## [1] "There are 102837 individuals without missing data in this analysis."
## Estimate SE p OR
## (Intercept) -1.73 0.01 0.00e+00 0.18
## CA_GroupHigh_CA -0.04 0.04 3.61e-01 0.96
## CA_GroupLow_CA 0.16 0.08 4.70e-02 1.17
## Sex 0.09 0.02 9.69e-07 1.09
## scale(max_age, scale = FALSE) -0.02 0.00 2.30e-34 0.98
## I(scale(max_age, scale = FALSE)^2) 0.00 0.00 3.01e-02 1.00
## CA_GroupHigh_CA:Sex 0.02 0.06 7.64e-01 1.02
## CA_GroupLow_CA:Sex 0.11 0.12 3.35e-01 1.12
## CA_GroupHigh_CA:scale(max_age, scale = FALSE) 0.01 0.00 1.64e-01 1.01
## CA_GroupLow_CA:scale(max_age, scale = FALSE) 0.00 0.01 9.03e-01 1.00
## Sex:scale(max_age, scale = FALSE) 0.00 0.00 1.06e-01 1.00
## CA_GroupHigh_CA:I(scale(max_age, scale = FALSE)^2) 0.00 0.00 7.03e-01 1.00
## CA_GroupLow_CA:I(scale(max_age, scale = FALSE)^2) 0.00 0.00 5.04e-01 1.00
## CA_GroupHigh_CA:Sex:scale(max_age, scale = FALSE) 0.00 0.01 7.73e-01 1.00
## CA_GroupLow_CA:Sex:scale(max_age, scale = FALSE) 0.01 0.02 3.48e-01 1.01
There is no influential observations in our data.
## # A tibble: 3 × 12
## Asthma_Binary CA_Group Sex `scale(max_…`[,1] `I(scale(m…`[,1] .fitted .resid
## <dbl> <fct> <dbl> <dbl> <I<dbl>> <dbl> <dbl>
## 1 1 Low_CA -0.5 -16.7 277. -1.33 1.77
## 2 1 Low_CA -0.5 13.7 187. -2.08 2.10
## 3 1 Low_CA 0.5 -17.5 306. -1.43 1.81
## # … with 5 more variables: .std.resid <dbl>, .hat <dbl>, .sigma <dbl>,
## # .cooksd <dbl>, index <int>
## # A tibble: 0 × 12
## # … with 12 variables: Asthma_Binary <dbl>, CA_Group <fct>, Sex <dbl>,
## # scale(max_age, scale = FALSE) <dbl[,1]>,
## # I(scale(max_age, 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 GVIF^(1/(2*Df))
## CA_Group 3.739348 2 1.390590
## Sex 1.155282 1 1.074840
## scale(max_age, scale = FALSE) 1.342328 1 1.158589
## I(scale(max_age, scale = FALSE)^2) 1.334435 1 1.155178
## CA_Group:Sex 1.245234 2 1.056362
## CA_Group:scale(max_age, scale = FALSE) 1.543402 2 1.114602
## Sex:scale(max_age, scale = FALSE) 1.170066 1 1.081696
## CA_Group:I(scale(max_age, scale = FALSE)^2) 4.629894 2 1.466874
## CA_Group:Sex:scale(max_age, scale = FALSE) 1.207079 2 1.048175
## [1] "There are 102837 individuals without missing data in this analysis."
## Estimate SE p OR
## (Intercept) -1.73 0.01 0.00e+00 0.18
## G_std 0.00 0.01 6.83e-01 1.00
## Sex 0.10 0.02 9.88e-08 1.11
## scale(max_age, scale = FALSE) -0.01 0.00 3.99e-31 0.99
## I(scale(max_age, scale = FALSE)^2) 0.00 0.00 1.22e-02 1.00
## G_std:Sex -0.01 0.01 4.19e-01 0.99
## G_std:scale(max_age, scale = FALSE) 0.00 0.00 2.31e-01 1.00
## Sex:scale(max_age, scale = FALSE) 0.00 0.00 6.19e-02 1.00
## G_std:I(scale(max_age, scale = FALSE)^2) 0.00 0.00 2.75e-01 1.00
## G_std:Sex:scale(max_age, scale = FALSE) 0.00 0.00 2.69e-01 1.00
## Using data Asthma_DF_items_0_3 from global environment. This could cause
## incorrect results if Asthma_DF_items_0_3 has been altered since the model
## was fit. You can manually provide the data to the "data =" argument.
## Using data Asthma_DF_items_0_3 from global environment. This could cause
## incorrect results if Asthma_DF_items_0_3 has been altered since the model
## was fit. You can manually provide the data to the "data =" argument.