## [1] "There are 81892 individuals without missing data in this analysis."
## Estimate SE p OR
## (Intercept) -1.71 0.01 0.00e+00 0.18
## CA_GroupHigh_CA -0.37 0.05 7.00e-13 0.69
## CA_GroupLow_CA 0.27 0.09 1.36e-03 1.31
## Sex 0.59 0.02 1.30e-159 1.80
## scale(max_age, scale = FALSE) -0.03 0.00 9.34e-84 0.97
## I(scale(max_age, scale = FALSE)^2) 0.00 0.00 4.89e-11 1.00
## CA_GroupHigh_CA:Sex -0.11 0.08 1.66e-01 0.90
## CA_GroupLow_CA:Sex 0.02 0.13 9.00e-01 1.02
## CA_GroupHigh_CA:scale(max_age, scale = FALSE) 0.00 0.01 4.20e-01 1.00
## CA_GroupLow_CA:scale(max_age, scale = FALSE) -0.02 0.01 8.58e-02 0.98
## Sex:scale(max_age, scale = FALSE) 0.00 0.00 6.19e-01 1.00
## CA_GroupHigh_CA:I(scale(max_age, scale = FALSE)^2) 0.00 0.00 3.47e-01 1.00
## CA_GroupLow_CA:I(scale(max_age, scale = FALSE)^2) 0.00 0.00 7.64e-01 1.00
## CA_GroupHigh_CA:Sex:scale(max_age, scale = FALSE) -0.01 0.01 3.01e-01 0.99
## CA_GroupLow_CA:Sex:scale(max_age, scale = FALSE) 0.01 0.02 6.27e-01 1.01
There are no influential observations in our data.
## # A tibble: 3 × 12
## Anxiety_General CA_Group Sex `scale(max_age, …`[,1] `I(scale(m…`[,1] .fitted
## <dbl> <fct> <dbl> <dbl> <I<dbl>> <dbl>
## 1 1 Low_CA -0.5 12.5 155. -2.48
## 2 1 Low_CA -0.5 13.3 177. -2.54
## 3 1 Low_CA -0.5 14.0 197. -2.60
## # … with 6 more variables: .resid <dbl>, .std.resid <dbl>, .hat <dbl>,
## # .sigma <dbl>, .cooksd <dbl>, index <int>
## # A tibble: 0 × 12
## # … with 12 variables: Anxiety_General <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>
## Estimate SE
## (Intercept) -1.7096532973 0.0145874176
## CA_GroupHigh_CA -0.3715564109 0.0517524601
## CA_GroupLow_CA 0.2726478694 0.0851273947
## Sex 0.5908864083 0.0219502041
## scale(max_age, scale = FALSE) -0.0305604438 0.0015760772
## I(scale(max_age, scale = FALSE)^2) -0.0011485495 0.0001747033
## CA_GroupHigh_CA:Sex -0.1087059636 0.0784496936
## CA_GroupLow_CA:Sex 0.0160374850 0.1280505566
## CA_GroupHigh_CA:scale(max_age, scale = FALSE) 0.0045155111 0.0056002289
## CA_GroupLow_CA:scale(max_age, scale = FALSE) -0.0152083516 0.0088531749
## Sex:scale(max_age, scale = FALSE) -0.0014179091 0.0028517743
## CA_GroupHigh_CA:I(scale(max_age, scale = FALSE)^2) 0.0005715768 0.0006075441
## CA_GroupLow_CA:I(scale(max_age, scale = FALSE)^2) 0.0002953636 0.0009834927
## CA_GroupHigh_CA:Sex:scale(max_age, scale = FALSE) -0.0103007387 0.0099513358
## CA_GroupLow_CA:Sex:scale(max_age, scale = FALSE) 0.0078194359 0.0160771939
## t/z p
## (Intercept) -117.2005455 0.000000e+00
## CA_GroupHigh_CA -7.1794927 6.997055e-13
## CA_GroupLow_CA 3.2028217 1.360882e-03
## Sex 26.9194039 1.301950e-159
## scale(max_age, scale = FALSE) -19.3901950 9.338226e-84
## I(scale(max_age, scale = FALSE)^2) -6.5742881 4.888649e-11
## CA_GroupHigh_CA:Sex -1.3856774 1.658454e-01
## CA_GroupLow_CA:Sex 0.1252434 9.003309e-01
## CA_GroupHigh_CA:scale(max_age, scale = FALSE) 0.8063083 4.200651e-01
## CA_GroupLow_CA:scale(max_age, scale = FALSE) -1.7178415 8.582552e-02
## Sex:scale(max_age, scale = FALSE) -0.4972024 6.190463e-01
## CA_GroupHigh_CA:I(scale(max_age, scale = FALSE)^2) 0.9407990 3.468079e-01
## CA_GroupLow_CA:I(scale(max_age, scale = FALSE)^2) 0.3003211 7.639323e-01
## CA_GroupHigh_CA:Sex:scale(max_age, scale = FALSE) -1.0351112 3.006170e-01
## CA_GroupLow_CA:Sex:scale(max_age, scale = FALSE) 0.4863682 6.267061e-01
## Model
## (Intercept) Anxiety_General 0_3
## CA_GroupHigh_CA Anxiety_General 0_3
## CA_GroupLow_CA Anxiety_General 0_3
## Sex Anxiety_General 0_3
## scale(max_age, scale = FALSE) Anxiety_General 0_3
## I(scale(max_age, scale = FALSE)^2) Anxiety_General 0_3
## CA_GroupHigh_CA:Sex Anxiety_General 0_3
## CA_GroupLow_CA:Sex Anxiety_General 0_3
## CA_GroupHigh_CA:scale(max_age, scale = FALSE) Anxiety_General 0_3
## CA_GroupLow_CA:scale(max_age, scale = FALSE) Anxiety_General 0_3
## Sex:scale(max_age, scale = FALSE) Anxiety_General 0_3
## CA_GroupHigh_CA:I(scale(max_age, scale = FALSE)^2) Anxiety_General 0_3
## CA_GroupLow_CA:I(scale(max_age, scale = FALSE)^2) Anxiety_General 0_3
## CA_GroupHigh_CA:Sex:scale(max_age, scale = FALSE) Anxiety_General 0_3
## CA_GroupLow_CA:Sex:scale(max_age, scale = FALSE) Anxiety_General 0_3
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 3.756112 2 1.392146
## Sex 1.145778 1 1.070410
## scale(max_age, scale = FALSE) 1.484923 1 1.218574
## I(scale(max_age, scale = FALSE)^2) 1.423550 1 1.193126
## CA_Group:Sex 1.353232 2 1.078557
## CA_Group:scale(max_age, scale = FALSE) 2.022530 2 1.192542
## Sex:scale(max_age, scale = FALSE) 1.231281 1 1.109631
## CA_Group:I(scale(max_age, scale = FALSE)^2) 5.538318 2 1.534068
## CA_Group:Sex:scale(max_age, scale = FALSE) 1.318745 2 1.071618
## [1] "There are 81892 individuals without missing data in this analysis."
## Estimate SE p OR
## (Intercept) -1.68 0.01 0.00e+00 0.19
## G_std -0.14 0.01 8.40e-35 0.87
## Sex 0.57 0.02 3.98e-157 1.77
## scale(max_age, scale = FALSE) -0.03 0.00 1.87e-96 0.97
## I(scale(max_age, scale = FALSE)^2) 0.00 0.00 1.32e-11 1.00
## G_std:Sex -0.01 0.02 5.42e-01 0.99
## G_std:scale(max_age, scale = FALSE) 0.00 0.00 3.32e-02 1.00
## Sex:scale(max_age, scale = FALSE) 0.00 0.00 6.23e-01 1.00
## G_std:I(scale(max_age, scale = FALSE)^2) 0.00 0.00 5.97e-01 1.00
## G_std:Sex:scale(max_age, scale = FALSE) 0.00 0.00 4.22e-01 1.00
## Using data Anxiety_DF_items_0_3 from global environment. This could cause
## incorrect results if Anxiety_DF_items_0_3 has been altered since the model
## was fit. You can manually provide the data to the "data =" argument.
## Using data Anxiety_DF_items_0_3 from global environment. This could cause
## incorrect results if Anxiety_DF_items_0_3 has been altered since the model
## was fit. You can manually provide the data to the "data =" argument.