## [1] "There are 102837 individuals without missing data in this analysis."
## Estimate SE
## (Intercept) -1.11 0.01
## CA_GroupHigh_CA 0.12 0.03
## CA_GroupLow_CA -0.21 0.07
## Sex 0.08 0.02
## scale(max_age_HFR, scale = FALSE) -0.03 0.00
## I(scale((max_age_HFR), scale = FALSE)^2) 0.00 0.00
## CA_GroupHigh_CA:Sex 0.06 0.07
## CA_GroupLow_CA:Sex 0.00 0.15
## CA_GroupHigh_CA:scale(max_age_HFR, scale = FALSE) 0.00 0.00
## CA_GroupLow_CA:scale(max_age_HFR, scale = FALSE) 0.01 0.01
## Sex:scale(max_age_HFR, scale = FALSE) 0.00 0.00
## CA_GroupHigh_CA:I(scale((max_age_HFR), scale = FALSE)^2) 0.00 0.00
## CA_GroupLow_CA:I(scale((max_age_HFR), scale = FALSE)^2) 0.00 0.00
## Sex:I(scale((max_age_HFR), scale = FALSE)^2) 0.00 0.00
## CA_GroupHigh_CA:Sex:scale(max_age_HFR, scale = FALSE) -0.01 0.01
## CA_GroupLow_CA:Sex:scale(max_age_HFR, scale = FALSE) 0.02 0.01
## CA_GroupHigh_CA:Sex:I(scale((max_age_HFR), scale = FALSE)^2) 0.00 0.00
## CA_GroupLow_CA:Sex:I(scale((max_age_HFR), scale = FALSE)^2) 0.00 0.00
## p OR
## (Intercept) 0.00e+00 0.33
## CA_GroupHigh_CA 2.90e-04 1.13
## CA_GroupLow_CA 5.23e-03 0.81
## Sex 3.40e-04 1.08
## scale(max_age_HFR, scale = FALSE) 6.84e-134 0.97
## I(scale((max_age_HFR), scale = FALSE)^2) 1.65e-03 1.00
## CA_GroupHigh_CA:Sex 3.91e-01 1.06
## CA_GroupLow_CA:Sex 9.90e-01 1.00
## CA_GroupHigh_CA:scale(max_age_HFR, scale = FALSE) 1.49e-01 1.00
## CA_GroupLow_CA:scale(max_age_HFR, scale = FALSE) 4.44e-01 1.01
## Sex:scale(max_age_HFR, scale = FALSE) 2.88e-01 1.00
## CA_GroupHigh_CA:I(scale((max_age_HFR), scale = FALSE)^2) 6.28e-01 1.00
## CA_GroupLow_CA:I(scale((max_age_HFR), scale = FALSE)^2) 2.39e-01 1.00
## Sex:I(scale((max_age_HFR), scale = FALSE)^2) 3.48e-01 1.00
## CA_GroupHigh_CA:Sex:scale(max_age_HFR, scale = FALSE) 2.27e-01 0.99
## CA_GroupLow_CA:Sex:scale(max_age_HFR, scale = FALSE) 2.91e-01 1.02
## CA_GroupHigh_CA:Sex:I(scale((max_age_HFR), scale = FALSE)^2) 8.01e-02 1.00
## CA_GroupLow_CA:Sex:I(scale((max_age_HFR), scale = FALSE)^2) 9.18e-01 1.00
There is no influential observations in our data.
## # A tibble: 3 × 12
## Phenotype_HFR_0_3 CA_Group Sex `scale(max_age…`[,1] `I(scale((…`[,1] .fitted
## <dbl> <fct> <dbl> <dbl> <I<dbl>> <dbl>
## 1 1 Low_CA -0.5 -17.7 314. -0.622
## 2 1 Low_CA -0.5 14.0 197. -1.66
## 3 1 Low_CA -0.5 16.6 276. -1.69
## # … 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_HFR_0_3 <dbl>, CA_Group <fct>, Sex <dbl>,
## # scale(max_age_HFR, scale = FALSE) <dbl[,1]>,
## # I(scale((max_age_HFR), 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 3.775563 2
## Sex 2.182827 1
## scale(max_age_HFR, scale = FALSE) 1.362722 1
## I(scale((max_age_HFR), scale = FALSE)^2) 1.375691 1
## CA_Group:Sex 4.299687 2
## CA_Group:scale(max_age_HFR, scale = FALSE) 1.611461 2
## Sex:scale(max_age_HFR, scale = FALSE) 1.365159 1
## CA_Group:I(scale((max_age_HFR), scale = FALSE)^2) 4.905857 2
## Sex:I(scale((max_age_HFR), scale = FALSE)^2) 2.474886 1
## CA_Group:Sex:scale(max_age_HFR, scale = FALSE) 1.612369 2
## CA_Group:Sex:I(scale((max_age_HFR), scale = FALSE)^2) 5.185142 2
## GVIF^(1/(2*Df))
## CA_Group 1.393944
## Sex 1.477439
## scale(max_age_HFR, scale = FALSE) 1.167357
## I(scale((max_age_HFR), scale = FALSE)^2) 1.172898
## CA_Group:Sex 1.439989
## CA_Group:scale(max_age_HFR, scale = FALSE) 1.126691
## Sex:scale(max_age_HFR, scale = FALSE) 1.168400
## CA_Group:I(scale((max_age_HFR), scale = FALSE)^2) 1.488260
## Sex:I(scale((max_age_HFR), scale = FALSE)^2) 1.573177
## CA_Group:Sex:scale(max_age_HFR, scale = FALSE) 1.126850
## CA_Group:Sex:I(scale((max_age_HFR), scale = FALSE)^2) 1.509003
## [1] "There are 102837 individuals without missing data in this analysis."
## Estimate SE p OR
## (Intercept) -1.14 0.01 0.00e+00 0.32
## G_std 0.06 0.01 5.87e-15 1.06
## Sex 0.07 0.02 3.32e-06 1.07
## scale(max_age_HFR, scale = FALSE) -0.03 0.00 2.07e-126 0.97
## I(scale(max_age_HFR, scale = FALSE)^2) 0.00 0.00 1.21e-02 1.00
## G_std:Sex -0.01 0.01 2.74e-01 0.99
## G_std:scale(max_age_HFR, scale = FALSE) 0.00 0.00 5.88e-01 1.00
## Sex:scale(max_age_HFR, scale = FALSE) 0.00 0.00 5.82e-02 1.00
## G_std:I(scale(max_age_HFR, scale = FALSE)^2) 0.00 0.00 5.09e-01 1.00
## G_std:Sex:scale(max_age_HFR, scale = FALSE) 0.00 0.00 2.60e-01 1.00
## Using data Phenotypes_Allergies_HFR_0_1_2_3_no_na from global environment.
## This could cause incorrect results if
## Phenotypes_Allergies_HFR_0_1_2_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_HFR_0_1_2_3_no_na from global environment.
## This could cause incorrect results if
## Phenotypes_Allergies_HFR_0_1_2_3_no_na has been altered since the model was
## fit. You can manually provide the data to the "data =" argument.