age_sex_model_binary <- glm(Phenotype_Other_0_3 ~ CA_Group*Sex + CA_Group*scale(max_age_center_other, scale = FALSE)+
CA_Group*I(scale((max_age_center_other), scale = FALSE)^2), data = Phenotypes_Allergies_Other_0_3_no_na,
family = binomial)
Summary_age_sex_model_binary_Phenotype_HFR <- as.data.frame(summary(age_sex_model_binary)$coefficients)
Summary_age_sex_model_binary_Phenotype_HFR$Model <- "Other"
names(Summary_age_sex_model_binary_Phenotype_HFR) <- c("Estimate", "SE", "t/z", "p", "Model")
Summary_age_sex_model_binary_Phenotype_HFR
## Estimate
## (Intercept) -3.7921287677
## CA_GroupHigh_CA 0.2823835967
## CA_GroupLow_CA -0.2365179009
## Sex 0.5307244756
## scale(max_age_center_other, scale = FALSE) 0.0248600207
## I(scale((max_age_center_other), scale = FALSE)^2) 0.0009626929
## CA_GroupHigh_CA:Sex 0.0989722730
## CA_GroupLow_CA:Sex 0.0785612398
## CA_GroupHigh_CA:scale(max_age_center_other, scale = FALSE) 0.0063111867
## CA_GroupLow_CA:scale(max_age_center_other, scale = FALSE) 0.0110834204
## CA_GroupHigh_CA:I(scale((max_age_center_other), scale = FALSE)^2) -0.0005033125
## CA_GroupLow_CA:I(scale((max_age_center_other), scale = FALSE)^2) 0.0001845002
## SE
## (Intercept) 0.0183428802
## CA_GroupHigh_CA 0.0627160293
## CA_GroupLow_CA 0.1058759816
## Sex 0.0278057029
## scale(max_age_center_other, scale = FALSE) 0.0015320477
## I(scale((max_age_center_other), scale = FALSE)^2) 0.0001521481
## CA_GroupHigh_CA:Sex 0.0965827569
## CA_GroupLow_CA:Sex 0.1591448061
## CA_GroupHigh_CA:scale(max_age_center_other, scale = FALSE) 0.0055819034
## CA_GroupLow_CA:scale(max_age_center_other, scale = FALSE) 0.0084087821
## CA_GroupHigh_CA:I(scale((max_age_center_other), scale = FALSE)^2) 0.0005106482
## CA_GroupLow_CA:I(scale((max_age_center_other), scale = FALSE)^2) 0.0008233581
## t/z
## (Intercept) -206.7357322
## CA_GroupHigh_CA 4.5025745
## CA_GroupLow_CA -2.2339146
## Sex 19.0868930
## scale(max_age_center_other, scale = FALSE) 16.2266627
## I(scale((max_age_center_other), scale = FALSE)^2) 6.3273406
## CA_GroupHigh_CA:Sex 1.0247406
## CA_GroupLow_CA:Sex 0.4936463
## CA_GroupHigh_CA:scale(max_age_center_other, scale = FALSE) 1.1306514
## CA_GroupLow_CA:scale(max_age_center_other, scale = FALSE) 1.3180768
## CA_GroupHigh_CA:I(scale((max_age_center_other), scale = FALSE)^2) -0.9856344
## CA_GroupLow_CA:I(scale((max_age_center_other), scale = FALSE)^2) 0.2240825
## p
## (Intercept) 0.000000e+00
## CA_GroupHigh_CA 6.713520e-06
## CA_GroupLow_CA 2.548869e-02
## Sex 3.245051e-81
## scale(max_age_center_other, scale = FALSE) 3.267484e-59
## I(scale((max_age_center_other), scale = FALSE)^2) 2.494224e-10
## CA_GroupHigh_CA:Sex 3.054856e-01
## CA_GroupLow_CA:Sex 6.215560e-01
## CA_GroupHigh_CA:scale(max_age_center_other, scale = FALSE) 2.582019e-01
## CA_GroupLow_CA:scale(max_age_center_other, scale = FALSE) 1.874779e-01
## CA_GroupHigh_CA:I(scale((max_age_center_other), scale = FALSE)^2) 3.243125e-01
## CA_GroupLow_CA:I(scale((max_age_center_other), scale = FALSE)^2) 8.226931e-01
## Model
## (Intercept) Other
## CA_GroupHigh_CA Other
## CA_GroupLow_CA Other
## Sex Other
## scale(max_age_center_other, scale = FALSE) Other
## I(scale((max_age_center_other), scale = FALSE)^2) Other
## CA_GroupHigh_CA:Sex Other
## CA_GroupLow_CA:Sex Other
## CA_GroupHigh_CA:scale(max_age_center_other, scale = FALSE) Other
## CA_GroupLow_CA:scale(max_age_center_other, scale = FALSE) Other
## CA_GroupHigh_CA:I(scale((max_age_center_other), scale = FALSE)^2) Other
## CA_GroupLow_CA:I(scale((max_age_center_other), scale = FALSE)^2) Other
Summary_ChildA <- Summary_age_sex_model_binary_Phenotype_HFR
## [1] "There are 261395 individuals without missing data in this analysis."
## Estimate SE
## (Intercept) -3.79 0.02
## CA_GroupHigh_CA 0.28 0.06
## CA_GroupLow_CA -0.24 0.11
## Sex 0.53 0.03
## scale(max_age_center_other, scale = FALSE) 0.02 0.00
## I(scale((max_age_center_other), scale = FALSE)^2) 0.00 0.00
## CA_GroupHigh_CA:Sex 0.10 0.10
## CA_GroupLow_CA:Sex 0.08 0.16
## CA_GroupHigh_CA:scale(max_age_center_other, scale = FALSE) 0.01 0.01
## CA_GroupLow_CA:scale(max_age_center_other, scale = FALSE) 0.01 0.01
## CA_GroupHigh_CA:I(scale((max_age_center_other), scale = FALSE)^2) 0.00 0.00
## CA_GroupLow_CA:I(scale((max_age_center_other), scale = FALSE)^2) 0.00 0.00
## p OR
## (Intercept) 0.00e+00 0.02
## CA_GroupHigh_CA 6.71e-06 1.32
## CA_GroupLow_CA 2.55e-02 0.79
## Sex 3.25e-81 1.70
## scale(max_age_center_other, scale = FALSE) 3.27e-59 1.02
## I(scale((max_age_center_other), scale = FALSE)^2) 2.49e-10 1.00
## CA_GroupHigh_CA:Sex 3.05e-01 1.11
## CA_GroupLow_CA:Sex 6.22e-01 1.08
## CA_GroupHigh_CA:scale(max_age_center_other, scale = FALSE) 2.58e-01 1.01
## CA_GroupLow_CA:scale(max_age_center_other, scale = FALSE) 1.87e-01 1.01
## CA_GroupHigh_CA:I(scale((max_age_center_other), scale = FALSE)^2) 3.24e-01 1.00
## CA_GroupLow_CA:I(scale((max_age_center_other), scale = FALSE)^2) 8.23e-01 1.00
There is no influential observations in our data.
## # A tibble: 3 × 12
## Phenotype_Other_0_3 CA_Group Sex `scale(max_a…`[,1] `I(scale((…`[,1] .fitted
## <dbl> <fct> <dbl> <dbl> <I<dbl>> <dbl>
## 1 1 Low_CA 0.5 19.4 377. -2.59
## 2 1 Low_CA 0.5 19.8 393. -2.56
## 3 1 Low_CA -0.5 19.5 380. -3.20
## # … with 6 more variables: .resid <dbl>, .std.resid <dbl>, .hat <dbl>,
## # .sigma <dbl>, .cooksd <dbl>, index <int>
## # A tibble: 17 × 12
## Phenotype_Other_0_3 CA_Group Sex `scale(max_…`[,1] `I(scale((…`[,1] .fitted
## <dbl> <fct> <dbl> <dbl> <I<dbl>> <dbl>
## 1 1 Low_CA -0.5 -5.67 32.1 -4.50
## 2 1 Low_CA -0.5 -6.42 41.2 -4.52
## 3 1 Low_CA -0.5 -13.8 191. -4.61
## 4 1 Low_CA -0.5 -6.58 43.3 -4.52
## 5 1 Low_CA -0.5 -12.2 150. -4.60
## 6 1 Low_CA -0.5 -10.8 117. -4.59
## 7 1 Low_CA -0.5 -8.17 66.7 -4.55
## 8 1 Low_CA -0.5 -10.6 112. -4.59
## 9 1 Low_CA -0.5 -15.6 243. -4.61
## 10 1 Low_CA -0.5 -12.7 163. -4.61
## 11 1 Low_CA -0.5 -10.2 105. -4.58
## 12 1 Low_CA -0.5 -15.7 245. -4.61
## 13 1 Low_CA -0.5 -9.00 81.0 -4.56
## 14 1 Low_CA -0.5 -9.08 82.5 -4.57
## 15 1 Low_CA -0.5 -14.3 205. -4.61
## 16 1 Low_CA -0.5 -15.3 235. -4.61
## 17 1 Low_CA -0.5 -15.6 243. -4.61
## # … with 6 more variables: .resid <dbl>, .std.resid <dbl>, .hat <dbl>,
## # .sigma <dbl>, .cooksd <dbl>, index <int>
As a rule of thumb, a VIF value that exceeds 5 or 10 indicates a problematic amount of collinearity.
## there are higher-order terms (interactions) in this model
## consider setting terms = 'marginal' or 'high-order'; see ?vif
## GVIF Df
## CA_Group 3.668880 2
## Sex 1.133104 1
## scale(max_age_center_other, scale = FALSE) 1.124147 1
## I(scale((max_age_center_other), scale = FALSE)^2) 1.138919 1
## CA_Group:Sex 1.362645 2
## CA_Group:scale(max_age_center_other, scale = FALSE) 1.300912 2
## CA_Group:I(scale((max_age_center_other), scale = FALSE)^2) 3.486354 2
## GVIF^(1/(2*Df))
## CA_Group 1.383991
## Sex 1.064474
## scale(max_age_center_other, scale = FALSE) 1.060258
## I(scale((max_age_center_other), scale = FALSE)^2) 1.067202
## CA_Group:Sex 1.080428
## CA_Group:scale(max_age_center_other, scale = FALSE) 1.067977
## CA_Group:I(scale((max_age_center_other), scale = FALSE)^2) 1.366447
## [1] "There are 261395 individuals without missing data in this analysis."
## Estimate SE p
## (Intercept) -3.80 0.02 0.00e+00
## G_std 0.09 0.01 1.56e-09
## Sex 0.53 0.03 4.26e-84
## scale(max_age_center_other, scale = FALSE) 0.02 0.00 1.40e-56
## I(scale(max_age_center_other, scale = FALSE)^2) 0.00 0.00 5.18e-11
## G_std:Sex 0.01 0.02 6.66e-01
## G_std:scale(max_age_center_other, scale = FALSE) 0.00 0.00 4.61e-01
## Sex:scale(max_age_center_other, scale = FALSE) 0.01 0.00 3.62e-02
## G_std:I(scale(max_age_center_other, scale = FALSE)^2) 0.00 0.00 4.45e-01
## G_std:Sex:scale(max_age_center_other, scale = FALSE) 0.00 0.00 9.52e-02
## OR
## (Intercept) 0.02
## G_std 1.09
## Sex 1.70
## scale(max_age_center_other, scale = FALSE) 1.02
## I(scale(max_age_center_other, scale = FALSE)^2) 1.00
## G_std:Sex 1.01
## G_std:scale(max_age_center_other, scale = FALSE) 1.00
## Sex:scale(max_age_center_other, scale = FALSE) 1.01
## G_std:I(scale(max_age_center_other, scale = FALSE)^2) 1.00
## G_std:Sex:scale(max_age_center_other, scale = FALSE) 1.00
## Using data Phenotypes_Allergies_Other_0_3_no_na from global environment.
## This could cause incorrect results if Phenotypes_Allergies_Other_0_3_no_na
## has been altered since the model was fit. You can manually provide the data
## to the "data =" argument.
## Using data Phenotypes_Allergies_Other_0_3_no_na from global environment.
## This could cause incorrect results if Phenotypes_Allergies_Other_0_3_no_na
## has been altered since the model was fit. You can manually provide the data
## to the "data =" argument.