This file lists full summary output for all models included in the final output file for Boothroyd et al, Testing Mate Choice Hypotheses in a Transitional Small Scale Population, Adaptive Human Behaviour and Physiology. Annotation regarding models can be found in the main supplementary output file.
summary(h1)
Call:
lm(formula = MASCprefs_malefaces ~ eth_mest + Age, data = facedataRW)
Residuals:
Min 1Q Median 3Q Max
-0.4644 -0.2366 -0.0394 0.1599 0.5634
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.481138 0.067902 7.086 7.46e-11 ***
eth_mest 0.007453 0.047176 0.158 0.875
Age -0.001462 0.002402 -0.608 0.544
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2717 on 132 degrees of freedom
(2 observations deleted due to missingness)
Multiple R-squared: 0.002856, Adjusted R-squared: -0.01225
F-statistic: 0.1891 on 2 and 132 DF, p-value: 0.828summary(h1cols) Length Class Mode
4 character character summary(h1xEff)
Call:
lm(formula = MascPref_FF ~ eth_mest + eth_gar + eth_misk + eth_creo +
Age, data = facedataRW)
Residuals:
Min 1Q Median 3Q Max
-0.49765 -0.09653 -0.06556 0.13416 0.53135
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4746079 0.0932023 5.092 1.22e-06 ***
eth_mest -0.0322139 0.0916055 -0.352 0.726
eth_gar 0.1148548 0.1038121 1.106 0.271
eth_misk -0.0539143 0.0914960 -0.589 0.557
eth_creo -0.1100331 0.0956335 -1.151 0.252
Age 0.0002808 0.0021171 0.133 0.895
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2399 on 129 degrees of freedom
(2 observations deleted due to missingness)
Multiple R-squared: 0.03851, Adjusted R-squared: 0.001239
F-statistic: 1.033 on 5 and 129 DF, p-value: 0.401summary(h1xEfm)
Call:
lm(formula = MascPref_MF ~ eth_mest + eth_gar + eth_misk + eth_creo +
Age, data = facedataRW)
Residuals:
Min 1Q Median 3Q Max
-0.47380 -0.23122 -0.03924 0.15258 0.55448
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.501117 0.103809 4.827 3.85e-06 ***
eth_mest 0.117136 0.102031 1.148 0.2531
eth_gar 0.194060 0.115627 1.678 0.0957 .
eth_misk 0.097456 0.101909 0.956 0.3407
eth_creo -0.161416 0.106517 -1.515 0.1321
Age -0.001488 0.002358 -0.631 0.5290
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2672 on 129 degrees of freedom
(2 observations deleted due to missingness)
Multiple R-squared: 0.05702, Adjusted R-squared: 0.02047
F-statistic: 1.56 on 5 and 129 DF, p-value: 0.1759summary(h1xEmf)
Call:
lm(formula = MascPref_FF ~ eth_mest + eth_gar + eth_misk + eth_creo +
Age, data = facedataRM)
Residuals:
Min 1Q Median 3Q Max
-0.56914 -0.13492 0.03092 0.13997 0.53999
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.754e-01 8.370e-02 5.680 9.19e-08 ***
eth_mest -7.468e-02 8.266e-02 -0.904 0.368
eth_gar -7.500e-03 9.867e-02 -0.076 0.940
eth_misk 3.424e-02 7.807e-02 0.439 0.662
eth_creo -7.036e-02 9.529e-02 -0.738 0.462
Age 5.554e-06 1.743e-03 0.003 0.997
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2491 on 123 degrees of freedom
(3 observations deleted due to missingness)
Multiple R-squared: 0.0372, Adjusted R-squared: -0.001941
F-statistic: 0.9504 on 5 and 123 DF, p-value: 0.4511summary(h1xEmm)
Call:
lm(formula = MascPref_MF ~ eth_mest + eth_gar + eth_misk + eth_creo +
Age, data = facedataRM)
Residuals:
Min 1Q Median 3Q Max
-0.44296 -0.18832 -0.00126 0.17299 0.60359
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.445619 0.087324 5.103 1.23e-06 ***
eth_mest -0.110562 0.086234 -1.282 0.202
eth_gar -0.054301 0.102943 -0.527 0.599
eth_misk -0.086438 0.081446 -1.061 0.291
eth_creo 0.060624 0.099413 0.610 0.543
Age -0.001618 0.001818 -0.890 0.375
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2599 on 123 degrees of freedom
(3 observations deleted due to missingness)
Multiple R-squared: 0.03368, Adjusted R-squared: -0.005606
F-statistic: 0.8573 on 5 and 123 DF, p-value: 0.512summary(h1xff)
Call:
lm(formula = MASCprefs_femalefaces ~ eth_mest + Age, data = facedataRW)
Residuals:
Min 1Q Median 3Q Max
-0.49909 -0.09433 -0.07784 0.12172 0.52187
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4786552 0.0604389 7.920 8.65e-13 ***
eth_mest 0.0108593 0.0419907 0.259 0.796
Age 0.0002885 0.0021384 0.135 0.893
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2418 on 132 degrees of freedom
(2 observations deleted due to missingness)
Multiple R-squared: 0.000714, Adjusted R-squared: -0.01443
F-statistic: 0.04716 on 2 and 132 DF, p-value: 0.954summary(h1xmf)
Call:
lm(formula = MASCprefs_femalefaces ~ eth_mest + Age, data = facedataRM)
Residuals:
Min 1Q Median 3Q Max
-0.54210 -0.13828 0.05954 0.14158 0.54213
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4949369 0.0546728 9.053 2.19e-15 ***
eth_mest -0.0790446 0.0436138 -1.812 0.0723 .
Age 0.0001364 0.0017179 0.079 0.9368
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2477 on 126 degrees of freedom
(3 observations deleted due to missingness)
Multiple R-squared: 0.02548, Adjusted R-squared: 0.01002
F-statistic: 1.647 on 2 and 126 DF, p-value: 0.1967summary(h1xmm)
Call:
lm(formula = MASCprefs_malefaces ~ eth_mest + Age, data = facedataRM)
Residuals:
Min 1Q Median 3Q Max
-0.45069 -0.18217 0.01486 0.15971 0.61932
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.444666 0.057059 7.793 2.11e-12 ***
eth_mest -0.065553 0.045517 -1.440 0.152
Age -0.001486 0.001793 -0.829 0.409
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2585 on 126 degrees of freedom
(3 observations deleted due to missingness)
Multiple R-squared: 0.02123, Adjusted R-squared: 0.00569
F-statistic: 1.366 on 2 and 126 DF, p-value: 0.2588summary(h2.1)Linear mixed model fit by REML ['lmerMod']
Formula: MASCprefs_malefaces ~ Age + Age2 + (1 | village)
Data: facedataW
REML criterion at convergence: 55.3
Scaled residuals:
Min 1Q Median 3Q Max
-1.7081 -0.8795 -0.1409 0.6288 2.2122
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 0.0002068 0.01438
Residual 0.0693640 0.26337
Number of obs: 154, groups: village, 6
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.3371961 0.1727323 1.952
Age 0.0076071 0.0115186 0.660
Age2 -0.0001292 0.0001729 -0.747
Correlation of Fixed Effects:
(Intr) Age
Age -0.980
Age2 0.936 -0.983summary(h2.2)Linear mixed model fit by REML ['lmerMod']
Formula: MASCprefs_malefaces ~ Agegroup + (1 | village)
Data: facedataW
REML criterion at convergence: 37.2
Scaled residuals:
Min 1Q Median 3Q Max
-1.7092 -0.8833 -0.1149 0.6343 2.1447
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 0.0002897 0.01702
Residual 0.0694651 0.26356
Number of obs: 154, groups: village, 6
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.43404 0.02777 15.630
Agegroupolder adult -0.03468 0.07807 -0.444
Agegroupyoung adult 0.01572 0.04804 0.327
Correlation of Fixed Effects:
(Intr) Aggrpla
Aggrpldradl -0.336
Aggrpyngadl -0.537 0.191 summary(h2ex1.1)Linear mixed model fit by REML ['lmerMod']
Formula: MASCprefs_femalefaces ~ Age * sex + Age2 * sex + (1 | village)
Data: facedata
REML criterion at convergence: 82.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.1451 -0.5998 -0.1832 0.5709 2.5014
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 0.004839 0.06956
Residual 0.061514 0.24802
Number of obs: 306, groups: village, 6
Fixed effects:
Estimate Std. Error t value
(Intercept) 3.854e-01 1.062e-01 3.628
Age 5.059e-03 6.589e-03 0.768
sex -4.988e-02 2.033e-01 -0.245
Age2 -6.060e-05 9.498e-05 -0.638
Age:sex 1.505e-03 1.306e-02 0.115
sex:Age2 -5.529e-06 1.886e-04 -0.029
Correlation of Fixed Effects:
(Intr) Age sex Age2 Age:sx
Age -0.940
sex 0.287 -0.342
Age2 0.887 -0.979 0.387
Age:sex -0.329 0.393 -0.976 -0.446
sex:Age2 0.372 -0.445 0.922 0.507 -0.979
fit warnings:
Some predictor variables are on very different scales: consider rescalingsummary(h2ex1.2)Linear mixed model fit by REML ['lmerMod']
Formula: MASCprefs_femalefaces ~ Agegroup * sex + (1 | village)
Data: facedata
REML criterion at convergence: 45.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.1380 -0.5980 -0.2038 0.5793 2.5926
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 0.004961 0.07044
Residual 0.061558 0.24811
Number of obs: 306, groups: village, 6
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.477491 0.034221 13.953
Agegroupolder adult 0.003643 0.048859 0.075
Agegroupyoung adult -0.026320 0.032706 -0.805
sex -0.021216 0.036562 -0.580
Agegroupolder adult:sex 0.004526 0.097458 0.046
Agegroupyoung adult:sex 0.027298 0.064624 0.422
Correlation of Fixed Effects:
(Intr) Aggrpla Aggrpya sex Aggrpladlt:
Aggrpldradl -0.215
Aggrpyngadl -0.298 0.206
sex -0.015 0.004 0.036
Aggrpladlt: 0.006 0.158 -0.025 -0.379
Aggrpyadlt: 0.007 0.000 -0.017 -0.560 0.214 summary(h2ex2.1)Linear mixed model fit by REML ['lmerMod']
Formula: MASCprefs_malefaces ~ Age * sex + Age2 * sex + (1 | village)
Data: facedata
REML criterion at convergence: 104.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.80768 -0.78850 -0.05959 0.70931 2.50158
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 0.003155 0.05617
Residual 0.066430 0.25774
Number of obs: 306, groups: village, 6
Fixed effects:
Estimate Std. Error t value
(Intercept) 4.547e-01 1.087e-01 4.184
Age -7.664e-04 6.837e-03 -0.112
sex -2.147e-01 2.110e-01 -1.017
Age2 -1.104e-05 9.856e-05 -0.112
Age:sex 1.781e-02 1.356e-02 1.313
sex:Age2 -2.705e-04 1.958e-04 -1.381
Correlation of Fixed Effects:
(Intr) Age sex Age2 Age:sx
Age -0.954
sex 0.290 -0.342
Age2 0.900 -0.979 0.387
Age:sex -0.334 0.393 -0.976 -0.445
sex:Age2 0.377 -0.445 0.922 0.507 -0.979
fit warnings:
Some predictor variables are on very different scales: consider rescalingsummary(h2ex2.2)Linear mixed model fit by REML ['lmerMod']
Formula: MASCprefs_malefaces ~ Agegroup * sex + (1 | village)
Data: facedata
REML criterion at convergence: 66.9
Scaled residuals:
Min 1Q Median 3Q Max
-1.82261 -0.81811 -0.04782 0.72716 2.43587
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 0.003269 0.05718
Residual 0.066601 0.25807
Number of obs: 306, groups: village, 6
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.41754 0.03026 13.800
Agegroupolder adult -0.01844 0.05074 -0.363
Agegroupyoung adult 0.03594 0.03397 1.058
sex 0.05632 0.03800 1.482
Agegroupolder adult:sex -0.07745 0.10123 -0.765
Agegroupyoung adult:sex -0.03108 0.06720 -0.462
Correlation of Fixed Effects:
(Intr) Aggrpla Aggrpya sex Aggrpladlt:
Aggrpldradl -0.251
Aggrpyngadl -0.351 0.207
sex -0.018 0.005 0.034
Aggrpladlt: 0.008 0.158 -0.023 -0.379
Aggrpyadlt: 0.009 -0.001 -0.018 -0.561 0.213 summary(h3)Linear mixed model fit by REML ['lmerMod']
Formula: MASCprefs_malefaces ~ rel + Age + (1 | village)
Data: facedataW
REML criterion at convergence: 43.8
Scaled residuals:
Min 1Q Median 3Q Max
-1.77482 -0.85327 -0.09262 0.67443 2.17544
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 0.00000 0.0000
Residual 0.06933 0.2633
Number of obs: 154, groups: village, 6
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.468060 0.061716 7.584
rel 0.043493 0.043558 0.999
Age -0.001124 0.002149 -0.523
Correlation of Fixed Effects:
(Intr) rel
rel 0.200
Age -0.937 -0.152
convergence code: 0
boundary (singular) fit: see ?isSingularsummary(h3xff)Linear mixed model fit by REML ['lmerMod']
Formula: MASCprefs_femalefaces ~ rel + Age + (1 | village)
Data: facedataW
REML criterion at convergence: 25.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.0268 -0.6061 -0.1163 0.7059 2.5363
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 0.005422 0.07363
Residual 0.058999 0.24290
Number of obs: 154, groups: village, 6
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.434682 0.065957 6.590
rel 0.052088 0.041653 1.251
Age 0.001386 0.002017 0.687
Correlation of Fixed Effects:
(Intr) rel
rel 0.174
Age -0.833 -0.155summary(h3xmf)Linear mixed model fit by REML ['lmerMod']
Formula: MASCprefs_femalefaces ~ rel + Age + (1 | village)
Data: facedataM
REML criterion at convergence: 30.3
Scaled residuals:
Min 1Q Median 3Q Max
-2.17410 -0.75179 -0.01535 0.65664 2.33094
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 0.006012 0.07754
Residual 0.060848 0.24667
Number of obs: 152, groups: village, 6
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.4385168 0.0640598 6.845
rel -0.0782513 0.0478687 -1.635
Age 0.0008254 0.0016490 0.501
Correlation of Fixed Effects:
(Intr) rel
rel 0.374
Age -0.794 -0.280summary(h3xmm)Linear mixed model fit by REML ['lmerMod']
Formula: MASCprefs_malefaces ~ rel + Age + (1 | village)
Data: facedataM
REML criterion at convergence: 38.5
Scaled residuals:
Min 1Q Median 3Q Max
-1.68523 -0.80963 -0.05649 0.70883 2.36619
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 0.002242 0.04735
Residual 0.065626 0.25618
Number of obs: 152, groups: village, 6
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.375482 0.060328 6.224
rel -0.074868 0.048732 -1.536
Age 0.000493 0.001702 0.290
Correlation of Fixed Effects:
(Intr) rel
rel 0.394
Age -0.865 -0.270summary(h4cf)
Call:
lm(formula = MASCprefs_femalefaces ~ rural + Age, data = facedata)
Residuals:
Min 1Q Median 3Q Max
-0.5175 -0.1187 -0.0824 0.1169 0.6565
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.2991025 0.0511997 5.842 1.33e-08 ***
rural 0.1686339 0.0410587 4.107 5.16e-05 ***
Age 0.0008884 0.0012284 0.723 0.47
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2472 on 303 degrees of freedom
(5 observations deleted due to missingness)
Multiple R-squared: 0.05427, Adjusted R-squared: 0.04803
F-statistic: 8.694 on 2 and 303 DF, p-value: 0.0002131summary(h4cm)
Call:
lm(formula = MASCprefs_malefaces ~ rural + Age, data = facedata)
Residuals:
Min 1Q Median 3Q Max
-0.42726 -0.22031 -0.02263 0.17844 0.61565
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4003852 0.0543660 7.365 1.69e-12 ***
rural 0.0318646 0.0435979 0.731 0.465
Age -0.0003563 0.0013044 -0.273 0.785
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2624 on 303 degrees of freedom
(5 observations deleted due to missingness)
Multiple R-squared: 0.002006, Adjusted R-squared: -0.004581
F-statistic: 0.3045 on 2 and 303 DF, p-value: 0.7377summary(h4rf)
Call:
lm(formula = MASCprefs_femalefaces ~ locrank + eth_mest + Age,
data = facedata)
Residuals:
Min 1Q Median 3Q Max
-0.54788 -0.18750 -0.01631 0.16545 0.59380
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3665419 0.0476843 7.687 2.13e-13 ***
locrank 0.0257961 0.0098700 2.614 0.00941 **
eth_mest -0.0326053 0.0307607 -1.060 0.29001
Age 0.0006033 0.0012507 0.482 0.62991
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2497 on 302 degrees of freedom
(5 observations deleted due to missingness)
Multiple R-squared: 0.03821, Adjusted R-squared: 0.02865
F-statistic: 3.999 on 3 and 302 DF, p-value: 0.008162summary(h4rf.s1)
Call:
glm(formula = MASCprefs_femalefaces ~ ns(locrank, 1) + eth_mest +
Age, data = facedata)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.54788 -0.18750 -0.01631 0.16545 0.59380
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3923380 0.0422531 9.285 < 2e-16 ***
ns(locrank, 1) 0.1608672 0.0615504 2.614 0.00941 **
eth_mest -0.0326053 0.0307607 -1.060 0.29001
Age 0.0006033 0.0012507 0.482 0.62991
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 0.06232952)
Null deviance: 19.571 on 305 degrees of freedom
Residual deviance: 18.824 on 302 degrees of freedom
(5 observations deleted due to missingness)
AIC: 25.116
Number of Fisher Scoring iterations: 2summary(h4rf.s2)
Call:
glm(formula = MASCprefs_femalefaces ~ ns(locrank, 2) + eth_mest +
Age, data = facedata)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.5224 -0.1642 -0.0167 0.1668 0.6140
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3645905 0.0496040 7.350 1.88e-12 ***
ns(locrank, 2)1 0.2069091 0.0813431 2.544 0.0115 *
ns(locrank, 2)2 0.0813016 0.0454487 1.789 0.0746 .
eth_mest -0.0292914 0.0309100 -0.948 0.3441
Age 0.0007209 0.0012553 0.574 0.5662
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 0.06230087)
Null deviance: 19.571 on 305 degrees of freedom
Residual deviance: 18.753 on 301 degrees of freedom
(5 observations deleted due to missingness)
AIC: 25.96
Number of Fisher Scoring iterations: 2summary(h4rf.s3)
Call:
glm(formula = MASCprefs_femalefaces ~ ns(locrank, 3) + eth_mest +
Age, data = facedata)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.55525 -0.14404 -0.04639 0.14314 0.63345
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3288436 0.0537945 6.113 3.04e-09 ***
ns(locrank, 3)1 0.0196532 0.0566484 0.347 0.72888
ns(locrank, 3)2 0.3143189 0.1006562 3.123 0.00197 **
ns(locrank, 3)3 0.0963766 0.0455828 2.114 0.03531 *
eth_mest -0.0077074 0.0333623 -0.231 0.81746
Age 0.0008313 0.0012532 0.663 0.50763
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 0.06192025)
Null deviance: 19.571 on 305 degrees of freedom
Residual deviance: 18.576 on 300 degrees of freedom
(5 observations deleted due to missingness)
AIC: 25.067
Number of Fisher Scoring iterations: 2summary(h4rf.s4)
Call:
glm(formula = MASCprefs_femalefaces ~ ns(locrank, 4) + eth_mest +
Age, data = facedata)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.5377 -0.1402 -0.0594 0.1379 0.6662
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3221532 0.0534806 6.024 5.0e-09 ***
ns(locrank, 4)1 0.0694885 0.0743292 0.935 0.351
ns(locrank, 4)2 0.0896688 0.0570166 1.573 0.117
ns(locrank, 4)3 0.3910095 0.0980592 3.987 8.4e-05 ***
ns(locrank, 4)4 0.0584053 0.0595232 0.981 0.327
eth_mest -0.0399692 0.0359055 -1.113 0.267
Age 0.0006329 0.0012470 0.508 0.612
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 0.06102268)
Null deviance: 19.571 on 305 degrees of freedom
Residual deviance: 18.246 on 299 degrees of freedom
(5 observations deleted due to missingness)
AIC: 21.577
Number of Fisher Scoring iterations: 2summary(h4rf.sx)
Call:
glm(formula = MascPref_FF ~ (as.factor(locrank)) + eth_mest +
Age, data = .)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.52578 -0.14095 -0.05124 0.14720 0.66434
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3152172 0.0564407 5.585 5.27e-08 ***
as.factor(locrank)2 0.1757223 0.0504998 3.480 0.000577 ***
as.factor(locrank)3 0.1367176 0.0666094 2.053 0.040992 *
as.factor(locrank)4 0.1283653 0.0488491 2.628 0.009039 **
as.factor(locrank)5 0.2121283 0.0708908 2.992 0.003000 **
as.factor(locrank)6 0.1929293 0.0643117 3.000 0.002929 **
eth_mest -0.0159451 0.0515916 -0.309 0.757489
Age 0.0005684 0.0012481 0.455 0.649173
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 0.06109075)
Null deviance: 19.571 on 305 degrees of freedom
Residual deviance: 18.205 on 298 degrees of freedom
(5 observations deleted due to missingness)
AIC: 22.893
Number of Fisher Scoring iterations: 2summary(h4rf.sxR)
Call:
glm(formula = MascPref_FF ~ (as.factor(locrank)) + eth_mest +
Age, data = .)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.53532 -0.13507 -0.05065 0.14842 0.54758
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.272e-01 5.716e-02 9.223 <2e-16 ***
as.factor(locrank)2 1.942e-02 5.710e-02 0.340 0.734
as.factor(locrank)3 -6.350e-02 6.168e-02 -1.030 0.304
as.factor(locrank)4 -6.043e-02 5.132e-02 -1.178 0.240
as.factor(locrank)5 -1.679e-02 6.035e-02 -0.278 0.781
eth_mest -1.930e-02 5.326e-02 -0.362 0.717
Age -9.089e-05 1.346e-03 -0.068 0.946
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 0.05968484)
Null deviance: 15.665 on 263 degrees of freedom
Residual deviance: 15.339 on 257 degrees of freedom
(5 observations deleted due to missingness)
AIC: 13.974
Number of Fisher Scoring iterations: 2summary(h4rm)
Call:
lm(formula = MASCprefs_malefaces ~ locrank + eth_mest + Age,
data = facedata)
Residuals:
Min 1Q Median 3Q Max
-0.43802 -0.21844 -0.02011 0.17955 0.64299
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3686987 0.0498695 7.393 1.42e-12 ***
locrank 0.0195908 0.0103223 1.898 0.0587 .
eth_mest -0.0086348 0.0321704 -0.268 0.7886
Age -0.0005991 0.0013081 -0.458 0.6473
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2611 on 302 degrees of freedom
(5 observations deleted due to missingness)
Multiple R-squared: 0.01544, Adjusted R-squared: 0.005661
F-statistic: 1.579 on 3 and 302 DF, p-value: 0.1945summary(h4rm.s1)
Call:
glm(formula = MASCprefs_malefaces ~ ns(locrank, 1) + eth_mest +
Age, data = facedata)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.43802 -0.21844 -0.02011 0.17955 0.64299
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3882895 0.0441894 8.787 <2e-16 ***
ns(locrank, 1) 0.1221701 0.0643710 1.898 0.0587 .
eth_mest -0.0086348 0.0321704 -0.268 0.7886
Age -0.0005991 0.0013081 -0.458 0.6473
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 0.06817303)
Null deviance: 20.911 on 305 degrees of freedom
Residual deviance: 20.588 on 302 degrees of freedom
(5 observations deleted due to missingness)
AIC: 52.538
Number of Fisher Scoring iterations: 2summary(h4rm.s2)
Call:
glm(formula = MASCprefs_malefaces ~ ns(locrank, 2) + eth_mest +
Age, data = facedata)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.43747 -0.19801 0.00311 0.20135 0.62120
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4429776 0.0516261 8.580 5.1e-16 ***
ns(locrank, 2)1 -0.0188233 0.0846591 -0.222 0.82420
ns(locrank, 2)2 0.1248966 0.0473015 2.640 0.00871 **
eth_mest -0.0151663 0.0321701 -0.471 0.63767
Age -0.0008309 0.0013065 -0.636 0.52526
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 0.06748386)
Null deviance: 20.911 on 305 degrees of freedom
Residual deviance: 20.313 on 301 degrees of freedom
(5 observations deleted due to missingness)
AIC: 50.414
Number of Fisher Scoring iterations: 2summary(h4rm.s3)
Call:
glm(formula = MASCprefs_malefaces ~ ns(locrank, 3) + eth_mest +
Age, data = facedata)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.44293 -0.19797 0.00084 0.20084 0.62664
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4492841 0.0562452 7.988 2.96e-14 ***
ns(locrank, 3)1 0.0201793 0.0592291 0.341 0.734
ns(locrank, 3)2 -0.0148200 0.1052417 -0.141 0.888
ns(locrank, 3)3 0.1204277 0.0476594 2.527 0.012 *
eth_mest -0.0189742 0.0348821 -0.544 0.587
Age -0.0008504 0.0013103 -0.649 0.517
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 0.0676905)
Null deviance: 20.911 on 305 degrees of freedom
Residual deviance: 20.307 on 300 degrees of freedom
(5 observations deleted due to missingness)
AIC: 52.331
Number of Fisher Scoring iterations: 2summary(h4rm.s4)
Call:
glm(formula = MASCprefs_malefaces ~ ns(locrank, 4) + eth_mest +
Age, data = facedata)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.49065 -0.19317 0.00286 0.20820 0.63705
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.441944 0.055866 7.911 5e-14 ***
ns(locrank, 4)1 -0.165071 0.077644 -2.126 0.0343 *
ns(locrank, 4)2 0.057028 0.059559 0.958 0.3391
ns(locrank, 4)3 0.169235 0.102433 1.652 0.0996 .
ns(locrank, 4)4 0.018819 0.062178 0.303 0.7624
eth_mest -0.054369 0.037507 -1.450 0.1482
Age -0.001068 0.001303 -0.820 0.4129
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 0.06658724)
Null deviance: 20.911 on 305 degrees of freedom
Residual deviance: 19.910 on 299 degrees of freedom
(5 observations deleted due to missingness)
AIC: 48.281
Number of Fisher Scoring iterations: 2summary(h4rm.sx)
Call:
glm(formula = MascPref_MF ~ (as.factor(locrank)), data = .)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.52432 -0.18831 0.00952 0.20952 0.64242
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.390476 0.039774 9.817 <2e-16 ***
as.factor(locrank)2 0.043452 0.052616 0.826 0.4096
as.factor(locrank)3 -0.032900 0.050879 -0.647 0.5184
as.factor(locrank)4 -0.008658 0.050879 -0.170 0.8650
as.factor(locrank)5 0.133848 0.058118 2.303 0.0220 *
as.factor(locrank)6 0.096703 0.057320 1.687 0.0926 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 0.06644273)
Null deviance: 20.911 on 305 degrees of freedom
Residual deviance: 19.933 on 300 degrees of freedom
(5 observations deleted due to missingness)
AIC: 46.638
Number of Fisher Scoring iterations: 2summary(h4rm.sxR)
Call:
glm(formula = MascPref_MF ~ (as.factor(locrank)) + eth_mest +
Age, data = .)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.4623 -0.1820 -0.0043 0.2182 0.6249
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.573499 0.060672 9.453 <2e-16 ***
as.factor(locrank)2 0.048068 0.060604 0.793 0.4284
as.factor(locrank)3 -0.135947 0.065460 -2.077 0.0388 *
as.factor(locrank)4 -0.134877 0.054467 -2.476 0.0139 *
as.factor(locrank)5 -0.081925 0.064051 -1.279 0.2020
eth_mest 0.036858 0.056524 0.652 0.5149
Age -0.002650 0.001428 -1.856 0.0647 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 0.06723433)
Null deviance: 18.478 on 263 degrees of freedom
Residual deviance: 17.279 on 257 degrees of freedom
(5 observations deleted due to missingness)
AIC: 45.418
Number of Fisher Scoring iterations: 2summary(h5fmo)Linear mixed model fit by REML ['lmerMod']
Formula: MASCprefs_malefaces ~ status + Age + (1 | village)
Data: facedataW
REML criterion at convergence: 43.5
Scaled residuals:
Min 1Q Median 3Q Max
-1.84443 -0.82196 -0.06956 0.67886 2.21592
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 0.00000 0.0000
Residual 0.06848 0.2617
Number of obs: 154, groups: village, 6
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.465197 0.060353 7.708
status -0.033314 0.019646 -1.696
Age -0.001469 0.002148 -0.684
Correlation of Fixed Effects:
(Intr) status
status -0.093
Age -0.934 0.184
convergence code: 0
boundary (singular) fit: see ?isSingularsummary(h5fms)Linear mixed model fit by REML ['lmerMod']
Formula: MASCprefs_malefaces ~ educ + income + Age + (1 | village)
Data: facedataW
REML criterion at convergence: 62.3
Scaled residuals:
Min 1Q Median 3Q Max
-1.80945 -0.90532 -0.09025 0.61018 2.14750
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 4.269e-11 6.533e-06
Residual 6.771e-02 2.602e-01
Number of obs: 154, groups: village, 6
Fixed effects:
Estimate Std. Error t value
(Intercept) 4.916e-01 7.442e-02 6.606
educ -3.204e-03 4.822e-03 -0.665
income -7.059e-05 3.516e-05 -2.008
Age -5.656e-04 2.204e-03 -0.257
Correlation of Fixed Effects:
(Intr) educ income
educ -0.595
income 0.128 -0.276
Age -0.876 0.278 -0.199
convergence code: 0
boundary (singular) fit: see ?isSingularsummary(h5long)Linear mixed model fit by REML ['lmerMod']
Formula: Value ~ status * FaceSex * sex + Age + (1 | village)
Data: .
REML criterion at convergence: 95.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.21010 -0.71037 -0.04248 0.63077 2.51440
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 0.002591 0.0509
Residual 0.062777 0.2506
Number of obs: 528, groups: village, 5
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.5330082 0.0395263 13.485
status 0.0068521 0.0108837 0.630
FaceSexMascPref_MF -0.0721380 0.0227997 -3.164
sex -0.0071967 0.0327794 -0.220
Age -0.0012244 0.0009916 -1.235
status:FaceSexMascPref_MF -0.0306461 0.0149260 -2.053
status:sex -0.0188158 0.0214157 -0.879
FaceSexMascPref_MF:sex 0.0300004 0.0455995 0.658
status:FaceSexMascPref_MF:sex 0.0083240 0.0298520 0.279
Correlation of Fixed Effects:
(Intr) status FcSMP_MF sex Age st:FSMP_MF stts:s FSMP_MF:
status -0.009
FcSxMscP_MF -0.288 -0.096
sex -0.050 0.311 -0.025
Age -0.706 0.103 0.000 0.101
stt:FSMP_MF -0.041 -0.686 0.141 -0.197 0.000
status:sex 0.140 0.261 -0.198 0.135 -0.039 -0.194
FcSxMsP_MF: -0.010 -0.195 0.035 -0.696 0.000 0.284 -0.098
st:FSMP_MF: -0.082 -0.191 0.284 -0.098 0.000 0.278 -0.697 0.141 summary(h5long2)Linear mixed model fit by REML ['lmerMod']
Formula: Value ~ income * FaceSex * sex + Age + (1 | village)
Data: .
REML criterion at convergence: 145
Scaled residuals:
Min 1Q Median 3Q Max
-2.14768 -0.66233 -0.07852 0.63039 2.48646
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 0.002596 0.05095
Residual 0.062659 0.25032
Number of obs: 528, groups: village, 5
Fixed effects:
Estimate Std. Error t value
(Intercept) 5.226e-01 4.026e-02 12.983
income 1.251e-05 2.258e-05 0.554
FaceSexMascPref_MF -4.016e-02 2.619e-02 -1.533
sex -1.420e-03 3.763e-02 -0.038
Age -1.003e-03 9.821e-04 -1.021
income:FaceSexMascPref_MF -7.222e-05 3.141e-05 -2.299
income:sex -9.928e-07 4.465e-05 -0.022
FaceSexMascPref_MF:sex 4.662e-02 5.237e-02 0.890
income:FaceSexMascPref_MF:sex -6.626e-05 6.282e-05 -1.055
Correlation of Fixed Effects:
(Intr) income FcSMP_MF sex Age in:FSMP_MF incm:s FSMP_MF:
income -0.177
FcSxMscP_MF -0.325 0.323
sex -0.142 -0.002 0.122
Age -0.681 -0.057 0.000 0.089
inc:FSMP_MF 0.151 -0.696 -0.464 0.013 0.000
income:sex 0.041 0.559 0.013 -0.469 -0.073 -0.405
FcSxMsP_MF: 0.057 0.013 -0.176 -0.696 0.000 -0.018 0.326
in:FSMP_MF: 0.006 -0.401 -0.018 0.323 0.000 0.576 -0.704 -0.464
fit warnings:
Some predictor variables are on very different scales: consider rescalingsummary(h5mfo)Linear mixed model fit by REML ['lmerMod']
Formula: MASCprefs_femalefaces ~ status + Age + (1 | village)
Data: facedataM
REML criterion at convergence: 33.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.1131 -0.6037 -0.0749 0.6170 2.3127
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 0.009287 0.09637
Residual 0.060189 0.24533
Number of obs: 152, groups: village, 6
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.4669337 0.0641351 7.280
status 0.0205959 0.0121856 1.690
Age 0.0002674 0.0015826 0.169
Correlation of Fixed Effects:
(Intr) status
status -0.116
Age -0.723 0.090summary(h5mfs)Linear mixed model fit by REML ['lmerMod']
Formula: MASCprefs_femalefaces ~ educ + income + Age + (1 | village)
Data: facedataM
REML criterion at convergence: 55.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.1212 -0.5831 -0.1553 0.6132 2.2918
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 0.008195 0.09052
Residual 0.060386 0.24574
Number of obs: 152, groups: village, 6
Fixed effects:
Estimate Std. Error t value
(Intercept) 4.763e-01 8.176e-02 5.826
educ -2.439e-04 5.195e-03 -0.047
income 1.238e-05 6.412e-06 1.930
Age -3.208e-04 1.691e-03 -0.190
Correlation of Fixed Effects:
(Intr) educ income
educ -0.647
income 0.039 -0.111
Age -0.743 0.344 -0.139
fit warnings:
Some predictor variables are on very different scales: consider rescalingsummary(h6fface)Linear mixed model fit by REML ['lmerMod']
Formula: MASCprefs_femalefaces ~ TV + (1 | village)
Data: facedata
REML criterion at convergence: 33.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.0962 -0.5223 -0.1440 0.5737 2.3447
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 0.002464 0.04964
Residual 0.061037 0.24706
Number of obs: 306, groups: village, 6
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.493940 0.028530 17.31
TV -0.002731 0.001626 -1.68
Correlation of Fixed Effects:
(Intr)
TV -0.491summary(h6ffaceR)Linear mixed model fit by REML ['lmerMod']
Formula: MascPref_FF ~ TV_ln + (1 | village)
Data: facedataR
REML criterion at convergence: 15.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.1074 -0.4678 -0.2769 0.5207 2.1762
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 0.0001602 0.01266
Residual 0.0594126 0.24375
Number of obs: 264, groups: village, 5
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.51123 0.02385 21.439
TV_ln -0.01287 0.01269 -1.014
Correlation of Fixed Effects:
(Intr)
TV_ln -0.738summary(h6mface)Linear mixed model fit by REML ['lmerMod']
Formula: MASCprefs_malefaces ~ TV + (1 | village)
Data: facedata
REML criterion at convergence: 60.5
Scaled residuals:
Min 1Q Median 3Q Max
-1.87339 -0.76927 -0.00436 0.76976 2.46731
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 0.002234 0.04726
Residual 0.066749 0.25836
Number of obs: 306, groups: village, 6
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.433546 0.028425 15.252
TV -0.001001 0.001678 -0.597
Correlation of Fixed Effects:
(Intr)
TV -0.509summary(h6mfaceR)Linear mixed model fit by REML ['lmerMod']
Formula: MascPref_MF ~ TV_ln + (1 | village)
Data: facedataR
REML criterion at convergence: 54.6
Scaled residuals:
Min 1Q Median 3Q Max
-1.88693 -0.74535 -0.06286 0.70367 2.44889
Random effects:
Groups Name Variance Std.Dev.
village (Intercept) 0.002159 0.04647
Residual 0.068077 0.26092
Number of obs: 264, groups: village, 5
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.45164 0.03325 13.581
TV_ln -0.01563 0.01535 -1.018
Correlation of Fixed Effects:
(Intr)
TV_ln -0.606