## [1] "There are 102837 individuals without missing data in this analysis."
## Estimate
## (Intercept) -0.92
## CA_GroupHigh_CA 0.12
## CA_GroupLow_CA -0.17
## Sex 0.10
## scale(max_age_general_allergy, scale = FALSE) -0.02
## I(scale((max_age_general_allergy), scale = FALSE)^2) 0.00
## CA_GroupHigh_CA:Sex 0.06
## CA_GroupLow_CA:Sex -0.01
## CA_GroupHigh_CA:scale(max_age_general_allergy, scale = FALSE) 0.01
## CA_GroupLow_CA:scale(max_age_general_allergy, scale = FALSE) 0.01
## CA_GroupHigh_CA:I(scale((max_age_general_allergy), scale = FALSE)^2) 0.00
## CA_GroupLow_CA:I(scale((max_age_general_allergy), scale = FALSE)^2) 0.00
## Sex:I(scale((max_age_general_allergy), scale = FALSE)^2) 0.00
## CA_GroupHigh_CA:Sex:I(scale((max_age_general_allergy), scale = FALSE)^2) 0.00
## CA_GroupLow_CA:Sex:I(scale((max_age_general_allergy), scale = FALSE)^2) 0.00
## SE
## (Intercept) 0.01
## CA_GroupHigh_CA 0.03
## CA_GroupLow_CA 0.07
## Sex 0.02
## scale(max_age_general_allergy, scale = FALSE) 0.00
## I(scale((max_age_general_allergy), scale = FALSE)^2) 0.00
## CA_GroupHigh_CA:Sex 0.06
## CA_GroupLow_CA:Sex 0.14
## CA_GroupHigh_CA:scale(max_age_general_allergy, scale = FALSE) 0.00
## CA_GroupLow_CA:scale(max_age_general_allergy, scale = FALSE) 0.01
## CA_GroupHigh_CA:I(scale((max_age_general_allergy), scale = FALSE)^2) 0.00
## CA_GroupLow_CA:I(scale((max_age_general_allergy), scale = FALSE)^2) 0.00
## Sex:I(scale((max_age_general_allergy), scale = FALSE)^2) 0.00
## CA_GroupHigh_CA:Sex:I(scale((max_age_general_allergy), scale = FALSE)^2) 0.00
## CA_GroupLow_CA:Sex:I(scale((max_age_general_allergy), scale = FALSE)^2) 0.00
## p
## (Intercept) 0.00e+00
## CA_GroupHigh_CA 6.29e-05
## CA_GroupLow_CA 1.51e-02
## Sex 1.47e-06
## scale(max_age_general_allergy, scale = FALSE) 4.81e-115
## I(scale((max_age_general_allergy), scale = FALSE)^2) 3.19e-02
## CA_GroupHigh_CA:Sex 3.49e-01
## CA_GroupLow_CA:Sex 9.68e-01
## CA_GroupHigh_CA:scale(max_age_general_allergy, scale = FALSE) 2.43e-02
## CA_GroupLow_CA:scale(max_age_general_allergy, scale = FALSE) 3.96e-01
## CA_GroupHigh_CA:I(scale((max_age_general_allergy), scale = FALSE)^2) 2.63e-01
## CA_GroupLow_CA:I(scale((max_age_general_allergy), scale = FALSE)^2) 5.13e-01
## Sex:I(scale((max_age_general_allergy), scale = FALSE)^2) 2.60e-01
## CA_GroupHigh_CA:Sex:I(scale((max_age_general_allergy), scale = FALSE)^2) 9.68e-02
## CA_GroupLow_CA:Sex:I(scale((max_age_general_allergy), scale = FALSE)^2) 9.51e-01
## OR
## (Intercept) 0.40
## CA_GroupHigh_CA 1.13
## CA_GroupLow_CA 0.84
## Sex 1.11
## scale(max_age_general_allergy, scale = FALSE) 0.98
## I(scale((max_age_general_allergy), scale = FALSE)^2) 1.00
## CA_GroupHigh_CA:Sex 1.06
## CA_GroupLow_CA:Sex 0.99
## CA_GroupHigh_CA:scale(max_age_general_allergy, scale = FALSE) 1.01
## CA_GroupLow_CA:scale(max_age_general_allergy, scale = FALSE) 1.01
## CA_GroupHigh_CA:I(scale((max_age_general_allergy), scale = FALSE)^2) 1.00
## CA_GroupLow_CA:I(scale((max_age_general_allergy), scale = FALSE)^2) 1.00
## Sex:I(scale((max_age_general_allergy), scale = FALSE)^2) 1.00
## CA_GroupHigh_CA:Sex:I(scale((max_age_general_allergy), scale = FALSE)^2) 1.00
## CA_GroupLow_CA:Sex:I(scale((max_age_general_allergy), scale = FALSE)^2) 1.00
There is no influential observations in our data.
## # A tibble: 3 × 12
## General_Allergy_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 -17.7 314. -0.675
## 2 1 Low_CA 0.5 -17.8 317. -0.692
## 3 1 Low_CA -0.5 16.6 276. -1.31
## # … with 6 more variables: .resid <dbl>, .std.resid <dbl>, .hat <dbl>,
## # .sigma <dbl>, .cooksd <dbl>, index <int>
## # A tibble: 0 × 12
## # … with 12 variables: General_Allergy_0_3 <dbl>, CA_Group <fct>, Sex <dbl>,
## # scale(max_age_general_allergy, scale = FALSE) <dbl[,1]>,
## # I(scale((max_age_general_allergy), 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 3.763101 2
## Sex 2.100360 1
## scale(max_age_general_allergy, scale = FALSE) 1.330532 1
## I(scale((max_age_general_allergy), scale = FALSE)^2) 1.348651 1
## CA_Group:Sex 4.001162 2
## CA_Group:scale(max_age_general_allergy, scale = FALSE) 1.471575 2
## CA_Group:I(scale((max_age_general_allergy), scale = FALSE)^2) 4.638974 2
## Sex:I(scale((max_age_general_allergy), scale = FALSE)^2) 2.138713 1
## CA_Group:Sex:I(scale((max_age_general_allergy), scale = FALSE)^2) 3.909847 2
## GVIF^(1/(2*Df))
## CA_Group 1.392793
## Sex 1.449262
## scale(max_age_general_allergy, scale = FALSE) 1.153487
## I(scale((max_age_general_allergy), scale = FALSE)^2) 1.161314
## CA_Group:Sex 1.414316
## CA_Group:scale(max_age_general_allergy, scale = FALSE) 1.101401
## CA_Group:I(scale((max_age_general_allergy), scale = FALSE)^2) 1.467592
## Sex:I(scale((max_age_general_allergy), scale = FALSE)^2) 1.462434
## CA_Group:Sex:I(scale((max_age_general_allergy), scale = FALSE)^2) 1.406177
## [1] "There are 102837 individuals without missing data in this analysis."
## Estimate SE
## (Intercept) -0.93 0.01
## G_std 0.06 0.01
## Sex 0.09 0.02
## scale(max_age_general_allergy, scale = FALSE) -0.02 0.00
## I(scale(max_age_general_allergy, scale = FALSE)^2) 0.00 0.00
## G_std:Sex -0.01 0.01
## G_std:scale(max_age_general_allergy, scale = FALSE) 0.00 0.00
## Sex:scale(max_age_general_allergy, scale = FALSE) 0.00 0.00
## G_std:I(scale(max_age_general_allergy, scale = FALSE)^2) 0.00 0.00
## G_std:Sex:scale(max_age_general_allergy, scale = FALSE) 0.00 0.00
## p OR
## (Intercept) 0.00e+00 0.39
## G_std 6.30e-13 1.06
## Sex 1.00e-09 1.09
## scale(max_age_general_allergy, scale = FALSE) 4.23e-108 0.98
## I(scale(max_age_general_allergy, scale = FALSE)^2) 8.17e-02 1.00
## G_std:Sex 2.69e-01 0.99
## G_std:scale(max_age_general_allergy, scale = FALSE) 3.69e-01 1.00
## Sex:scale(max_age_general_allergy, scale = FALSE) 7.37e-02 1.00
## G_std:I(scale(max_age_general_allergy, scale = FALSE)^2) 7.92e-01 1.00
## G_std:Sex:scale(max_age_general_allergy, scale = FALSE) 1.42e-01 1.00
## Using data Phenotypes_Allergies_Final_0_3 from global environment. This
## could cause incorrect results if Phenotypes_Allergies_Final_0_3 has been
## altered since the model was fit. You can manually provide the data to the
## "data =" argument.
## Using data Phenotypes_Allergies_Final_0_3 from global environment. This
## could cause incorrect results if Phenotypes_Allergies_Final_0_3 has been
## altered since the model was fit. You can manually provide the data to the
## "data =" argument.