## [1] "There are 123674 individuals without missing data in this analysis."
## Estimate SE
## (Intercept) -2.86 0.02
## CA_GroupHigh_CA -0.08 0.06
## CA_GroupLow_CA -0.07 0.12
## Sex -0.90 0.03
## scale(max_age_alcohol, scale = FALSE) -0.04 0.00
## I(scale(max_age_alcohol, scale = FALSE)^2) 0.00 0.00
## CA_GroupHigh_CA:Sex 0.23 0.10
## CA_GroupLow_CA:Sex -0.10 0.18
## CA_GroupHigh_CA:scale(max_age_alcohol, scale = FALSE) 0.01 0.01
## CA_GroupLow_CA:scale(max_age_alcohol, scale = FALSE) 0.00 0.01
## Sex:scale(max_age_alcohol, scale = FALSE) -0.02 0.00
## CA_GroupHigh_CA:I(scale(max_age_alcohol, scale = FALSE)^2) 0.00 0.00
## CA_GroupLow_CA:I(scale(max_age_alcohol, scale = FALSE)^2) 0.00 0.00
## CA_GroupHigh_CA:Sex:scale(max_age_alcohol, scale = FALSE) 0.00 0.01
## CA_GroupLow_CA:Sex:scale(max_age_alcohol, scale = FALSE) -0.03 0.02
## p OR
## (Intercept) 0.00e+00 0.06
## CA_GroupHigh_CA 2.16e-01 0.92
## CA_GroupLow_CA 5.62e-01 0.93
## Sex 9.97e-209 0.41
## scale(max_age_alcohol, scale = FALSE) 1.38e-93 0.96
## I(scale(max_age_alcohol, scale = FALSE)^2) 9.27e-08 1.00
## CA_GroupHigh_CA:Sex 1.94e-02 1.26
## CA_GroupLow_CA:Sex 5.92e-01 0.90
## CA_GroupHigh_CA:scale(max_age_alcohol, scale = FALSE) 1.44e-01 1.01
## CA_GroupLow_CA:scale(max_age_alcohol, scale = FALSE) 8.72e-01 1.00
## Sex:scale(max_age_alcohol, scale = FALSE) 3.23e-06 0.98
## CA_GroupHigh_CA:I(scale(max_age_alcohol, scale = FALSE)^2) 2.90e-01 1.00
## CA_GroupLow_CA:I(scale(max_age_alcohol, scale = FALSE)^2) 7.93e-01 1.00
## CA_GroupHigh_CA:Sex:scale(max_age_alcohol, scale = FALSE) 9.13e-01 1.00
## CA_GroupLow_CA:Sex:scale(max_age_alcohol, scale = FALSE) 2.32e-01 0.97
There is no influential observations in our data.
## # A tibble: 3 × 12
## Alcohol_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 -17.4 302. -2.29 2.19
## 2 1 Low_CA 0.5 12.6 160. -4.41 2.97
## 3 1 Low_CA -0.5 14.8 219. -2.96 2.45
## # … with 5 more variables: .std.resid <dbl>, .hat <dbl>, .sigma <dbl>,
## # .cooksd <dbl>, index <int>
## # A tibble: 0 × 12
## # … with 12 variables: Alcohol_Binary <dbl>, CA_Group <fct>, Sex <dbl>,
## # scale(max_age_alcohol, scale = FALSE) <dbl[,1]>,
## # I(scale(max_age_alcohol, scale = FALSE)^2) <I<dbl[,1]>[,1]>, .fitted <dbl>,
## # .resid <dbl>, .std.resid <dbl>, .hat <dbl>, .sigma <dbl>, .cooksd <dbl>,
## # index <int>
## Estimate
## (Intercept) -2.8608863851
## CA_GroupHigh_CA -0.0779471451
## CA_GroupLow_CA -0.0685916193
## Sex -0.8979203733
## scale(max_age_alcohol, scale = FALSE) -0.0428576148
## I(scale(max_age_alcohol, scale = FALSE)^2) -0.0012174145
## CA_GroupHigh_CA:Sex 0.2321466873
## CA_GroupLow_CA:Sex -0.0977094665
## CA_GroupHigh_CA:scale(max_age_alcohol, scale = FALSE) 0.0100950035
## CA_GroupLow_CA:scale(max_age_alcohol, scale = FALSE) -0.0020076551
## Sex:scale(max_age_alcohol, scale = FALSE) -0.0171567730
## CA_GroupHigh_CA:I(scale(max_age_alcohol, scale = FALSE)^2) 0.0007511362
## CA_GroupLow_CA:I(scale(max_age_alcohol, scale = FALSE)^2) 0.0003581208
## CA_GroupHigh_CA:Sex:scale(max_age_alcohol, scale = FALSE) 0.0013496781
## CA_GroupLow_CA:Sex:scale(max_age_alcohol, scale = FALSE) -0.0268877701
## SE
## (Intercept) 0.0188907934
## CA_GroupHigh_CA 0.0629767794
## CA_GroupLow_CA 0.1182677144
## Sex 0.0291236283
## scale(max_age_alcohol, scale = FALSE) 0.0020884099
## I(scale(max_age_alcohol, scale = FALSE)^2) 0.0002279574
## CA_GroupHigh_CA:Sex 0.0993087818
## CA_GroupLow_CA:Sex 0.1821370080
## CA_GroupHigh_CA:scale(max_age_alcohol, scale = FALSE) 0.0069019529
## CA_GroupLow_CA:scale(max_age_alcohol, scale = FALSE) 0.0124912739
## Sex:scale(max_age_alcohol, scale = FALSE) 0.0036851805
## CA_GroupHigh_CA:I(scale(max_age_alcohol, scale = FALSE)^2) 0.0007101435
## CA_GroupLow_CA:I(scale(max_age_alcohol, scale = FALSE)^2) 0.0013679796
## CA_GroupHigh_CA:Sex:scale(max_age_alcohol, scale = FALSE) 0.0123238945
## CA_GroupLow_CA:Sex:scale(max_age_alcohol, scale = FALSE) 0.0225141930
## t/z
## (Intercept) -151.4434217
## CA_GroupHigh_CA -1.2377125
## CA_GroupLow_CA -0.5799691
## Sex -30.8313362
## scale(max_age_alcohol, scale = FALSE) -20.5216491
## I(scale(max_age_alcohol, scale = FALSE)^2) -5.3405357
## CA_GroupHigh_CA:Sex 2.3376250
## CA_GroupLow_CA:Sex -0.5364614
## CA_GroupHigh_CA:scale(max_age_alcohol, scale = FALSE) 1.4626300
## CA_GroupLow_CA:scale(max_age_alcohol, scale = FALSE) -0.1607246
## Sex:scale(max_age_alcohol, scale = FALSE) -4.6556127
## CA_GroupHigh_CA:I(scale(max_age_alcohol, scale = FALSE)^2) 1.0577246
## CA_GroupLow_CA:I(scale(max_age_alcohol, scale = FALSE)^2) 0.2617881
## CA_GroupHigh_CA:Sex:scale(max_age_alcohol, scale = FALSE) 0.1095172
## CA_GroupLow_CA:Sex:scale(max_age_alcohol, scale = FALSE) -1.1942587
## p
## (Intercept) 0.000000e+00
## CA_GroupHigh_CA 2.158227e-01
## CA_GroupLow_CA 5.619355e-01
## Sex 9.966880e-209
## scale(max_age_alcohol, scale = FALSE) 1.379528e-93
## I(scale(max_age_alcohol, scale = FALSE)^2) 9.267235e-08
## CA_GroupHigh_CA:Sex 1.940671e-02
## CA_GroupLow_CA:Sex 5.916397e-01
## CA_GroupHigh_CA:scale(max_age_alcohol, scale = FALSE) 1.435686e-01
## CA_GroupLow_CA:scale(max_age_alcohol, scale = FALSE) 8.723103e-01
## Sex:scale(max_age_alcohol, scale = FALSE) 3.230187e-06
## CA_GroupHigh_CA:I(scale(max_age_alcohol, scale = FALSE)^2) 2.901810e-01
## CA_GroupLow_CA:I(scale(max_age_alcohol, scale = FALSE)^2) 7.934848e-01
## CA_GroupHigh_CA:Sex:scale(max_age_alcohol, scale = FALSE) 9.127923e-01
## CA_GroupLow_CA:Sex:scale(max_age_alcohol, scale = FALSE) 2.323768e-01
## Model
## (Intercept) Alcohol_Binary 0_3
## CA_GroupHigh_CA Alcohol_Binary 0_3
## CA_GroupLow_CA Alcohol_Binary 0_3
## Sex Alcohol_Binary 0_3
## scale(max_age_alcohol, scale = FALSE) Alcohol_Binary 0_3
## I(scale(max_age_alcohol, scale = FALSE)^2) Alcohol_Binary 0_3
## CA_GroupHigh_CA:Sex Alcohol_Binary 0_3
## CA_GroupLow_CA:Sex Alcohol_Binary 0_3
## CA_GroupHigh_CA:scale(max_age_alcohol, scale = FALSE) Alcohol_Binary 0_3
## CA_GroupLow_CA:scale(max_age_alcohol, scale = FALSE) Alcohol_Binary 0_3
## Sex:scale(max_age_alcohol, scale = FALSE) Alcohol_Binary 0_3
## CA_GroupHigh_CA:I(scale(max_age_alcohol, scale = FALSE)^2) Alcohol_Binary 0_3
## CA_GroupLow_CA:I(scale(max_age_alcohol, scale = FALSE)^2) Alcohol_Binary 0_3
## CA_GroupHigh_CA:Sex:scale(max_age_alcohol, scale = FALSE) Alcohol_Binary 0_3
## CA_GroupLow_CA:Sex:scale(max_age_alcohol, scale = FALSE) Alcohol_Binary 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 3.998696 2 1.414098
## Sex 1.246564 1 1.116497
## scale(max_age_alcohol, scale = FALSE) 1.607185 1 1.267748
## I(scale(max_age_alcohol, scale = FALSE)^2) 1.492123 1 1.221525
## CA_Group:Sex 1.646573 2 1.132779
## CA_Group:scale(max_age_alcohol, scale = FALSE) 2.554410 2 1.264220
## Sex:scale(max_age_alcohol, scale = FALSE) 1.300809 1 1.140530
## CA_Group:I(scale(max_age_alcohol, scale = FALSE)^2) 6.091183 2 1.570997
## CA_Group:Sex:scale(max_age_alcohol, scale = FALSE) 1.659774 2 1.135043
## [1] "There are 123674 individuals without missing data in this analysis."
## Estimate SE p OR
## (Intercept) -2.86 0.02 0.00e+00 0.06
## G_std -0.02 0.01 1.74e-01 0.98
## Sex -0.90 0.03 1.30e-213 0.41
## scale(max_age_alcohol, scale = FALSE) -0.04 0.00 9.74e-98 0.96
## I(scale(max_age_alcohol, scale = FALSE)^2) 0.00 0.00 1.66e-08 1.00
## G_std:Sex 0.05 0.02 1.70e-02 1.05
## G_std:scale(max_age_alcohol, scale = FALSE) 0.00 0.00 2.68e-02 1.00
## Sex:scale(max_age_alcohol, scale = FALSE) -0.02 0.00 4.52e-07 0.98
## G_std:I(scale(max_age_alcohol, scale = FALSE)^2) 0.00 0.00 4.89e-02 1.00
## G_std:Sex:scale(max_age_alcohol, scale = FALSE) 0.00 0.00 4.39e-01 1.00
## Using data Alcohol_DF_items_0_3 from global environment. This could cause
## incorrect results if Alcohol_DF_items_0_3 has been altered since the model
## was fit. You can manually provide the data to the "data =" argument.
## Using data Alcohol_DF_items_0_3 from global environment. This could cause
## incorrect results if Alcohol_DF_items_0_3 has been altered since the model
## was fit. You can manually provide the data to the "data =" argument.