age_sex_model_binary <- glm(Phenotype_Eczema_0_3 ~ CA_Group* Sex*scale(max_age_center_Eczema, scale = FALSE) +
CA_Group*Sex*I(scale((max_age_center_Eczema), scale = FALSE)^2) , data = Phenotypes_Allergies_Eczema_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 <- "Eczema"
names(Summary_age_sex_model_binary_Phenotype_HFR) <- c("Estimate", "SE", "t/z", "p", "Model")
Summary_ChildA <- Summary_age_sex_model_binary_Phenotype_HFR
## [1] "There are 261395 individuals without missing data in this analysis."
## Estimate
## (Intercept) -3.49
## CA_GroupHigh_CA 0.22
## CA_GroupLow_CA -0.23
## Sex 0.03
## scale(max_age_center_Eczema, scale = FALSE) 0.01
## I(scale((max_age_center_Eczema), scale = FALSE)^2) 0.00
## CA_GroupHigh_CA:Sex 0.09
## CA_GroupLow_CA:Sex 0.10
## CA_GroupHigh_CA:scale(max_age_center_Eczema, scale = FALSE) 0.01
## CA_GroupLow_CA:scale(max_age_center_Eczema, scale = FALSE) 0.00
## Sex:scale(max_age_center_Eczema, scale = FALSE) -0.01
## CA_GroupHigh_CA:I(scale((max_age_center_Eczema), scale = FALSE)^2) 0.00
## CA_GroupLow_CA:I(scale((max_age_center_Eczema), scale = FALSE)^2) 0.00
## Sex:I(scale((max_age_center_Eczema), scale = FALSE)^2) 0.00
## CA_GroupHigh_CA:Sex:scale(max_age_center_Eczema, scale = FALSE) 0.01
## CA_GroupLow_CA:Sex:scale(max_age_center_Eczema, scale = FALSE) -0.01
## CA_GroupHigh_CA:Sex:I(scale((max_age_center_Eczema), scale = FALSE)^2) 0.00
## CA_GroupLow_CA:Sex:I(scale((max_age_center_Eczema), scale = FALSE)^2) 0.00
## SE
## (Intercept) 0.02
## CA_GroupHigh_CA 0.06
## CA_GroupLow_CA 0.09
## Sex 0.03
## scale(max_age_center_Eczema, scale = FALSE) 0.00
## I(scale((max_age_center_Eczema), scale = FALSE)^2) 0.00
## CA_GroupHigh_CA:Sex 0.11
## CA_GroupLow_CA:Sex 0.19
## CA_GroupHigh_CA:scale(max_age_center_Eczema, scale = FALSE) 0.00
## CA_GroupLow_CA:scale(max_age_center_Eczema, scale = FALSE) 0.01
## Sex:scale(max_age_center_Eczema, scale = FALSE) 0.00
## CA_GroupHigh_CA:I(scale((max_age_center_Eczema), scale = FALSE)^2) 0.00
## CA_GroupLow_CA:I(scale((max_age_center_Eczema), scale = FALSE)^2) 0.00
## Sex:I(scale((max_age_center_Eczema), scale = FALSE)^2) 0.00
## CA_GroupHigh_CA:Sex:scale(max_age_center_Eczema, scale = FALSE) 0.01
## CA_GroupLow_CA:Sex:scale(max_age_center_Eczema, scale = FALSE) 0.02
## CA_GroupHigh_CA:Sex:I(scale((max_age_center_Eczema), scale = FALSE)^2) 0.00
## CA_GroupLow_CA:Sex:I(scale((max_age_center_Eczema), scale = FALSE)^2) 0.00
## p
## (Intercept) 0.00e+00
## CA_GroupHigh_CA 7.61e-05
## CA_GroupLow_CA 1.64e-02
## Sex 3.82e-01
## scale(max_age_center_Eczema, scale = FALSE) 9.22e-11
## I(scale((max_age_center_Eczema), scale = FALSE)^2) 3.16e-15
## CA_GroupHigh_CA:Sex 4.01e-01
## CA_GroupLow_CA:Sex 6.05e-01
## CA_GroupHigh_CA:scale(max_age_center_Eczema, scale = FALSE) 1.62e-01
## CA_GroupLow_CA:scale(max_age_center_Eczema, scale = FALSE) 9.82e-01
## Sex:scale(max_age_center_Eczema, scale = FALSE) 3.63e-03
## CA_GroupHigh_CA:I(scale((max_age_center_Eczema), scale = FALSE)^2) 5.15e-01
## CA_GroupLow_CA:I(scale((max_age_center_Eczema), scale = FALSE)^2) 7.45e-01
## Sex:I(scale((max_age_center_Eczema), scale = FALSE)^2) 3.46e-02
## CA_GroupHigh_CA:Sex:scale(max_age_center_Eczema, scale = FALSE) 3.61e-01
## CA_GroupLow_CA:Sex:scale(max_age_center_Eczema, scale = FALSE) 6.08e-01
## CA_GroupHigh_CA:Sex:I(scale((max_age_center_Eczema), scale = FALSE)^2) 8.48e-01
## CA_GroupLow_CA:Sex:I(scale((max_age_center_Eczema), scale = FALSE)^2) 5.61e-01
## OR
## (Intercept) 0.03
## CA_GroupHigh_CA 1.25
## CA_GroupLow_CA 0.79
## Sex 1.03
## scale(max_age_center_Eczema, scale = FALSE) 1.01
## I(scale((max_age_center_Eczema), scale = FALSE)^2) 1.00
## CA_GroupHigh_CA:Sex 1.09
## CA_GroupLow_CA:Sex 1.11
## CA_GroupHigh_CA:scale(max_age_center_Eczema, scale = FALSE) 1.01
## CA_GroupLow_CA:scale(max_age_center_Eczema, scale = FALSE) 1.00
## Sex:scale(max_age_center_Eczema, scale = FALSE) 0.99
## CA_GroupHigh_CA:I(scale((max_age_center_Eczema), scale = FALSE)^2) 1.00
## CA_GroupLow_CA:I(scale((max_age_center_Eczema), scale = FALSE)^2) 1.00
## Sex:I(scale((max_age_center_Eczema), scale = FALSE)^2) 1.00
## CA_GroupHigh_CA:Sex:scale(max_age_center_Eczema, scale = FALSE) 1.01
## CA_GroupLow_CA:Sex:scale(max_age_center_Eczema, scale = FALSE) 0.99
## CA_GroupHigh_CA:Sex:I(scale((max_age_center_Eczema), scale = FALSE)^2) 1.00
## CA_GroupLow_CA:Sex:I(scale((max_age_center_Eczema), scale = FALSE)^2) 1.00
There is no influential observations in our data.
## # A tibble: 3 × 12
## Phenotype_Eczema_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 20.6 424. -3.16
## 2 1 Low_CA -0.5 21.0 441. -3.14
## 3 1 Low_CA -0.5 18.6 345. -3.25
## # … with 6 more variables: .resid <dbl>, .std.resid <dbl>, .hat <dbl>,
## # .sigma <dbl>, .cooksd <dbl>, index <int>
## # A tibble: 0 × 12
## # … with 12 variables: Phenotype_Eczema_0_3 <dbl>, CA_Group <fct>, Sex <dbl>,
## # scale(max_age_center_Eczema, scale = FALSE) <dbl[,1]>,
## # I(scale((max_age_center_Eczema), scale = FALSE)^2) <I<dbl[,1]>[,1]>,
## # .fitted <dbl>, .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 3.514808 2
## Sex 2.056148 1
## scale(max_age_center_Eczema, scale = FALSE) 1.172918 1
## I(scale((max_age_center_Eczema), scale = FALSE)^2) 1.184727 1
## CA_Group:Sex 3.919719 2
## CA_Group:scale(max_age_center_Eczema, scale = FALSE) 1.272159 2
## Sex:scale(max_age_center_Eczema, scale = FALSE) 1.168528 1
## CA_Group:I(scale((max_age_center_Eczema), scale = FALSE)^2) 3.963048 2
## Sex:I(scale((max_age_center_Eczema), scale = FALSE)^2) 2.121075 1
## CA_Group:Sex:scale(max_age_center_Eczema, scale = FALSE) 1.272236 2
## CA_Group:Sex:I(scale((max_age_center_Eczema), scale = FALSE)^2) 4.147600 2
## GVIF^(1/(2*Df))
## CA_Group 1.369227
## Sex 1.433928
## scale(max_age_center_Eczema, scale = FALSE) 1.083014
## I(scale((max_age_center_Eczema), scale = FALSE)^2) 1.088452
## CA_Group:Sex 1.407064
## CA_Group:scale(max_age_center_Eczema, scale = FALSE) 1.062026
## Sex:scale(max_age_center_Eczema, scale = FALSE) 1.080985
## CA_Group:I(scale((max_age_center_Eczema), scale = FALSE)^2) 1.410936
## Sex:I(scale((max_age_center_Eczema), scale = FALSE)^2) 1.456391
## CA_Group:Sex:scale(max_age_center_Eczema, scale = FALSE) 1.062043
## CA_Group:Sex:I(scale((max_age_center_Eczema), scale = FALSE)^2) 1.427083
## [1] "There are 261395 individuals without missing data in this analysis."
## Estimate SE p
## (Intercept) -3.51 0.02 0.00e+00
## G_std 0.09 0.01 4.24e-14
## Sex 0.01 0.02 7.83e-01
## scale(max_age_center_Eczema, scale = FALSE) 0.01 0.00 1.39e-14
## I(scale(max_age_center_Eczema, scale = FALSE)^2) 0.00 0.00 1.33e-16
## G_std:Sex 0.00 0.02 8.96e-01
## G_std:scale(max_age_center_Eczema, scale = FALSE) 0.00 0.00 3.34e-01
## Sex:scale(max_age_center_Eczema, scale = FALSE) -0.01 0.00 2.46e-03
## G_std:I(scale(max_age_center_Eczema, scale = FALSE)^2) 0.00 0.00 6.62e-01
## G_std:Sex:scale(max_age_center_Eczema, scale = FALSE) 0.00 0.00 1.45e-01
## OR
## (Intercept) 0.03
## G_std 1.09
## Sex 1.01
## scale(max_age_center_Eczema, scale = FALSE) 1.01
## I(scale(max_age_center_Eczema, scale = FALSE)^2) 1.00
## G_std:Sex 1.00
## G_std:scale(max_age_center_Eczema, scale = FALSE) 1.00
## Sex:scale(max_age_center_Eczema, scale = FALSE) 0.99
## G_std:I(scale(max_age_center_Eczema, scale = FALSE)^2) 1.00
## G_std:Sex:scale(max_age_center_Eczema, scale = FALSE) 1.00
## Using data Phenotypes_Allergies_Eczema_no_na from global environment. This
## could cause incorrect results if Phenotypes_Allergies_Eczema_no_na has been
## altered since the model was fit. You can manually provide the data to the
## "data =" argument.
## Using data Phenotypes_Allergies_Eczema_no_na from global environment. This
## could cause incorrect results if Phenotypes_Allergies_Eczema_no_na has been
## altered since the model was fit. You can manually provide the data to the
## "data =" argument.