1.20606 0.42239 2.855 0.00432 **
## bmi_T3 -0.03634 0.02749 -1.322 0.18624
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.826 on 3719 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.009473, Adjusted R-squared: 0.008407
## F-statistic: 8.891 on 4 and 3719 DF, p-value: 3.834e-07confint(sin_total, level=0.99375)## 0.312 % 99.688 %
## (Intercept) 13.42863787 22.85900094
## singlepar -1.47506375 1.14748688
## age_T3 0.07520855 0.21860570
## sex_T3 0.05043696 2.36168711
## bmi_T3 -0.11154142 0.03886394unloadNamespace("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 123 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 67
##
## Used Total
## Number of observations 3610 3725
## Number of clusters [country] 6
##
## Model Test User Model:
##
## Test statistic 59.777
## 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 1.603 0.243 6.601 0.000
## fds_01_T3 (a1) 0.800 0.119 6.706 0.000
## fds_02_T3 (a2) -0.599 0.127 -4.707 0.000
## fds_03_T3 (a3) 0.361 0.156 2.306 0.021
## fds_04_T3 (a4) 0.615 0.138 4.470 0.000
## fds_05_T3 (a5) -0.128 0.160 -0.797 0.425
## fds_06_T3 (a6) -0.651 0.147 -4.415 0.000
## fds_07_T3 (a7) -0.063 0.172 -0.367 0.714
## fds_08_T3 (a8) 0.356 0.102 3.503 0.000
## age_T3 0.094 0.025 3.767 0.000
## sex_T3 1.480 0.402 3.679 0.000
## bmi_T3 0.078 0.026 2.959 0.003
## foodst_01_T3 ~
## i_2011_T3 (b1) 0.106 0.036 2.928 0.003
## foodst_02_T3 ~
## i_2011_T3 (b2) -0.266 0.031 -8.651 0.000
## foodst_03_T3 ~
## i_2011_T3 (b3) 0.120 0.031 3.856 0.000
## foodst_04_T3 ~
## i_2011_T3 (b4) 0.082 0.036 2.306 0.021
## foodst_05_T3 ~
## i_2011_T3 (b5) -0.032 0.027 -1.176 0.240
## foodst_06_T3 ~
## i_2011_T3 (b6) 0.199 0.030 6.720 0.000
## foodst_07_T3 ~
## i_2011_T3 (b7) 0.006 0.025 0.256 0.798
## foodst_08_T3 ~
## i_2011_T3 (b8) -0.017 0.039 -0.451 0.652
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .foodst_01_T3 ~~
## .foodst_02_T3 0.198 0.023 8.550 0.000
## .foodst_03_T3 0.559 0.025 22.325 0.000
## .foodst_04_T3 0.564 0.028 20.067 0.000
## .foodst_05_T3 0.202 0.021 9.769 0.000
## .foodst_06_T3 -0.137 0.022 -6.193 0.000
## .foodst_07_T3 -0.105 0.019 -5.575 0.000
## .foodst_08_T3 0.142 0.029 4.934 0.000
## .foodst_02_T3 ~~
## .foodst_03_T3 0.078 0.020 3.932 0.000
## .foodst_04_T3 0.115 0.023 5.048 0.000
## .foodst_05_T3 0.056 0.017 3.182 0.001
## .foodst_06_T3 0.064 0.019 3.377 0.001
## .foodst_07_T3 0.055 0.016 3.422 0.001
## .foodst_08_T3 -0.042 0.025 -1.695 0.090
## .foodst_03_T3 ~~
## .foodst_04_T3 0.749 0.026 28.704 0.000
## .foodst_05_T3 0.379 0.019 20.193 0.000
## .foodst_06_T3 -0.257 0.020 -13.134 0.000
## .foodst_07_T3 -0.200 0.017 -12.074 0.000
## .foodst_08_T3 0.161 0.025 6.437 0.000
## .foodst_04_T3 ~~
## .foodst_05_T3 0.508 0.022 23.278 0.000
## .foodst_06_T3 -0.251 0.022 -11.335 0.000
## .foodst_07_T3 -0.206 0.019 -10.956 0.000
## .foodst_08_T3 0.198 0.028 6.941 0.000
## .foodst_05_T3 ~~
## .foodst_06_T3 -0.231 0.017 -13.442 0.000
## .foodst_07_T3 -0.170 0.015 -11.686 0.000
## .foodst_08_T3 0.164 0.022 7.483 0.000
## .foodst_06_T3 ~~
## .foodst_07_T3 0.397 0.017 23.704 0.000
## .foodst_08_T3 -0.063 0.024 -2.685 0.007
## .foodst_07_T3 ~~
## .foodst_08_T3 -0.032 0.020 -1.573 0.116
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 0.000
## .foodst_01_T3 3.202 0.091 35.131 0.000
## .foodst_02_T3 2.555 0.078 32.855 0.000
## .foodst_03_T3 3.700 0.079 46.945 0.000
## .foodst_04_T3 3.343 0.090 37.228 0.000
## .foodst_05_T3 4.439 0.069 64.154 0.000
## .foodst_06_T3 1.423 0.075 19.033 0.000
## .foodst_07_T3 1.550 0.064 24.372 0.000
## .foodst_08_T3 3.398 0.097 34.917 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 66.394 1.564 42.450 0.000
## .foodst_01_T3 1.616 0.038 42.485 0.000
## .foodst_02_T3 1.177 0.028 42.485 0.000
## .foodst_03_T3 1.209 0.028 42.485 0.000
## .foodst_04_T3 1.569 0.037 42.485 0.000
## .foodst_05_T3 0.931 0.022 42.485 0.000
## .foodst_06_T3 1.087 0.026 42.485 0.000
## .foodst_07_T3 0.786 0.019 42.486 0.000
## .foodst_08_T3 1.843 0.043 42.485 0.000
##
##
## Level 2 [country]:
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 8.485 2.226 3.812 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 7.234 4.258 1.699 0.089
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ebndfdst_01_T3 0.085 0.032 2.683 0.007
## ebndfdst_02_T3 0.160 0.039 4.135 0.000
## ebndfdst_03_T3 0.043 0.022 1.979 0.048
## ebndfdst_04_T3 0.050 0.025 2.049 0.040
## ebndfdst_05_T3 0.004 0.006 0.660 0.509
## ebndfdst_06_T3 -0.129 0.035 -3.690 0.000
## ebndfdst_07_T3 -0.000 0.002 -0.210 0.834
## ebndfdst_08_T3 -0.006 0.014 -0.447 0.655parameterEstimates(fit_SEM_edu1, ci=TRUE, level=.99375)## lhs op rhs block level label est
## 1 hds_T3 ~ isced_cat2011_T3 1 1 1.603
## 2 hds_T3 ~ foodst_01_T3 1 1 a1 0.800
## 3 hds_T3 ~ foodst_02_T3 1 1 a2 -0.599
## 4 hds_T3 ~ foodst_03_T3 1 1 a3 0.361
## 5 hds_T3 ~ foodst_04_T3 1 1 a4 0.615
## 6 hds_T3 ~ foodst_05_T3 1 1 a5 -0.128
## 7 hds_T3 ~ foodst_06_T3 1 1 a6 -0.651
## 8 hds_T3 ~ foodst_07_T3 1 1 a7 -0.063
## 9 hds_T3 ~ foodst_08_T3 1 1 a8 0.356
## 10 hds_T3 ~ age_T3 1 1 0.094
## 11 hds_T3 ~ sex_T3 1 1 1.480
## 12 hds_T3 ~ bmi_T3 1 1 0.078
## 13 foodst_01_T3 ~ isced_cat2011_T3 1 1 b1 0.106
## 14 foodst_02_T3 ~ isced_cat2011_T3 1 1 b2 -0.266
## 15 foodst_03_T3 ~ isced_cat2011_T3 1 1 b3 0.120
## 16 foodst_04_T3 ~ isced_cat2011_T3 1 1 b4 0.082
## 17 foodst_05_T3 ~ isced_cat2011_T3 1 1 b5 -0.032
## 18 foodst_06_T3 ~ isced_cat2011_T3 1 1 b6 0.199
## 19 foodst_07_T3 ~ isced_cat2011_T3 1 1 b7 0.006
## 20 foodst_08_T3 ~ isced_cat2011_T3 1 1 b8 -0.017
## 21 foodst_01_T3 ~~ foodst_02_T3 1 1 0.198
## 22 foodst_01_T3 ~~ foodst_03_T3 1 1 0.559
## 23 foodst_01_T3 ~~ foodst_04_T3 1 1 0.564
## 24 foodst_01_T3 ~~ foodst_05_T3 1 1 0.202
## 25 foodst_01_T3 ~~ foodst_06_T3 1 1 -0.137
## 26 foodst_01_T3 ~~ foodst_07_T3 1 1 -0.105
## 27 foodst_01_T3 ~~ foodst_08_T3 1 1 0.142
## 28 foodst_02_T3 ~~ foodst_03_T3 1 1 0.078
## 29 foodst_02_T3 ~~ foodst_04_T3 1 1 0.115
## 30 foodst_02_T3 ~~ foodst_05_T3 1 1 0.056
## 31 foodst_02_T3 ~~ foodst_06_T3 1 1 0.064
## 32 foodst_02_T3 ~~ foodst_07_T3 1 1 0.055
## 33 foodst_02_T3 ~~ foodst_08_T3 1 1 -0.042
## 34 foodst_03_T3 ~~ foodst_04_T3 1 1 0.749
## 35 foodst_03_T3 ~~ foodst_05_T3 1 1 0.379
## 36 foodst_03_T3 ~~ foodst_06_T3 1 1 -0.257
## 37 foodst_03_T3 ~~ foodst_07_T3 1 1 -0.200
## 38 foodst_03_T3 ~~ foodst_08_T3 1 1 0.161
## 39 foodst_04_T3 ~~ foodst_05_T3 1 1 0.508
## 40 foodst_04_T3 ~~ foodst_06_T3 1 1 -0.251
## 41 foodst_04_T3 ~~ foodst_07_T3 1 1 -0.206
## 42 foodst_04_T3 ~~ foodst_08_T3 1 1 0.198
## 43 foodst_05_T3 ~~ foodst_06_T3 1 1 -0.231
## 44 foodst_05_T3 ~~ foodst_07_T3 1 1 -0.170
## 45 foodst_05_T3 ~~ foodst_08_T3 1 1 0.164
## 46 foodst_06_T3 ~~ foodst_07_T3 1 1 0.397
## 47 foodst_06_T3 ~~ foodst_08_T3 1 1 -0.063
## 48 foodst_07_T3 ~~ foodst_08_T3 1 1 -0.032
## 49 hds_T3 ~~ hds_T3 1 1 66.394
## 50 foodst_01_T3 ~~ foodst_01_T3 1 1 1.616
## 51 foodst_02_T3 ~~ foodst_02_T3 1 1 1.177
## 52 foodst_03_T3 ~~ foodst_03_T3 1 1 1.209
## 53 foodst_04_T3 ~~ foodst_04_T3 1 1 1.569
## 54 foodst_05_T3 ~~ foodst_05_T3 1 1 0.931
## 55 foodst_06_T3 ~~ foodst_06_T3 1 1 1.087
## 56 foodst_07_T3 ~~ foodst_07_T3 1 1 0.786
## 57 foodst_08_T3 ~~ foodst_08_T3 1 1 1.843
## 58 isced_cat2011_T3 ~~ isced_cat2011_T3 1 1 0.344
## 59 isced_cat2011_T3 ~~ age_T3 1 1 0.306
## 60 isced_cat2011_T3 ~~ sex_T3 1 1 -0.017
## 61 isced_cat2011_T3 ~~ bmi_T3 1 1 -0.589
## 62 age_T3 ~~ age_T3 1 1 32.026
## 63 age_T3 ~~ sex_T3 1 1 -0.453
## 64 age_T3 ~~ bmi_T3 1 1 2.566
## 65 sex_T3 ~~ sex_T3 1 1 0.124
## 66 sex_T3 ~~ bmi_T3 1 1 -0.236
## 67 bmi_T3 ~~ bmi_T3 1 1 28.520
## 68 hds_T3 ~1 1 1 0.000
## 69 foodst_01_T3 ~1 1 1 3.202
## 70 foodst_02_T3 ~1 1 1 2.555
## 71 foodst_03_T3 ~1 1 1 3.700
## 72 foodst_04_T3 ~1 1 1 3.343
## 73 foodst_05_T3 ~1 1 1 4.439
## 74 foodst_06_T3 ~1 1 1 1.423
## 75 foodst_07_T3 ~1 1 1 1.550
## 76 foodst_08_T3 ~1 1 1 3.398
## 77 isced_cat2011_T3 ~1 1 1 2.458
## 78 age_T3 ~1 1 1 41.760
## 79 sex_T3 ~1 1 1 1.855
## 80 bmi_T3 ~1 1 1 26.173
## 81 hds_T3 ~1 2 2 8.485
## 82 hds_T3 ~~ hds_T3 2 2 7.234
## 83 ebindfoodst_01_T3 := a1*b1 0 0 ebindfoodst_01_T3 0.085
## 84 ebindfoodst_02_T3 := a2*b2 0 0 ebindfoodst_02_T3 0.160
## 85 ebindfoodst_03_T3 := a3*b3 0 0 ebindfoodst_03_T3 0.043
## 86 ebindfoodst_04_T3 := a4*b4 0 0 ebindfoodst_04_T3 0.050
## 87 ebindfoodst_05_T3 := a5*b5 0 0 ebindfoodst_05_T3 0.004
## 88 ebindfoodst_06_T3 := a6*b6 0 0 ebindfoodst_06_T3 -0.129
## 89 ebindfoodst_07_T3 := a7*b7 0 0 ebindfoodst_07_T3 0.000
## 90 ebindfoodst_08_T3 := a8*b8 0 0 ebindfoodst_08_T3 -0.006
## se z pvalue ci.lower ci.upper
## 1 0.243 6.601 0.000 0.939 2.267
## 2 0.119 6.706 0.000 0.474 1.127
## 3 0.127 -4.707 0.000 -0.947 -0.251
## 4 0.156 2.306 0.021 -0.067 0.788
## 5 0.138 4.470 0.000 0.239 0.991
## 6 0.160 -0.797 0.425 -0.565 0.310
## 7 0.147 -4.415 0.000 -1.054 -0.248
## 8 0.172 -0.367 0.714 -0.533 0.407
## 9 0.102 3.503 0.000 0.078 0.633
## 10 0.025 3.767 0.000 0.026 0.162
## 11 0.402 3.679 0.000 0.380 2.580
## 12 0.026 2.959 0.003 0.006 0.150
## 13 0.036 2.928 0.003 0.007 0.204
## 14 0.031 -8.651 0.000 -0.350 -0.182
## 15 0.031 3.856 0.000 0.035 0.206
## 16 0.036 2.306 0.021 -0.015 0.179
## 17 0.027 -1.176 0.240 -0.107 0.043
## 18 0.030 6.720 0.000 0.118 0.280
## 19 0.025 0.256 0.798 -0.062 0.075
## 20 0.039 -0.451 0.652 -0.123 0.088
## 21 0.023 8.550 0.000 0.135 0.262
## 22 0.025 22.325 0.000 0.491 0.628
## 23 0.028 20.067 0.000 0.487 0.641
## 24 0.021 9.769 0.000 0.146 0.259
## 25 0.022 -6.193 0.000 -0.198 -0.077
## 26 0.019 -5.575 0.000 -0.157 -0.054
## 27 0.029 4.934 0.000 0.063 0.221
## 28 0.020 3.932 0.000 0.024 0.133
## 29 0.023 5.048 0.000 0.052 0.177
## 30 0.017 3.182 0.001 0.008 0.103
## 31 0.019 3.377 0.001 0.012 0.115
## 32 0.016 3.422 0.001 0.011 0.099
## 33 0.025 -1.695 0.090 -0.109 0.025
## 34 0.026 28.704 0.000 0.678 0.820
## 35 0.019 20.193 0.000 0.327 0.430
## 36 0.020 -13.134 0.000 -0.310 -0.203
## 37 0.017 -12.074 0.000 -0.245 -0.155
## 38 0.025 6.437 0.000 0.093 0.229
## 39 0.022 23.278 0.000 0.448 0.568
## 40 0.022 -11.335 0.000 -0.311 -0.190
## 41 0.019 -10.956 0.000 -0.257 -0.155
## 42 0.028 6.941 0.000 0.120 0.276
## 43 0.017 -13.442 0.000 -0.278 -0.184
## 44 0.015 -11.686 0.000 -0.209 -0.130
## 45 0.022 7.483 0.000 0.104 0.225
## 46 0.017 23.704 0.000 0.351 0.443
## 47 0.024 -2.685 0.007 -0.128 0.001
## 48 0.020 -1.573 0.116 -0.086 0.023
## 49 1.564 42.450 0.000 62.118 70.671
## 50 0.038 42.485 0.000 1.512 1.720
## 51 0.028 42.485 0.000 1.101 1.252
## 52 0.028 42.485 0.000 1.131 1.287
## 53 0.037 42.485 0.000 1.468 1.670
## 54 0.022 42.485 0.000 0.871 0.991
## 55 0.026 42.485 0.000 1.017 1.157
## 56 0.019 42.486 0.000 0.736 0.837
## 57 0.043 42.485 0.000 1.724 1.962
## 58 0.000 NA NA 0.344 0.344
## 59 0.000 NA NA 0.306 0.306
## 60 0.000 NA NA -0.017 -0.017
## 61 0.000 NA NA -0.589 -0.589
## 62 0.000 NA NA 32.026 32.026
## 63 0.000 NA NA -0.453 -0.453
## 64 0.000 NA NA 2.566 2.566
## 65 0.000 NA NA 0.124 0.124
## 66 0.000 NA NA -0.236 -0.236
## 67 0.000 NA NA 28.520 28.520
## 68 0.000 NA NA 0.000 0.000
## 69 0.091 35.131 0.000 2.953 3.451
## 70 0.078 32.855 0.000 2.342 2.768
## 71 0.079 46.945 0.000 3.485 3.916
## 72 0.090 37.228 0.000 3.097 3.588
## 73 0.069 64.154 0.000 4.250 4.628
## 74 0.075 19.033 0.000 1.218 1.627
## 75 0.064 24.372 0.000 1.376 1.723
## 76 0.097 34.917 0.000 3.132 3.664
## 77 0.000 NA NA 2.458 2.458
## 78 0.000 NA NA 41.760 41.760
## 79 0.000 NA NA 1.855 1.855
## 80 0.000 NA NA 26.173 26.173
## 81 2.226 3.812 0.000 2.398 14.571
## 82 4.258 1.699 0.089 -4.410 18.877
## 83 0.032 2.683 0.007 -0.002 0.171
## 84 0.039 4.135 0.000 0.054 0.265
## 85 0.022 1.979 0.048 -0.017 0.103
## 86 0.025 2.049 0.040 -0.017 0.118
## 87 0.006 0.660 0.509 -0.013 0.021
## 88 0.035 -3.690 0.000 -0.225 -0.033
## 89 0.002 -0.210 0.834 -0.006 0.005
## 90 0.014 -0.447 0.655 -0.044 0.032Controlled 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 114 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 67
##
## Used Total
## Number of observations 3517 3725
## Number of clusters [country] 6
##
## Model Test User Model:
##
## Test statistic 69.849
## 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 0.564 0.101 5.586 0.000
## fds_01_T3 (a1) 0.865 0.121 7.143 0.000
## fds_02_T3 (a2) -0.580 0.129 -4.491 0.000
## fds_03_T3 (a3) 0.348 0.160 2.177 0.029
## fds_04_T3 (a4) 0.696 0.141 4.942 0.000
## fds_05_T3 (a5) -0.079 0.162 -0.486 0.627
## fds_06_T3 (a6) -0.523 0.148 -3.543 0.000
## fds_07_T3 (a7) -0.103 0.173 -0.599 0.549
## fds_08_T3 (a8) 0.358 0.104 3.450 0.001
## age_T3 0.106 0.025 4.215 0.000
## sex_T3 1.524 0.401 3.798 0.000
## bmi_T3 0.068 0.027 2.531 0.011
## foodst_01_T3 ~
## incm_c_T3 (b1) 0.000 0.015 0.021 0.983
## foodst_02_T3 ~
## incm_c_T3 (b2) -0.110 0.013 -8.453 0.000
## foodst_03_T3 ~
## incm_c_T3 (b3) -0.000 0.013 -0.001 0.999
## foodst_04_T3 ~
## incm_c_T3 (b4) -0.050 0.015 -3.323 0.001
## foodst_05_T3 ~
## incm_c_T3 (b5) -0.033 0.012 -2.844 0.004
## foodst_06_T3 ~
## incm_c_T3 (b6) 0.066 0.013 5.246 0.000
## foodst_07_T3 ~
## incm_c_T3 (b7) 0.014 0.011 1.290 0.197
## foodst_08_T3 ~
## incm_c_T3 (b8) -0.035 0.016 -2.183 0.029
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .foodst_01_T3 ~~
## .foodst_02_T3 0.198 0.024 8.434 0.000
## .foodst_03_T3 0.552 0.025 21.767 0.000
## .foodst_04_T3 0.570 0.029 20.007 0.000
## .foodst_05_T3 0.191 0.021 9.129 0.000
## .foodst_06_T3 -0.124 0.023 -5.493 0.000
## .foodst_07_T3 -0.095 0.019 -4.942 0.000
## .foodst_08_T3 0.121 0.029 4.187 0.000
## .foodst_02_T3 ~~
## .foodst_03_T3 0.076 0.020 3.753 0.000
## .foodst_04_T3 0.094 0.023 4.090 0.000
## .foodst_05_T3 0.052 0.018 2.919 0.004
## .foodst_06_T3 0.069 0.019 3.605 0.000
## .foodst_07_T3 0.058 0.016 3.527 0.000
## .foodst_08_T3 -0.045 0.025 -1.826 0.068
## .foodst_03_T3 ~~
## .foodst_04_T3 0.762 0.027 28.728 0.000
## .foodst_05_T3 0.377 0.019 19.843 0.000
## .foodst_06_T3 -0.251 0.020 -12.618 0.000
## .foodst_07_T3 -0.195 0.017 -11.560 0.000
## .foodst_08_T3 0.158 0.025 6.272 0.000
## .foodst_04_T3 ~~
## .foodst_05_T3 0.498 0.022 22.623 0.000
## .foodst_06_T3 -0.232 0.022 -10.307 0.000
## .foodst_07_T3 -0.200 0.019 -10.446 0.000
## .foodst_08_T3 0.186 0.029 6.476 0.000
## .foodst_05_T3 ~~
## .foodst_06_T3 -0.227 0.018 -12.974 0.000
## .foodst_07_T3 -0.166 0.015 -11.263 0.000
## .foodst_08_T3 0.163 0.022 7.361 0.000
## .foodst_06_T3 ~~
## .foodst_07_T3 0.390 0.017 22.791 0.000
## .foodst_08_T3 -0.073 0.024 -3.065 0.002
## .foodst_07_T3 ~~
## .foodst_08_T3 -0.035 0.020 -1.745 0.081
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 0.000
## .foodst_01_T3 3.457 0.051 68.033 0.000
## .foodst_02_T3 2.222 0.043 51.189 0.000
## .foodst_03_T3 4.001 0.044 91.082 0.000
## .foodst_04_T3 3.696 0.050 73.918 0.000
## .foodst_05_T3 4.458 0.039 115.557 0.000
## .foodst_06_T3 1.728 0.042 41.175 0.000
## .foodst_07_T3 1.526 0.036 42.848 0.000
## .foodst_08_T3 3.473 0.054 64.398 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 66.805 1.594 41.898 0.000
## .foodst_01_T3 1.617 0.039 41.934 0.000
## .foodst_02_T3 1.180 0.028 41.934 0.000
## .foodst_03_T3 1.208 0.029 41.934 0.000
## .foodst_04_T3 1.566 0.037 41.935 0.000
## .foodst_05_T3 0.932 0.022 41.935 0.000
## .foodst_06_T3 1.102 0.026 41.935 0.000
## .foodst_07_T3 0.794 0.019 41.934 0.000
## .foodst_08_T3 1.821 0.043 41.934 0.000
##
##
## Level 2 [country]:
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 9.563 2.195 4.357 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 7.190 4.232 1.699 0.089
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ebndfdst_01_T3 0.000 0.013 0.021 0.983
## ebndfdst_02_T3 0.064 0.016 3.966 0.000
## ebndfdst_03_T3 -0.000 0.005 -0.001 0.999
## ebndfdst_04_T3 -0.035 0.013 -2.758 0.006
## ebndfdst_05_T3 0.003 0.005 0.479 0.632
## ebndfdst_06_T3 -0.034 0.012 -2.936 0.003
## ebndfdst_07_T3 -0.001 0.003 -0.544 0.587
## ebndfdst_08_T3 -0.013 0.007 -1.845 0.065parameterEstimates(fit_SEM_inc1, ci=TRUE, level=.99375)## lhs op rhs block level label est
## 1 hds_T3 ~ income_cat_T3 1 1 0.564
## 2 hds_T3 ~ foodst_01_T3 1 1 a1 0.865
## 3 hds_T3 ~ foodst_02_T3 1 1 a2 -0.580
## 4 hds_T3 ~ foodst_03_T3 1 1 a3 0.348
## 5 hds_T3 ~ foodst_04_T3 1 1 a4 0.696
## 6 hds_T3 ~ foodst_05_T3 1 1 a5 -0.079
## 7 hds_T3 ~ foodst_06_T3 1 1 a6 -0.523
## 8 hds_T3 ~ foodst_07_T3 1 1 a7 -0.103
## 9 hds_T3 ~ foodst_08_T3 1 1 a8 0.358
## 10 hds_T3 ~ age_T3 1 1 0.106
## 11 hds_T3 ~ sex_T3 1 1 1.524
## 12 hds_T3 ~ bmi_T3 1 1 0.068
## 13 foodst_01_T3 ~ income_cat_T3 1 1 b1 0.000
## 14 foodst_02_T3 ~ income_cat_T3 1 1 b2 -0.110
## 15 foodst_03_T3 ~ income_cat_T3 1 1 b3 0.000
## 16 foodst_04_T3 ~ income_cat_T3 1 1 b4 -0.050
## 17 foodst_05_T3 ~ income_cat_T3 1 1 b5 -0.033
## 18 foodst_06_T3 ~ income_cat_T3 1 1 b6 0.066
## 19 foodst_07_T3 ~ income_cat_T3 1 1 b7 0.014
## 20 foodst_08_T3 ~ income_cat_T3 1 1 b8 -0.035
## 21 foodst_01_T3 ~~ foodst_02_T3 1 1 0.198
## 22 foodst_01_T3 ~~ foodst_03_T3 1 1 0.552
## 23 foodst_01_T3 ~~ foodst_04_T3 1 1 0.570
## 24 foodst_01_T3 ~~ foodst_05_T3 1 1 0.191
## 25 foodst_01_T3 ~~ foodst_06_T3 1 1 -0.124
## 26 foodst_01_T3 ~~ foodst_07_T3 1 1 -0.095
## 27 foodst_01_T3 ~~ foodst_08_T3 1 1 0.121
## 28 foodst_02_T3 ~~ foodst_03_T3 1 1 0.076
## 29 foodst_02_T3 ~~ foodst_04_T3 1 1 0.094
## 30 foodst_02_T3 ~~ foodst_05_T3 1 1 0.052
## 31 foodst_02_T3 ~~ foodst_06_T3 1 1 0.069
## 32 foodst_02_T3 ~~ foodst_07_T3 1 1 0.058
## 33 foodst_02_T3 ~~ foodst_08_T3 1 1 -0.045
## 34 foodst_03_T3 ~~ foodst_04_T3 1 1 0.762
## 35 foodst_03_T3 ~~ foodst_05_T3 1 1 0.377
## 36 foodst_03_T3 ~~ foodst_06_T3 1 1 -0.251
## 37 foodst_03_T3 ~~ foodst_07_T3 1 1 -0.195
## 38 foodst_03_T3 ~~ foodst_08_T3 1 1 0.158
## 39 foodst_04_T3 ~~ foodst_05_T3 1 1 0.498
## 40 foodst_04_T3 ~~ foodst_06_T3 1 1 -0.232
## 41 foodst_04_T3 ~~ foodst_07_T3 1 1 -0.200
## 42 foodst_04_T3 ~~ foodst_08_T3 1 1 0.186
## 43 foodst_05_T3 ~~ foodst_06_T3 1 1 -0.227
## 44 foodst_05_T3 ~~ foodst_07_T3 1 1 -0.166
## 45 foodst_05_T3 ~~ foodst_08_T3 1 1 0.163
## 46 foodst_06_T3 ~~ foodst_07_T3 1 1 0.390
## 47 foodst_06_T3 ~~ foodst_08_T3 1 1 -0.073
## 48 foodst_07_T3 ~~ foodst_08_T3 1 1 -0.035
## 49 hds_T3 ~~ hds_T3 1 1 66.805
## 50 foodst_01_T3 ~~ foodst_01_T3 1 1 1.617
## 51 foodst_02_T3 ~~ foodst_02_T3 1 1 1.180
## 52 foodst_03_T3 ~~ foodst_03_T3 1 1 1.208
## 53 foodst_04_T3 ~~ foodst_04_T3 1 1 1.566
## 54 foodst_05_T3 ~~ foodst_05_T3 1 1 0.932
## 55 foodst_06_T3 ~~ foodst_06_T3 1 1 1.102
## 56 foodst_07_T3 ~~ foodst_07_T3 1 1 0.794
## 57 foodst_08_T3 ~~ foodst_08_T3 1 1 1.821
## 58 income_cat_T3 ~~ income_cat_T3 1 1 1.987
## 59 income_cat_T3 ~~ age_T3 1 1 0.334
## 60 income_cat_T3 ~~ sex_T3 1 1 -0.038
## 61 income_cat_T3 ~~ bmi_T3 1 1 -1.111
## 62 age_T3 ~~ age_T3 1 1 32.297
## 63 age_T3 ~~ sex_T3 1 1 -0.476
## 64 age_T3 ~~ bmi_T3 1 1 2.594
## 65 sex_T3 ~~ sex_T3 1 1 0.129
## 66 sex_T3 ~~ bmi_T3 1 1 -0.244
## 67 bmi_T3 ~~ bmi_T3 1 1 28.009
## 68 hds_T3 ~1 1 1 0.000
## 69 foodst_01_T3 ~1 1 1 3.457
## 70 foodst_02_T3 ~1 1 1 2.222
## 71 foodst_03_T3 ~1 1 1 4.001
## 72 foodst_04_T3 ~1 1 1 3.696
## 73 foodst_05_T3 ~1 1 1 4.458
## 74 foodst_06_T3 ~1 1 1 1.728
## 75 foodst_07_T3 ~1 1 1 1.526
## 76 foodst_08_T3 ~1 1 1 3.473
## 77 income_cat_T3 ~1 1 1 3.029
## 78 age_T3 ~1 1 1 41.738
## 79 sex_T3 ~1 1 1 1.848
## 80 bmi_T3 ~1 1 1 26.149
## 81 hds_T3 ~1 2 2 9.563
## 82 hds_T3 ~~ hds_T3 2 2 7.190
## 83 ebindfoodst_01_T3 := a1*b1 0 0 ebindfoodst_01_T3 0.000
## 84 ebindfoodst_02_T3 := a2*b2 0 0 ebindfoodst_02_T3 0.064
## 85 ebindfoodst_03_T3 := a3*b3 0 0 ebindfoodst_03_T3 0.000
## 86 ebindfoodst_04_T3 := a4*b4 0 0 ebindfoodst_04_T3 -0.035
## 87 ebindfoodst_05_T3 := a5*b5 0 0 ebindfoodst_05_T3 0.003
## 88 ebindfoodst_06_T3 := a6*b6 0 0 ebindfoodst_06_T3 -0.034
## 89 ebindfoodst_07_T3 := a7*b7 0 0 ebindfoodst_07_T3 -0.001
## 90 ebindfoodst_08_T3 := a8*b8 0 0 ebindfoodst_08_T3 -0.013
## se z pvalue ci.lower ci.upper
## 1 0.101 5.586 0.000 0.288 0.840
## 2 0.121 7.143 0.000 0.534 1.196
## 3 0.129 -4.491 0.000 -0.933 -0.227
## 4 0.160 2.177 0.029 -0.089 0.785
## 5 0.141 4.942 0.000 0.311 1.081
## 6 0.162 -0.486 0.627 -0.522 0.364
## 7 0.148 -3.543 0.000 -0.927 -0.119
## 8 0.173 -0.599 0.549 -0.575 0.368
## 9 0.104 3.450 0.001 0.074 0.641
## 10 0.025 4.215 0.000 0.037 0.175
## 11 0.401 3.798 0.000 0.427 2.622
## 12 0.027 2.531 0.011 -0.005 0.141
## 13 0.015 0.021 0.983 -0.041 0.042
## 14 0.013 -8.453 0.000 -0.145 -0.074
## 15 0.013 -0.001 0.999 -0.036 0.036
## 16 0.015 -3.323 0.001 -0.091 -0.009
## 17 0.012 -2.844 0.004 -0.064 -0.001
## 18 0.013 5.246 0.000 0.032 0.100
## 19 0.011 1.290 0.197 -0.015 0.043
## 20 0.016 -2.183 0.029 -0.079 0.009
## 21 0.024 8.434 0.000 0.134 0.263
## 22 0.025 21.767 0.000 0.482 0.621
## 23 0.029 20.007 0.000 0.492 0.648
## 24 0.021 9.129 0.000 0.134 0.249
## 25 0.023 -5.493 0.000 -0.186 -0.062
## 26 0.019 -4.942 0.000 -0.147 -0.042
## 27 0.029 4.187 0.000 0.042 0.201
## 28 0.020 3.753 0.000 0.021 0.131
## 29 0.023 4.090 0.000 0.031 0.157
## 30 0.018 2.919 0.004 0.003 0.100
## 31 0.019 3.605 0.000 0.017 0.122
## 32 0.016 3.527 0.000 0.013 0.102
## 33 0.025 -1.826 0.068 -0.113 0.022
## 34 0.027 28.728 0.000 0.689 0.834
## 35 0.019 19.843 0.000 0.325 0.429
## 36 0.020 -12.618 0.000 -0.306 -0.197
## 37 0.017 -11.560 0.000 -0.241 -0.149
## 38 0.025 6.272 0.000 0.089 0.227
## 39 0.022 22.623 0.000 0.438 0.559
## 40 0.022 -10.307 0.000 -0.293 -0.170
## 41 0.019 -10.446 0.000 -0.252 -0.147
## 42 0.029 6.476 0.000 0.107 0.264
## 43 0.018 -12.974 0.000 -0.275 -0.179
## 44 0.015 -11.263 0.000 -0.207 -0.126
## 45 0.022 7.361 0.000 0.102 0.223
## 46 0.017 22.791 0.000 0.343 0.436
## 47 0.024 -3.065 0.002 -0.139 -0.008
## 48 0.020 -1.745 0.081 -0.091 0.020
## 49 1.594 41.898 0.000 62.445 71.165
## 50 0.039 41.934 0.000 1.512 1.722
## 51 0.028 41.934 0.000 1.103 1.257
## 52 0.029 41.934 0.000 1.129 1.287
## 53 0.037 41.935 0.000 1.464 1.668
## 54 0.022 41.935 0.000 0.871 0.993
## 55 0.026 41.935 0.000 1.030 1.174
## 56 0.019 41.934 0.000 0.743 0.846
## 57 0.043 41.934 0.000 1.703 1.940
## 58 0.000 NA NA 1.987 1.987
## 59 0.000 NA NA 0.334 0.334
## 60 0.000 NA NA -0.038 -0.038
## 61 0.000 NA NA -1.111 -1.111
## 62 0.000 NA NA 32.297 32.297
## 63 0.000 NA NA -0.476 -0.476
## 64 0.000 NA NA 2.594 2.594
## 65 0.000 NA NA 0.129 0.129
## 66 0.000 NA NA -0.244 -0.244
## 67 0.000 NA NA 28.009 28.009
## 68 0.000 NA NA 0.000 0.000
## 69 0.051 68.033 0.000 3.318 3.596
## 70 0.043 51.189 0.000 2.104 2.341
## 71 0.044 91.082 0.000 3.881 4.121
## 72 0.050 73.918 0.000 3.560 3.833
## 73 0.039 115.557 0.000 4.352 4.563
## 74 0.042 41.175 0.000 1.613 1.842
## 75 0.036 42.848 0.000 1.429 1.624
## 76 0.054 64.398 0.000 3.326 3.621
## 77 0.000 NA NA 3.029 3.029
## 78 0.000 NA NA 41.738 41.738
## 79 0.000 NA NA 1.848 1.848
## 80 0.000 NA NA 26.149 26.149
## 81 2.195 4.357 0.000 3.562 15.564
## 82 4.232 1.699 0.089 -4.383 18.763
## 83 0.013 0.021 0.983 -0.036 0.036
## 84 0.016 3.966 0.000 0.020 0.108
## 85 0.005 -0.001 0.999 -0.013 0.013
## 86 0.013 -2.758 0.006 -0.069 0.000
## 87 0.005 0.479 0.632 -0.012 0.017
## 88 0.012 -2.936 0.003 -0.067 -0.002
## 89 0.003 -0.544 0.587 -0.009 0.006
## 90 0.007 -1.845 0.065 -0.031 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 120 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 67
##
## Number of observations 3725
## Number of clusters [country] 6
##
## Model Test User Model:
##
## Test statistic 103.423
## 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 -0.463 0.397 -1.164 0.244
## fds_01_T3 (a1) 0.861 0.118 7.316 0.000
## fds_02_T3 (a2) -0.704 0.124 -5.684 0.000
## fds_03_T3 (a3) 0.421 0.154 2.736 0.006
## fds_04_T3 (a4) 0.622 0.136 4.567 0.000
## fds_05_T3 (a5) -0.135 0.159 -0.851 0.395
## fds_06_T3 (a6) -0.492 0.144 -3.407 0.001
## fds_07_T3 (a7) -0.107 0.169 -0.634 0.526
## fds_08_T3 (a8) 0.350 0.100 3.483 0.000
## age_T3 0.111 0.024 4.558 0.000
## sex_T3 1.233 0.394 3.131 0.002
## bmi_T3 0.049 0.026 1.890 0.059
## foodst_01_T3 ~
## migration (b1) 0.091 0.062 1.471 0.141
## foodst_02_T3 ~
## migration (b2) 0.137 0.053 2.567 0.010
## foodst_03_T3 ~
## migration (b3) 0.018 0.053 0.335 0.738
## foodst_04_T3 ~
## migration (b4) 0.161 0.060 2.659 0.008
## foodst_05_T3 ~
## migration (b5) -0.010 0.046 -0.209 0.835
## foodst_06_T3 ~
## migration (b6) -0.098 0.051 -1.929 0.054
## foodst_07_T3 ~
## migration (b7) -0.001 0.043 -0.023 0.981
## foodst_08_T3 ~
## migration (b8) 0.097 0.066 1.471 0.141
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .foodst_01_T3 ~~
## .foodst_02_T3 0.197 0.023 8.475 0.000
## .foodst_03_T3 0.561 0.025 22.589 0.000
## .foodst_04_T3 0.569 0.028 20.486 0.000
## .foodst_05_T3 0.194 0.020 9.535 0.000
## .foodst_06_T3 -0.128 0.022 -5.830 0.000
## .foodst_07_T3 -0.102 0.019 -5.451 0.000
## .foodst_08_T3 0.128 0.028 4.483 0.000
## .foodst_02_T3 ~~
## .foodst_03_T3 0.069 0.020 3.461 0.001
## .foodst_04_T3 0.110 0.023 4.840 0.000
## .foodst_05_T3 0.062 0.017 3.535 0.000
## .foodst_06_T3 0.053 0.019 2.799 0.005
## .foodst_07_T3 0.057 0.016 3.567 0.000
## .foodst_08_T3 -0.047 0.025 -1.906 0.057
## .foodst_03_T3 ~~
## .foodst_04_T3 0.751 0.026 29.158 0.000
## .foodst_05_T3 0.373 0.018 20.230 0.000
## .foodst_06_T3 -0.246 0.019 -12.680 0.000
## .foodst_07_T3 -0.194 0.016 -11.850 0.000
## .foodst_08_T3 0.159 0.025 6.439 0.000
## .foodst_04_T3 ~~
## .foodst_05_T3 0.500 0.021 23.393 0.000
## .foodst_06_T3 -0.237 0.022 -10.835 0.000
## .foodst_07_T3 -0.204 0.019 -10.996 0.000
## .foodst_08_T3 0.192 0.028 6.835 0.000
## .foodst_05_T3 ~~
## .foodst_06_T3 -0.229 0.017 -13.507 0.000
## .foodst_07_T3 -0.166 0.014 -11.646 0.000
## .foodst_08_T3 0.167 0.022 7.729 0.000
## .foodst_06_T3 ~~
## .foodst_07_T3 0.394 0.017 23.762 0.000
## .foodst_08_T3 -0.064 0.023 -2.741 0.006
## .foodst_07_T3 ~~
## .foodst_08_T3 -0.034 0.020 -1.723 0.085
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 0.000
## .foodst_01_T3 3.361 0.073 46.128 0.000
## .foodst_02_T3 1.749 0.063 27.749 0.000
## .foodst_03_T3 3.978 0.063 63.144 0.000
## .foodst_04_T3 3.375 0.072 47.177 0.000
## .foodst_05_T3 4.378 0.055 79.722 0.000
## .foodst_06_T3 2.019 0.060 33.698 0.000
## .foodst_07_T3 1.563 0.051 30.813 0.000
## .foodst_08_T3 3.256 0.078 41.974 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 67.110 1.556 43.121 0.000
## .foodst_01_T3 1.628 0.038 43.157 0.000
## .foodst_02_T3 1.217 0.028 43.157 0.000
## .foodst_03_T3 1.217 0.028 43.156 0.000
## .foodst_04_T3 1.568 0.036 43.157 0.000
## .foodst_05_T3 0.924 0.021 43.157 0.000
## .foodst_06_T3 1.101 0.026 43.157 0.000
## .foodst_07_T3 0.788 0.018 43.157 0.000
## .foodst_08_T3 1.845 0.043 43.157 0.000
##
##
## Level 2 [country]:
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 12.947 2.160 5.994 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 7.434 4.370 1.701 0.089
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ebndfdst_01_T3 0.078 0.054 1.442 0.149
## ebndfdst_02_T3 -0.096 0.041 -2.339 0.019
## ebndfdst_03_T3 0.008 0.023 0.332 0.740
## ebndfdst_04_T3 0.100 0.044 2.298 0.022
## ebndfdst_05_T3 0.001 0.006 0.203 0.839
## ebndfdst_06_T3 0.048 0.029 1.679 0.093
## ebndfdst_07_T3 0.000 0.005 0.023 0.981
## ebndfdst_08_T3 0.034 0.025 1.355 0.175parameterEstimates(fit_SEM_mig, ci=TRUE, level=.99375)## lhs op rhs block level label est se
## 1 hds_T3 ~ migration 1 1 -0.463 0.397
## 2 hds_T3 ~ foodst_01_T3 1 1 a1 0.861 0.118
## 3 hds_T3 ~ foodst_02_T3 1 1 a2 -0.704 0.124
## 4 hds_T3 ~ foodst_03_T3 1 1 a3 0.421 0.154
## 5 hds_T3 ~ foodst_04_T3 1 1 a4 0.622 0.136
## 6 hds_T3 ~ foodst_05_T3 1 1 a5 -0.135 0.159
## 7 hds_T3 ~ foodst_06_T3 1 1 a6 -0.492 0.144
## 8 hds_T3 ~ foodst_07_T3 1 1 a7 -0.107 0.169
## 9 hds_T3 ~ foodst_08_T3 1 1 a8 0.350 0.100
## 10 hds_T3 ~ age_T3 1 1 0.111 0.024
## 11 hds_T3 ~ sex_T3 1 1 1.233 0.394
## 12 hds_T3 ~ bmi_T3 1 1 0.049 0.026
## 13 foodst_01_T3 ~ migration 1 1 b1 0.091 0.062
## 14 foodst_02_T3 ~ migration 1 1 b2 0.137 0.053
## 15 foodst_03_T3 ~ migration 1 1 b3 0.018 0.053
## 16 foodst_04_T3 ~ migration 1 1 b4 0.161 0.060
## 17 foodst_05_T3 ~ migration 1 1 b5 -0.010 0.046
## 18 foodst_06_T3 ~ migration 1 1 b6 -0.098 0.051
## 19 foodst_07_T3 ~ migration 1 1 b7 -0.001 0.043
## 20 foodst_08_T3 ~ migration 1 1 b8 0.097 0.066
## 21 foodst_01_T3 ~~ foodst_02_T3 1 1 0.197 0.023
## 22 foodst_01_T3 ~~ foodst_03_T3 1 1 0.561 0.025
## 23 foodst_01_T3 ~~ foodst_04_T3 1 1 0.569 0.028
## 24 foodst_01_T3 ~~ foodst_05_T3 1 1 0.194 0.020
## 25 foodst_01_T3 ~~ foodst_06_T3 1 1 -0.128 0.022
## 26 foodst_01_T3 ~~ foodst_07_T3 1 1 -0.102 0.019
## 27 foodst_01_T3 ~~ foodst_08_T3 1 1 0.128 0.028
## 28 foodst_02_T3 ~~ foodst_03_T3 1 1 0.069 0.020
## 29 foodst_02_T3 ~~ foodst_04_T3 1 1 0.110 0.023
## 30 foodst_02_T3 ~~ foodst_05_T3 1 1 0.062 0.017
## 31 foodst_02_T3 ~~ foodst_06_T3 1 1 0.053 0.019
## 32 foodst_02_T3 ~~ foodst_07_T3 1 1 0.057 0.016
## 33 foodst_02_T3 ~~ foodst_08_T3 1 1 -0.047 0.025
## 34 foodst_03_T3 ~~ foodst_04_T3 1 1 0.751 0.026
## 35 foodst_03_T3 ~~ foodst_05_T3 1 1 0.373 0.018
## 36 foodst_03_T3 ~~ foodst_06_T3 1 1 -0.246 0.019
## 37 foodst_03_T3 ~~ foodst_07_T3 1 1 -0.194 0.016
## 38 foodst_03_T3 ~~ foodst_08_T3 1 1 0.159 0.025
## 39 foodst_04_T3 ~~ foodst_05_T3 1 1 0.500 0.021
## 40 foodst_04_T3 ~~ foodst_06_T3 1 1 -0.237 0.022
## 41 foodst_04_T3 ~~ foodst_07_T3 1 1 -0.204 0.019
## 42 foodst_04_T3 ~~ foodst_08_T3 1 1 0.192 0.028
## 43 foodst_05_T3 ~~ foodst_06_T3 1 1 -0.229 0.017
## 44 foodst_05_T3 ~~ foodst_07_T3 1 1 -0.166 0.014
## 45 foodst_05_T3 ~~ foodst_08_T3 1 1 0.167 0.022
## 46 foodst_06_T3 ~~ foodst_07_T3 1 1 0.394 0.017
## 47 foodst_06_T3 ~~ foodst_08_T3 1 1 -0.064 0.023
## 48 foodst_07_T3 ~~ foodst_08_T3 1 1 -0.034 0.020
## 49 hds_T3 ~~ hds_T3 1 1 67.110 1.556
## 50 foodst_01_T3 ~~ foodst_01_T3 1 1 1.628 0.038
## 51 foodst_02_T3 ~~ foodst_02_T3 1 1 1.217 0.028
## 52 foodst_03_T3 ~~ foodst_03_T3 1 1 1.217 0.028
## 53 foodst_04_T3 ~~ foodst_04_T3 1 1 1.568 0.036
## 54 foodst_05_T3 ~~ foodst_05_T3 1 1 0.924 0.021
## 55 foodst_06_T3 ~~ foodst_06_T3 1 1 1.101 0.026
## 56 foodst_07_T3 ~~ foodst_07_T3 1 1 0.788 0.018
## 57 foodst_08_T3 ~~ foodst_08_T3 1 1 1.845 0.043
## 58 migration ~~ migration 1 1 0.115 0.000
## 59 migration ~~ age_T3 1 1 0.040 0.000
## 60 migration ~~ sex_T3 1 1 -0.004 0.000
## 61 migration ~~ bmi_T3 1 1 0.062 0.000
## 62 age_T3 ~~ age_T3 1 1 32.288 0.000
## 63 age_T3 ~~ sex_T3 1 1 -0.464 0.000
## 64 age_T3 ~~ bmi_T3 1 1 2.609 0.000
## 65 sex_T3 ~~ sex_T3 1 1 0.126 0.000
## 66 sex_T3 ~~ bmi_T3 1 1 -0.235 0.000
## 67 bmi_T3 ~~ bmi_T3 1 1 28.240 0.000
## 68 hds_T3 ~1 1 1 0.000 0.000
## 69 foodst_01_T3 ~1 1 1 3.361 0.073
## 70 foodst_02_T3 ~1 1 1 1.749 0.063
## 71 foodst_03_T3 ~1 1 1 3.978 0.063
## 72 foodst_04_T3 ~1 1 1 3.375 0.072
## 73 foodst_05_T3 ~1 1 1 4.378 0.055
## 74 foodst_06_T3 ~1 1 1 2.019 0.060
## 75 foodst_07_T3 ~1 1 1 1.563 0.051
## 76 foodst_08_T3 ~1 1 1 3.256 0.078
## 77 migration ~1 1 1 1.133 0.000
## 78 age_T3 ~1 1 1 41.772 0.000
## 79 sex_T3 ~1 1 1 1.852 0.000
## 80 bmi_T3 ~1 1 1 26.174 0.000
## 81 hds_T3 ~1 2 2 12.947 2.160
## 82 hds_T3 ~~ hds_T3 2 2 7.434 4.370
## 83 ebindfoodst_01_T3 := a1*b1 0 0 ebindfoodst_01_T3 0.078 0.054
## 84 ebindfoodst_02_T3 := a2*b2 0 0 ebindfoodst_02_T3 -0.096 0.041
## 85 ebindfoodst_03_T3 := a3*b3 0 0 ebindfoodst_03_T3 0.008 0.023
## 86 ebindfoodst_04_T3 := a4*b4 0 0 ebindfoodst_04_T3 0.100 0.044
## 87 ebindfoodst_05_T3 := a5*b5 0 0 ebindfoodst_05_T3 0.001 0.006
## 88 ebindfoodst_06_T3 := a6*b6 0 0 ebindfoodst_06_T3 0.048 0.029
## 89 ebindfoodst_07_T3 := a7*b7 0 0 ebindfoodst_07_T3 0.000 0.005
## 90 ebindfoodst_08_T3 := a8*b8 0 0 ebindfoodst_08_T3 0.034 0.025
## z pvalue ci.lower ci.upper
## 1 -1.164 0.244 -1.549 0.624
## 2 7.316 0.000 0.539 1.183
## 3 -5.684 0.000 -1.043 -0.366
## 4 2.736 0.006 0.000 0.843
## 5 4.567 0.000 0.250 0.994
## 6 -0.851 0.395 -0.569 0.299
## 7 -3.407 0.001 -0.886 -0.097
## 8 -0.634 0.526 -0.570 0.356
## 9 3.483 0.000 0.075 0.624
## 10 4.558 0.000 0.045 0.178
## 11 3.131 0.002 0.156 2.309
## 12 1.890 0.059 -0.022 0.119
## 13 1.471 0.141 -0.078 0.259
## 14 2.567 0.010 -0.009 0.283
## 15 0.335 0.738 -0.128 0.164
## 16 2.659 0.008 -0.005 0.326
## 17 -0.209 0.835 -0.137 0.117
## 18 -1.929 0.054 -0.236 0.041
## 19 -0.023 0.981 -0.118 0.116
## 20 1.471 0.141 -0.083 0.276
## 21 8.475 0.000 0.134 0.261
## 22 22.589 0.000 0.493 0.629
## 23 20.486 0.000 0.493 0.645
## 24 9.535 0.000 0.138 0.250
## 25 -5.830 0.000 -0.189 -0.068
## 26 -5.451 0.000 -0.153 -0.051
## 27 4.483 0.000 0.050 0.205
## 28 3.461 0.001 0.015 0.124
## 29 4.840 0.000 0.048 0.172
## 30 3.535 0.000 0.014 0.109
## 31 2.799 0.005 0.001 0.105
## 32 3.567 0.000 0.013 0.101
## 33 -1.906 0.057 -0.114 0.020
## 34 29.158 0.000 0.681 0.822
## 35 20.230 0.000 0.322 0.423
## 36 -12.680 0.000 -0.299 -0.193
## 37 -11.850 0.000 -0.239 -0.149
## 38 6.439 0.000 0.091 0.226
## 39 23.393 0.000 0.441 0.558
## 40 -10.835 0.000 -0.297 -0.177
## 41 -10.996 0.000 -0.254 -0.153
## 42 6.835 0.000 0.115 0.268
## 43 -13.507 0.000 -0.275 -0.183
## 44 -11.646 0.000 -0.205 -0.127
## 45 7.729 0.000 0.108 0.226
## 46 23.762 0.000 0.348 0.439
## 47 -2.741 0.006 -0.128 0.000
## 48 -1.723 0.085 -0.088 0.020
## 49 43.121 0.000 62.854 71.365
## 50 43.157 0.000 1.524 1.731
## 51 43.157 0.000 1.140 1.294
## 52 43.156 0.000 1.140 1.294
## 53 43.157 0.000 1.469 1.668
## 54 43.157 0.000 0.866 0.983
## 55 43.157 0.000 1.031 1.171
## 56 43.157 0.000 0.739 0.838
## 57 43.157 0.000 1.728 1.962
## 58 NA NA 0.115 0.115
## 59 NA NA 0.040 0.040
## 60 NA NA -0.004 -0.004
## 61 NA NA 0.062 0.062
## 62 NA NA 32.288 32.288
## 63 NA NA -0.464 -0.464
## 64 NA NA 2.609 2.609
## 65 NA NA 0.126 0.126
## 66 NA NA -0.235 -0.235
## 67 NA NA 28.240 28.240
## 68 NA NA 0.000 0.000
## 69 46.128 0.000 3.162 3.560
## 70 27.749 0.000 1.576 1.921
## 71 63.144 0.000 3.806 4.151
## 72 47.177 0.000 3.179 3.570
## 73 79.722 0.000 4.228 4.528
## 74 33.698 0.000 1.856 2.183
## 75 30.813 0.000 1.424 1.701
## 76 41.974 0.000 3.044 3.468
## 77 NA NA 1.133 1.133
## 78 NA NA 41.772 41.772
## 79 NA NA 1.852 1.852
## 80 NA NA 26.174 26.174
## 81 5.994 0.000 7.041 18.853
## 82 1.701 0.089 -4.515 19.383
## 83 1.442 0.149 -0.070 0.226
## 84 -2.339 0.019 -0.209 0.016
## 85 0.332 0.740 -0.054 0.069
## 86 2.298 0.022 -0.019 0.219
## 87 0.203 0.839 -0.016 0.019
## 88 1.679 0.093 -0.030 0.126
## 89 0.023 0.981 -0.012 0.013
## 90 1.355 0.175 -0.034 0.102Controlled 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 141 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 67
##
## Number of observations 3725
## Number of clusters [country] 6
##
## Model Test User Model:
##
## Test statistic 101.730
## 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 -1.525 0.471 -3.240 0.001
## fds_01_T3 (a1) 0.856 0.118 7.279 0.000
## fds_02_T3 (a2) -0.704 0.124 -5.687 0.000
## fds_03_T3 (a3) 0.418 0.154 2.720 0.007
## fds_04_T3 (a4) 0.630 0.136 4.633 0.000
## fds_05_T3 (a5) -0.125 0.158 -0.787 0.431
## fds_06_T3 (a6) -0.499 0.144 -3.463 0.001
## fds_07_T3 (a7) -0.113 0.169 -0.669 0.503
## fds_08_T3 (a8) 0.343 0.100 3.423 0.001
## age_T3 0.110 0.024 4.485 0.000
## sex_T3 1.280 0.393 3.256 0.001
## bmi_T3 0.055 0.026 2.119 0.034
## foodst_01_T3 ~
## unemploy (b1) 0.007 0.073 0.097 0.922
## foodst_02_T3 ~
## unemploy (b2) 0.087 0.063 1.371 0.171
## foodst_03_T3 ~
## unemploy (b3) 0.034 0.063 0.543 0.587
## foodst_04_T3 ~
## unemploy (b4) 0.167 0.072 2.326 0.020
## foodst_05_T3 ~
## unemploy (b5) 0.101 0.055 1.832 0.067
## foodst_06_T3 ~
## unemploy (b6) -0.155 0.060 -2.581 0.010
## foodst_07_T3 ~
## unemploy (b7) -0.071 0.051 -1.398 0.162
## foodst_08_T3 ~
## unemploy (b8) -0.009 0.078 -0.122 0.903
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .foodst_01_T3 ~~
## .foodst_02_T3 0.199 0.023 8.526 0.000
## .foodst_03_T3 0.561 0.025 22.590 0.000
## .foodst_04_T3 0.571 0.028 20.527 0.000
## .foodst_05_T3 0.194 0.020 9.529 0.000
## .foodst_06_T3 -0.129 0.022 -5.872 0.000
## .foodst_07_T3 -0.102 0.019 -5.450 0.000
## .foodst_08_T3 0.129 0.028 4.516 0.000
## .foodst_02_T3 ~~
## .foodst_03_T3 0.069 0.020 3.460 0.001
## .foodst_04_T3 0.111 0.023 4.895 0.000
## .foodst_05_T3 0.061 0.017 3.484 0.000
## .foodst_06_T3 0.053 0.019 2.775 0.006
## .foodst_07_T3 0.058 0.016 3.596 0.000
## .foodst_08_T3 -0.045 0.025 -1.839 0.066
## .foodst_03_T3 ~~
## .foodst_04_T3 0.751 0.026 29.150 0.000
## .foodst_05_T3 0.372 0.018 20.224 0.000
## .foodst_06_T3 -0.246 0.019 -12.673 0.000
## .foodst_07_T3 -0.194 0.016 -11.842 0.000
## .foodst_08_T3 0.159 0.025 6.446 0.000
## .foodst_04_T3 ~~
## .foodst_05_T3 0.498 0.021 23.335 0.000
## .foodst_06_T3 -0.237 0.022 -10.823 0.000
## .foodst_07_T3 -0.203 0.019 -10.947 0.000
## .foodst_08_T3 0.194 0.028 6.898 0.000
## .foodst_05_T3 ~~
## .foodst_06_T3 -0.228 0.017 -13.440 0.000
## .foodst_07_T3 -0.165 0.014 -11.614 0.000
## .foodst_08_T3 0.167 0.022 7.728 0.000
## .foodst_06_T3 ~~
## .foodst_07_T3 0.393 0.017 23.729 0.000
## .foodst_08_T3 -0.065 0.023 -2.792 0.005
## .foodst_07_T3 ~~
## .foodst_08_T3 -0.034 0.020 -1.726 0.084
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 0.000
## .foodst_01_T3 3.456 0.082 42.008 0.000
## .foodst_02_T3 1.809 0.071 25.420 0.000
## .foodst_03_T3 3.961 0.071 55.701 0.000
## .foodst_04_T3 3.375 0.081 41.793 0.000
## .foodst_05_T3 4.257 0.062 68.710 0.000
## .foodst_06_T3 2.077 0.068 30.724 0.000
## .foodst_07_T3 1.639 0.057 28.637 0.000
## .foodst_08_T3 3.376 0.088 38.542 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 66.944 1.552 43.122 0.000
## .foodst_01_T3 1.629 0.038 43.157 0.000
## .foodst_02_T3 1.219 0.028 43.157 0.000
## .foodst_03_T3 1.217 0.028 43.157 0.000
## .foodst_04_T3 1.569 0.036 43.157 0.000
## .foodst_05_T3 0.923 0.021 43.157 0.000
## .foodst_06_T3 1.100 0.025 43.157 0.000
## .foodst_07_T3 0.788 0.018 43.157 0.000
## .foodst_08_T3 1.846 0.043 43.157 0.000
##
##
## Level 2 [country]:
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 13.922 2.163 6.438 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 7.435 4.370 1.701 0.089
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ebndfdst_01_T3 0.006 0.062 0.097 0.922
## ebndfdst_02_T3 -0.061 0.046 -1.332 0.183
## ebndfdst_03_T3 0.014 0.027 0.532 0.594
## ebndfdst_04_T3 0.105 0.050 2.079 0.038
## ebndfdst_05_T3 -0.013 0.017 -0.723 0.470
## ebndfdst_06_T3 0.077 0.037 2.069 0.039
## ebndfdst_07_T3 0.008 0.013 0.604 0.546
## ebndfdst_08_T3 -0.003 0.027 -0.122 0.903parameterEstimates(fit_SEM_une, ci=TRUE, level=.99375)## lhs op rhs block level label est se
## 1 hds_T3 ~ unemploy 1 1 -1.525 0.471
## 2 hds_T3 ~ foodst_01_T3 1 1 a1 0.856 0.118
## 3 hds_T3 ~ foodst_02_T3 1 1 a2 -0.704 0.124
## 4 hds_T3 ~ foodst_03_T3 1 1 a3 0.418 0.154
## 5 hds_T3 ~ foodst_04_T3 1 1 a4 0.630 0.136
## 6 hds_T3 ~ foodst_05_T3 1 1 a5 -0.125 0.158
## 7 hds_T3 ~ foodst_06_T3 1 1 a6 -0.499 0.144
## 8 hds_T3 ~ foodst_07_T3 1 1 a7 -0.113 0.169
## 9 hds_T3 ~ foodst_08_T3 1 1 a8 0.343 0.100
## 10 hds_T3 ~ age_T3 1 1 0.110 0.024
## 11 hds_T3 ~ sex_T3 1 1 1.280 0.393
## 12 hds_T3 ~ bmi_T3 1 1 0.055 0.026
## 13 foodst_01_T3 ~ unemploy 1 1 b1 0.007 0.073
## 14 foodst_02_T3 ~ unemploy 1 1 b2 0.087 0.063
## 15 foodst_03_T3 ~ unemploy 1 1 b3 0.034 0.063
## 16 foodst_04_T3 ~ unemploy 1 1 b4 0.167 0.072
## 17 foodst_05_T3 ~ unemploy 1 1 b5 0.101 0.055
## 18 foodst_06_T3 ~ unemploy 1 1 b6 -0.155 0.060
## 19 foodst_07_T3 ~ unemploy 1 1 b7 -0.071 0.051
## 20 foodst_08_T3 ~ unemploy 1 1 b8 -0.009 0.078
## 21 foodst_01_T3 ~~ foodst_02_T3 1 1 0.199 0.023
## 22 foodst_01_T3 ~~ foodst_03_T3 1 1 0.561 0.025
## 23 foodst_01_T3 ~~ foodst_04_T3 1 1 0.571 0.028
## 24 foodst_01_T3 ~~ foodst_05_T3 1 1 0.194 0.020
## 25 foodst_01_T3 ~~ foodst_06_T3 1 1 -0.129 0.022
## 26 foodst_01_T3 ~~ foodst_07_T3 1 1 -0.102 0.019
## 27 foodst_01_T3 ~~ foodst_08_T3 1 1 0.129 0.028
## 28 foodst_02_T3 ~~ foodst_03_T3 1 1 0.069 0.020
## 29 foodst_02_T3 ~~ foodst_04_T3 1 1 0.111 0.023
## 30 foodst_02_T3 ~~ foodst_05_T3 1 1 0.061 0.017
## 31 foodst_02_T3 ~~ foodst_06_T3 1 1 0.053 0.019
## 32 foodst_02_T3 ~~ foodst_07_T3 1 1 0.058 0.016
## 33 foodst_02_T3 ~~ foodst_08_T3 1 1 -0.045 0.025
## 34 foodst_03_T3 ~~ foodst_04_T3 1 1 0.751 0.026
## 35 foodst_03_T3 ~~ foodst_05_T3 1 1 0.372 0.018
## 36 foodst_03_T3 ~~ foodst_06_T3 1 1 -0.246 0.019
## 37 foodst_03_T3 ~~ foodst_07_T3 1 1 -0.194 0.016
## 38 foodst_03_T3 ~~ foodst_08_T3 1 1 0.159 0.025
## 39 foodst_04_T3 ~~ foodst_05_T3 1 1 0.498 0.021
## 40 foodst_04_T3 ~~ foodst_06_T3 1 1 -0.237 0.022
## 41 foodst_04_T3 ~~ foodst_07_T3 1 1 -0.203 0.019
## 42 foodst_04_T3 ~~ foodst_08_T3 1 1 0.194 0.028
## 43 foodst_05_T3 ~~ foodst_06_T3 1 1 -0.228 0.017
## 44 foodst_05_T3 ~~ foodst_07_T3 1 1 -0.165 0.014
## 45 foodst_05_T3 ~~ foodst_08_T3 1 1 0.167 0.022
## 46 foodst_06_T3 ~~ foodst_07_T3 1 1 0.393 0.017
## 47 foodst_06_T3 ~~ foodst_08_T3 1 1 -0.065 0.023
## 48 foodst_07_T3 ~~ foodst_08_T3 1 1 -0.034 0.020
## 49 hds_T3 ~~ hds_T3 1 1 66.944 1.552
## 50 foodst_01_T3 ~~ foodst_01_T3 1 1 1.629 0.038
## 51 foodst_02_T3 ~~ foodst_02_T3 1 1 1.219 0.028
## 52 foodst_03_T3 ~~ foodst_03_T3 1 1 1.217 0.028
## 53 foodst_04_T3 ~~ foodst_04_T3 1 1 1.569 0.036
## 54 foodst_05_T3 ~~ foodst_05_T3 1 1 0.923 0.021
## 55 foodst_06_T3 ~~ foodst_06_T3 1 1 1.100 0.025
## 56 foodst_07_T3 ~~ foodst_07_T3 1 1 0.788 0.018
## 57 foodst_08_T3 ~~ foodst_08_T3 1 1 1.846 0.043
## 58 unemploy ~~ unemploy 1 1 0.082 0.000
## 59 unemploy ~~ age_T3 1 1 -0.028 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 32.288 0.000
## 63 age_T3 ~~ sex_T3 1 1 -0.464 0.000
## 64 age_T3 ~~ bmi_T3 1 1 2.609 0.000
## 65 sex_T3 ~~ sex_T3 1 1 0.126 0.000
## 66 sex_T3 ~~ bmi_T3 1 1 -0.235 0.000
## 67 bmi_T3 ~~ bmi_T3 1 1 28.240 0.000
## 68 hds_T3 ~1 1 1 0.000 0.000
## 69 foodst_01_T3 ~1 1 1 3.456 0.082
## 70 foodst_02_T3 ~1 1 1 1.809 0.071
## 71 foodst_03_T3 ~1 1 1 3.961 0.071
## 72 foodst_04_T3 ~1 1 1 3.375 0.081
## 73 foodst_05_T3 ~1 1 1 4.257 0.062
## 74 foodst_06_T3 ~1 1 1 2.077 0.068
## 75 foodst_07_T3 ~1 1 1 1.639 0.057
## 76 foodst_08_T3 ~1 1 1 3.376 0.088
## 77 unemploy ~1 1 1 1.090 0.000
## 78 age_T3 ~1 1 1 41.772 0.000
## 79 sex_T3 ~1 1 1 1.852 0.000
## 80 bmi_T3 ~1 1 1 26.174 0.000
## 81 hds_T3 ~1 2 2 13.922 2.163
## 82 hds_T3 ~~ hds_T3 2 2 7.435 4.370
## 83 ebindfoodst_01_T3 := a1*b1 0 0 ebindfoodst_01_T3 0.006 0.062
## 84 ebindfoodst_02_T3 := a2*b2 0 0 ebindfoodst_02_T3 -0.061 0.046
## 85 ebindfoodst_03_T3 := a3*b3 0 0 ebindfoodst_03_T3 0.014 0.027
## 86 ebindfoodst_04_T3 := a4*b4 0 0 ebindfoodst_04_T3 0.105 0.050
## 87 ebindfoodst_05_T3 := a5*b5 0 0 ebindfoodst_05_T3 -0.013 0.017
## 88 ebindfoodst_06_T3 := a6*b6 0 0 ebindfoodst_06_T3 0.077 0.037
## 89 ebindfoodst_07_T3 := a7*b7 0 0 ebindfoodst_07_T3 0.008 0.013
## 90 ebindfoodst_08_T3 := a8*b8 0 0 ebindfoodst_08_T3 -0.003 0.027
## z pvalue ci.lower ci.upper
## 1 -3.240 0.001 -2.812 -0.238
## 2 7.279 0.000 0.534 1.177
## 3 -5.687 0.000 -1.042 -0.365
## 4 2.720 0.007 -0.002 0.839
## 5 4.633 0.000 0.258 1.002
## 6 -0.787 0.431 -0.558 0.308
## 7 -3.463 0.001 -0.893 -0.105
## 8 -0.669 0.503 -0.575 0.349
## 9 3.423 0.001 0.069 0.617
## 10 4.485 0.000 0.043 0.176
## 11 3.256 0.001 0.205 2.356
## 12 2.119 0.034 -0.016 0.125
## 13 0.097 0.922 -0.192 0.207
## 14 1.371 0.171 -0.086 0.259
## 15 0.543 0.587 -0.138 0.207
## 16 2.326 0.020 -0.029 0.363
## 17 1.832 0.067 -0.050 0.251
## 18 -2.581 0.010 -0.319 0.009
## 19 -1.398 0.162 -0.210 0.068
## 20 -0.122 0.903 -0.222 0.203
## 21 8.526 0.000 0.135 0.262
## 22 22.590 0.000 0.493 0.629
## 23 20.527 0.000 0.495 0.647
## 24 9.529 0.000 0.138 0.249
## 25 -5.872 0.000 -0.190 -0.069
## 26 -5.450 0.000 -0.153 -0.051
## 27 4.516 0.000 0.051 0.207
## 28 3.460 0.001 0.015 0.124
## 29 4.895 0.000 0.049 0.173
## 30 3.484 0.000 0.013 0.108
## 31 2.775 0.006 0.001 0.105
## 32 3.596 0.000 0.014 0.102
## 33 -1.839 0.066 -0.112 0.022
## 34 29.150 0.000 0.681 0.822
## 35 20.224 0.000 0.322 0.423
## 36 -12.673 0.000 -0.299 -0.193
## 37 -11.842 0.000 -0.238 -0.149
## 38 6.446 0.000 0.092 0.227
## 39 23.335 0.000 0.440 0.556
## 40 -10.823 0.000 -0.297 -0.177
## 41 -10.947 0.000 -0.253 -0.152
## 42 6.898 0.000 0.117 0.270
## 43 -13.440 0.000 -0.274 -0.181
## 44 -11.614 0.000 -0.204 -0.126
## 45 7.728 0.000 0.108 0.226
## 46 23.729 0.000 0.348 0.438
## 47 -2.792 0.005 -0.129 -0.001
## 48 -1.726 0.084 -0.088 0.020
## 49 43.122 0.000 62.699 71.189
## 50 43.157 0.000 1.525 1.732
## 51 43.157 0.000 1.142 1.296
## 52 43.157 0.000 1.140 1.294
## 53 43.157 0.000 1.470 1.668
## 54 43.157 0.000 0.865 0.982
## 55 43.157 0.000 1.030 1.170
## 56 43.157 0.000 0.738 0.838
## 57 43.157 0.000 1.729 1.963
## 58 NA NA 0.082 0.082
## 59 NA NA -0.028 -0.028
## 60 NA NA 0.002 0.002
## 61 NA NA 0.120 0.120
## 62 NA NA 32.288 32.288
## 63 NA NA -0.464 -0.464
## 64 NA NA 2.609 2.609
## 65 NA NA 0.126 0.126
## 66 NA NA -0.235 -0.235
## 67 NA NA 28.240 28.240
## 68 NA NA 0.000 0.000
## 69 42.008 0.000 3.231 3.681
## 70 25.420 0.000 1.615 2.004
## 71 55.701 0.000 3.767 4.156
## 72 41.793 0.000 3.154 3.596
## 73 68.710 0.000 4.088 4.426
## 74 30.724 0.000 1.893 2.262
## 75 28.637 0.000 1.482 1.795
## 76 38.542 0.000 3.136 3.615
## 77 NA NA 1.090 1.090
## 78 NA NA 41.772 41.772
## 79 NA NA 1.852 1.852
## 80 NA NA 26.174 26.174
## 81 6.438 0.000 8.008 19.835
## 82 1.701 0.089 -4.515 19.384
## 83 0.097 0.922 -0.165 0.177
## 84 -1.332 0.183 -0.186 0.064
## 85 0.532 0.594 -0.059 0.088
## 86 2.079 0.038 -0.033 0.243
## 87 -0.723 0.470 -0.060 0.035
## 88 2.069 0.039 -0.025 0.179
## 89 0.604 0.546 -0.028 0.044
## 90 -0.122 0.903 -0.076 0.070Controlled 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 119 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 67
##
## Used Total
## Number of observations 3724 3725
## Number of clusters [country] 6
##
## Model Test User Model:
##
## Test statistic 109.069
## 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 0.351 0.448 0.783 0.434
## fds_01_T3 (a1) 0.859 0.118 7.301 0.000
## fds_02_T3 (a2) -0.705 0.124 -5.694 0.000
## fds_03_T3 (a3) 0.418 0.154 2.715 0.007
## fds_04_T3 (a4) 0.627 0.136 4.603 0.000
## fds_05_T3 (a5) -0.120 0.159 -0.756 0.450
## fds_06_T3 (a6) -0.492 0.144 -3.406 0.001
## fds_07_T3 (a7) -0.099 0.169 -0.582 0.560
## fds_08_T3 (a8) 0.344 0.100 3.430 0.001
## age_T3 0.111 0.024 4.528 0.000
## sex_T3 1.229 0.395 3.115 0.002
## bmi_T3 0.048 0.026 1.854 0.064
## foodst_01_T3 ~
## singlepar (b1) -0.274 0.069 -3.973 0.000
## foodst_02_T3 ~
## singlepar (b2) -0.092 0.060 -1.532 0.126
## foodst_03_T3 ~
## singlepar (b3) -0.254 0.060 -4.268 0.000
## foodst_04_T3 ~
## singlepar (b4) -0.347 0.068 -5.138 0.000
## foodst_05_T3 ~
## singlepar (b5) -0.209 0.052 -4.021 0.000
## foodst_06_T3 ~
## singlepar (b6) 0.275 0.057 4.853 0.000
## foodst_07_T3 ~
## singlepar (b7) 0.099 0.048 2.066 0.039
## foodst_08_T3 ~
## singlepar (b8) 0.135 0.074 1.838 0.066
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .foodst_01_T3 ~~
## .foodst_02_T3 0.197 0.023 8.469 0.000
## .foodst_03_T3 0.554 0.025 22.438 0.000
## .foodst_04_T3 0.563 0.028 20.346 0.000
## .foodst_05_T3 0.189 0.020 9.328 0.000
## .foodst_06_T3 -0.122 0.022 -5.572 0.000
## .foodst_07_T3 -0.098 0.019 -5.286 0.000
## .foodst_08_T3 0.132 0.028 4.655 0.000
## .foodst_02_T3 ~~
## .foodst_03_T3 0.068 0.020 3.405 0.001
## .foodst_04_T3 0.109 0.023 4.827 0.000
## .foodst_05_T3 0.059 0.017 3.421 0.001
## .foodst_06_T3 0.054 0.019 2.829 0.005
## .foodst_07_T3 0.057 0.016 3.577 0.000
## .foodst_08_T3 -0.044 0.025 -1.805 0.071
## .foodst_03_T3 ~~
## .foodst_04_T3 0.744 0.026 29.051 0.000
## .foodst_05_T3 0.368 0.018 20.098 0.000
## .foodst_06_T3 -0.239 0.019 -12.410 0.000
## .foodst_07_T3 -0.190 0.016 -11.686 0.000
## .foodst_08_T3 0.163 0.025 6.605 0.000
## .foodst_04_T3 ~~
## .foodst_05_T3 0.493 0.021 23.208 0.000
## .foodst_06_T3 -0.230 0.022 -10.587 0.000
## .foodst_07_T3 -0.201 0.018 -10.890 0.000
## .foodst_08_T3 0.198 0.028 7.064 0.000
## .foodst_05_T3 ~~
## .foodst_06_T3 -0.224 0.017 -13.277 0.000
## .foodst_07_T3 -0.165 0.014 -11.587 0.000
## .foodst_08_T3 0.169 0.022 7.854 0.000
## .foodst_06_T3 ~~
## .foodst_07_T3 0.391 0.016 23.682 0.000
## .foodst_08_T3 -0.069 0.023 -2.949 0.003
## .foodst_07_T3 ~~
## .foodst_08_T3 -0.036 0.020 -1.809 0.070
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 0.000
## .foodst_01_T3 3.766 0.079 47.803 0.000
## .foodst_02_T3 2.004 0.068 29.345 0.000
## .foodst_03_T3 4.279 0.068 62.873 0.000
## .foodst_04_T3 3.940 0.077 50.971 0.000
## .foodst_05_T3 4.597 0.059 77.441 0.000
## .foodst_06_T3 1.605 0.065 24.798 0.000
## .foodst_07_T3 1.452 0.055 26.462 0.000
## .foodst_08_T3 3.217 0.084 38.279 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 67.081 1.556 43.116 0.000
## .foodst_01_T3 1.622 0.038 43.151 0.000
## .foodst_02_T3 1.219 0.028 43.151 0.000
## .foodst_03_T3 1.210 0.028 43.151 0.000
## .foodst_04_T3 1.561 0.036 43.151 0.000
## .foodst_05_T3 0.920 0.021 43.151 0.000
## .foodst_06_T3 1.095 0.025 43.151 0.000
## .foodst_07_T3 0.786 0.018 43.151 0.000
## .foodst_08_T3 1.845 0.043 43.151 0.000
##
##
## Level 2 [country]:
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 12.042 2.162 5.569 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hds_T3 7.405 4.354 1.701 0.089
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ebndfdst_01_T3 -0.235 0.067 -3.490 0.000
## ebndfdst_02_T3 0.065 0.044 1.479 0.139
## ebndfdst_03_T3 -0.106 0.046 -2.290 0.022
## ebndfdst_04_T3 -0.218 0.064 -3.428 0.001
## ebndfdst_05_T3 0.025 0.034 0.743 0.458
## ebndfdst_06_T3 -0.135 0.049 -2.788 0.005
## ebndfdst_07_T3 -0.010 0.017 -0.560 0.575
## ebndfdst_08_T3 0.047 0.029 1.620 0.105parameterEstimates(fit_SEM_sin, ci=TRUE, level=.99375)## lhs op rhs block level label est se
## 1 hds_T3 ~ singlepar 1 1 0.351 0.448
## 2 hds_T3 ~ foodst_01_T3 1 1 a1 0.859 0.118
## 3 hds_T3 ~ foodst_02_T3 1 1 a2 -0.705 0.124
## 4 hds_T3 ~ foodst_03_T3 1 1 a3 0.418 0.154
## 5 hds_T3 ~ foodst_04_T3 1 1 a4 0.627 0.136
## 6 hds_T3 ~ foodst_05_T3 1 1 a5 -0.120 0.159
## 7 hds_T3 ~ foodst_06_T3 1 1 a6 -0.492 0.144
## 8 hds_T3 ~ foodst_07_T3 1 1 a7 -0.099 0.169
## 9 hds_T3 ~ foodst_08_T3 1 1 a8 0.344 0.100
## 10 hds_T3 ~ age_T3 1 1 0.111 0.024
## 11 hds_T3 ~ sex_T3 1 1 1.229 0.395
## 12 hds_T3 ~ bmi_T3 1 1 0.048 0.026
## 13 foodst_01_T3 ~ singlepar 1 1 b1 -0.274 0.069
## 14 foodst_02_T3 ~ singlepar 1 1 b2 -0.092 0.060
## 15 foodst_03_T3 ~ singlepar 1 1 b3 -0.254 0.060
## 16 foodst_04_T3 ~ singlepar 1 1 b4 -0.347 0.068
## 17 foodst_05_T3 ~ singlepar 1 1 b5 -0.209 0.052
## 18 foodst_06_T3 ~ singlepar 1 1 b6 0.275 0.057
## 19 foodst_07_T3 ~ singlepar 1 1 b7 0.099 0.048
## 20 foodst_08_T3 ~ singlepar 1 1 b8 0.135 0.074
## 21 foodst_01_T3 ~~ foodst_02_T3 1 1 0.197 0.023
## 22 foodst_01_T3 ~~ foodst_03_T3 1 1 0.554 0.025
## 23 foodst_01_T3 ~~ foodst_04_T3 1 1 0.563 0.028
## 24 foodst_01_T3 ~~ foodst_05_T3 1 1 0.189 0.020
## 25 foodst_01_T3 ~~ foodst_06_T3 1 1 -0.122 0.022
## 26 foodst_01_T3 ~~ foodst_07_T3 1 1 -0.098 0.019
## 27 foodst_01_T3 ~~ foodst_08_T3 1 1 0.132 0.028
## 28 foodst_02_T3 ~~ foodst_03_T3 1 1 0.068 0.020
## 29 foodst_02_T3 ~~ foodst_04_T3 1 1 0.109 0.023
## 30 foodst_02_T3 ~~ foodst_05_T3 1 1 0.059 0.017
## 31 foodst_02_T3 ~~ foodst_06_T3 1 1 0.054 0.019
## 32 foodst_02_T3 ~~ foodst_07_T3 1 1 0.057 0.016
## 33 foodst_02_T3 ~~ foodst_08_T3 1 1 -0.044 0.025
## 34 foodst_03_T3 ~~ foodst_04_T3 1 1 0.744 0.026
## 35 foodst_03_T3 ~~ foodst_05_T3 1 1 0.368 0.018
## 36 foodst_03_T3 ~~ foodst_06_T3 1 1 -0.239 0.019
## 37 foodst_03_T3 ~~ foodst_07_T3 1 1 -0.190 0.016
## 38 foodst_03_T3 ~~ foodst_08_T3 1 1 0.163 0.025
## 39 foodst_04_T3 ~~ foodst_05_T3 1 1 0.493 0.021
## 40 foodst_04_T3 ~~ foodst_06_T3 1 1 -0.230 0.022
## 41 foodst_04_T3 ~~ foodst_07_T3 1 1 -0.201 0.018
## 42 foodst_04_T3 ~~ foodst_08_T3 1 1 0.198 0.028
## 43 foodst_05_T3 ~~ foodst_06_T3 1 1 -0.224 0.017
## 44 foodst_05_T3 ~~ foodst_07_T3 1 1 -0.165 0.014
## 45 foodst_05_T3 ~~ foodst_08_T3 1 1 0.169 0.022
## 46 foodst_06_T3 ~~ foodst_07_T3 1 1 0.391 0.016
## 47 foodst_06_T3 ~~ foodst_08_T3 1 1 -0.069 0.023
## 48 foodst_07_T3 ~~ foodst_08_T3 1 1 -0.036 0.020
## 49 hds_T3 ~~ hds_T3 1 1 67.081 1.556
## 50 foodst_01_T3 ~~ foodst_01_T3 1 1 1.622 0.038
## 51 foodst_02_T3 ~~ foodst_02_T3 1 1 1.219 0.028
## 52 foodst_03_T3 ~~ foodst_03_T3 1 1 1.210 0.028
## 53 foodst_04_T3 ~~ foodst_04_T3 1 1 1.561 0.036
## 54 foodst_05_T3 ~~ foodst_05_T3 1 1 0.920 0.021
## 55 foodst_06_T3 ~~ foodst_06_T3 1 1 1.095 0.025
## 56 foodst_07_T3 ~~ foodst_07_T3 1 1 0.786 0.018
## 57 foodst_08_T3 ~~ foodst_08_T3 1 1 1.845 0.043
## 58 singlepar ~~ singlepar 1 1 0.092 0.000
## 59 singlepar ~~ age_T3 1 1 0.009 0.000
## 60 singlepar ~~ sex_T3 1 1 0.008 0.000
## 61 singlepar ~~ bmi_T3 1 1 -0.057 0.000
## 62 age_T3 ~~ age_T3 1 1 32.296 0.000
## 63 age_T3 ~~ sex_T3 1 1 -0.464 0.000
## 64 age_T3 ~~ bmi_T3 1 1 2.611 0.000
## 65 sex_T3 ~~ sex_T3 1 1 0.126 0.000
## 66 sex_T3 ~~ bmi_T3 1 1 -0.235 0.000
## 67 bmi_T3 ~~ bmi_T3 1 1 28.244 0.000
## 68 hds_T3 ~1 1 1 0.000 0.000
## 69 foodst_01_T3 ~1 1 1 3.766 0.079
## 70 foodst_02_T3 ~1 1 1 2.004 0.068
## 71 foodst_03_T3 ~1 1 1 4.279 0.068
## 72 foodst_04_T3 ~1 1 1 3.940 0.077
## 73 foodst_05_T3 ~1 1 1 4.597 0.059
## 74 foodst_06_T3 ~1 1 1 1.605 0.065
## 75 foodst_07_T3 ~1 1 1 1.452 0.055
## 76 foodst_08_T3 ~1 1 1 3.217 0.084
## 77 singlepar ~1 1 1 1.102 0.000
## 78 age_T3 ~1 1 1 41.771 0.000
## 79 sex_T3 ~1 1 1 1.852 0.000
## 80 bmi_T3 ~1 1 1 26.175 0.000
## 81 hds_T3 ~1 2 2 12.042 2.162
## 82 hds_T3 ~~ hds_T3 2 2 7.405 4.354
## 83 ebindfoodst_01_T3 := a1*b1 0 0 ebindfoodst_01_T3 -0.235 0.067
## 84 ebindfoodst_02_T3 := a2*b2 0 0 ebindfoodst_02_T3 0.065 0.044
## 85 ebindfoodst_03_T3 := a3*b3 0 0 ebindfoodst_03_T3 -0.106 0.046
## 86 ebindfoodst_04_T3 := a4*b4 0 0 ebindfoodst_04_T3 -0.218 0.064
## 87 ebindfoodst_05_T3 := a5*b5 0 0 ebindfoodst_05_T3 0.025 0.034
## 88 ebindfoodst_06_T3 := a6*b6 0 0 ebindfoodst_06_T3 -0.135 0.049
## 89 ebindfoodst_07_T3 := a7*b7 0 0 ebindfoodst_07_T3 -0.010 0.017
## 90 ebindfoodst_08_T3 := a8*b8 0 0 ebindfoodst_08_T3 0.047 0.029
## z pvalue ci.lower ci.upper
## 1 0.783 0.434 -0.875 1.577
## 2 7.301 0.000 0.538 1.181
## 3 -5.694 0.000 -1.044 -0.367
## 4 2.715 0.007 -0.003 0.840
## 5 4.603 0.000 0.254 0.999
## 6 -0.756 0.450 -0.554 0.314
## 7 -3.406 0.001 -0.887 -0.097
## 8 -0.582 0.560 -0.562 0.365
## 9 3.430 0.001 0.070 0.619
## 10 4.528 0.000 0.044 0.178
## 11 3.115 0.002 0.150 2.308
## 12 1.854 0.064 -0.023 0.118
## 13 -3.973 0.000 -0.462 -0.085
## 14 -1.532 0.126 -0.255 0.072
## 15 -4.268 0.000 -0.417 -0.091
## 16 -5.138 0.000 -0.532 -0.163
## 17 -4.021 0.000 -0.351 -0.067
## 18 4.853 0.000 0.120 0.430
## 19 2.066 0.039 -0.032 0.230
## 20 1.838 0.066 -0.066 0.336
## 21 8.469 0.000 0.133 0.261
## 22 22.438 0.000 0.486 0.621
## 23 20.346 0.000 0.487 0.638
## 24 9.328 0.000 0.134 0.244
## 25 -5.572 0.000 -0.182 -0.062
## 26 -5.286 0.000 -0.149 -0.047
## 27 4.655 0.000 0.055 0.210
## 28 3.405 0.001 0.013 0.122
## 29 4.827 0.000 0.047 0.171
## 30 3.421 0.001 0.012 0.107
## 31 2.829 0.005 0.002 0.105
## 32 3.577 0.000 0.014 0.101
## 33 -1.805 0.071 -0.112 0.023
## 34 29.051 0.000 0.674 0.814
## 35 20.098 0.000 0.318 0.418
## 36 -12.410 0.000 -0.292 -0.186
## 37 -11.686 0.000 -0.235 -0.146
## 38 6.605 0.000 0.095 0.230
## 39 23.208 0.000 0.435 0.551
## 40 -10.587 0.000 -0.290 -0.171
## 41 -10.890 0.000 -0.251 -0.150
## 42 7.064 0.000 0.121 0.274
## 43 -13.277 0.000 -0.270 -0.178
## 44 -11.587 0.000 -0.203 -0.126
## 45 7.854 0.000 0.110 0.228
## 46 23.682 0.000 0.346 0.436
## 47 -2.949 0.003 -0.133 -0.005
## 48 -1.809 0.070 -0.090 0.018
## 49 43.116 0.000 62.827 71.335
## 50 43.151 0.000 1.519 1.724
## 51 43.151 0.000 1.141 1.296
## 52 43.151 0.000 1.134 1.287
## 53 43.151 0.000 1.462 1.660
## 54 43.151 0.000 0.862 0.979
## 55 43.151 0.000 1.026 1.164
## 56 43.151 0.000 0.736 0.836
## 57 43.151 0.000 1.728 1.962
## 58 NA NA 0.092 0.092
## 59 NA NA 0.009 0.009
## 60 NA NA 0.008 0.008
## 61 NA NA -0.057 -0.057
## 62 NA NA 32.296 32.296
## 63 NA NA -0.464 -0.464
## 64 NA NA 2.611 2.611
## 65 NA NA 0.126 0.126
## 66 NA NA -0.235 -0.235
## 67 NA NA 28.244 28.244
## 68 NA NA 0.000 0.000
## 69 47.803 0.000 3.551 3.982
## 70 29.345 0.000 1.817 2.191
## 71 62.873 0.000 4.093 4.465
## 72 50.971 0.000 3.728 4.151
## 73 77.441 0.000 4.434 4.759
## 74 24.798 0.000 1.428 1.783
## 75 26.462 0.000 1.302 1.602
## 76 38.279 0.000 2.987 3.446
## 77 NA NA 1.102 1.102
## 78 NA NA 41.771 41.771
## 79 NA NA 1.852 1.852
## 80 NA NA 26.175 26.175
## 81 5.569 0.000 6.129 17.954
## 82 1.701 0.089 -4.500 19.311
## 83 -3.490 0.000 -0.420 -0.051
## 84 1.479 0.139 -0.055 0.184
## 85 -2.290 0.022 -0.233 0.021
## 86 -3.428 0.001 -0.391 -0.044
## 87 0.743 0.458 -0.067 0.117
## 88 -2.788 0.005 -0.268 -0.003
## 89 -0.560 0.575 -0.058 0.038
## 90 1.620 0.105 -0.032 0.125