## [1] "There are 261518 individuals without missing data in this analysis."
## Estimate SE
## (Intercept) 1.32 0.01
## CA_GroupHigh_CA -0.01 0.03
## CA_GroupLow_CA 0.08 0.04
## Sex 0.81 0.01
## scale(max_age_insomnia, scale = FALSE) 0.03 0.00
## I(scale((max_age_insomnia), scale = FALSE)^2) 0.00 0.00
## CA_GroupHigh_CA:Sex -0.03 0.05
## CA_GroupLow_CA:Sex -0.11 0.07
## CA_GroupHigh_CA:scale(max_age_insomnia, scale = FALSE) 0.01 0.00
## CA_GroupLow_CA:scale(max_age_insomnia, scale = FALSE) -0.01 0.00
## Sex:scale(max_age_insomnia, scale = FALSE) 0.01 0.00
## CA_GroupHigh_CA:I(scale((max_age_insomnia), scale = FALSE)^2) 0.00 0.00
## CA_GroupLow_CA:I(scale((max_age_insomnia), scale = FALSE)^2) 0.00 0.00
## Sex:I(scale((max_age_insomnia), scale = FALSE)^2) 0.00 0.00
## CA_GroupHigh_CA:Sex:scale(max_age_insomnia, scale = FALSE) 0.00 0.00
## CA_GroupLow_CA:Sex:scale(max_age_insomnia, scale = FALSE) 0.00 0.01
## CA_GroupHigh_CA:Sex:I(scale((max_age_insomnia), scale = FALSE)^2) 0.00 0.00
## CA_GroupLow_CA:Sex:I(scale((max_age_insomnia), scale = FALSE)^2) 0.00 0.00
## p OR
## (Intercept) 0.00e+00 3.74
## CA_GroupHigh_CA 7.91e-01 0.99
## CA_GroupLow_CA 3.00e-02 1.08
## Sex 0.00e+00 2.25
## scale(max_age_insomnia, scale = FALSE) 0.00e+00 1.03
## I(scale((max_age_insomnia), scale = FALSE)^2) 1.40e-07 1.00
## CA_GroupHigh_CA:Sex 6.10e-01 0.97
## CA_GroupLow_CA:Sex 1.23e-01 0.90
## CA_GroupHigh_CA:scale(max_age_insomnia, scale = FALSE) 1.62e-02 1.01
## CA_GroupLow_CA:scale(max_age_insomnia, scale = FALSE) 5.39e-03 0.99
## Sex:scale(max_age_insomnia, scale = FALSE) 3.72e-24 1.01
## CA_GroupHigh_CA:I(scale((max_age_insomnia), scale = FALSE)^2) 6.56e-01 1.00
## CA_GroupLow_CA:I(scale((max_age_insomnia), scale = FALSE)^2) 7.01e-01 1.00
## Sex:I(scale((max_age_insomnia), scale = FALSE)^2) 2.76e-44 1.00
## CA_GroupHigh_CA:Sex:scale(max_age_insomnia, scale = FALSE) 6.63e-01 1.00
## CA_GroupLow_CA:Sex:scale(max_age_insomnia, scale = FALSE) 5.19e-01 1.00
## CA_GroupHigh_CA:Sex:I(scale((max_age_insomnia), scale = FALSE)^2) 1.94e-01 1.00
## CA_GroupLow_CA:Sex:I(scale((max_age_insomnia), scale = FALSE)^2) 5.12e-01 1.00
There is no influential observations in our data.
## # A tibble: 3 × 12
## Insomnia CA_Group Sex `scale(max_age_i…`[,1] `I(scale((…`[,1] .fitted .resid
## <int> <fct> <dbl> <dbl> <I<dbl>> <dbl> <dbl>
## 1 0 Low_CA 0.5 22.1 490. 2.06 -2.09
## 2 0 Low_CA 0.5 20.1 405. 2.07 -2.09
## 3 0 Low_CA 0.5 21.2 450. 2.07 -2.09
## # … with 5 more variables: .std.resid <dbl>, .hat <dbl>, .sigma <dbl>,
## # .cooksd <dbl>, index <int>
## # A tibble: 0 × 12
## # … with 12 variables: Insomnia <int>, CA_Group <fct>, Sex <dbl>,
## # scale(max_age_insomnia, scale = FALSE) <dbl[,1]>,
## # I(scale((max_age_insomnia), 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 3.593480 2
## Sex 2.023036 1
## scale(max_age_insomnia, scale = FALSE) 1.346822 1
## I(scale((max_age_insomnia), scale = FALSE)^2) 1.327494 1
## CA_Group:Sex 3.965183 2
## CA_Group:scale(max_age_insomnia, scale = FALSE) 1.720304 2
## Sex:scale(max_age_insomnia, scale = FALSE) 1.363791 1
## CA_Group:I(scale((max_age_insomnia), scale = FALSE)^2) 4.844164 2
## Sex:I(scale((max_age_insomnia), scale = FALSE)^2) 2.371873 1
## CA_Group:Sex:scale(max_age_insomnia, scale = FALSE) 1.723317 2
## CA_Group:Sex:I(scale((max_age_insomnia), scale = FALSE)^2) 5.053809 2
## GVIF^(1/(2*Df))
## CA_Group 1.376825
## Sex 1.422335
## scale(max_age_insomnia, scale = FALSE) 1.160526
## I(scale((max_age_insomnia), scale = FALSE)^2) 1.152169
## CA_Group:Sex 1.411126
## CA_Group:scale(max_age_insomnia, scale = FALSE) 1.145253
## Sex:scale(max_age_insomnia, scale = FALSE) 1.167815
## CA_Group:I(scale((max_age_insomnia), scale = FALSE)^2) 1.483559
## Sex:I(scale((max_age_insomnia), scale = FALSE)^2) 1.540089
## CA_Group:Sex:scale(max_age_insomnia, scale = FALSE) 1.145754
## CA_Group:Sex:I(scale((max_age_insomnia), scale = FALSE)^2) 1.499356
## [1] "There are 261518 individuals without missing data in this analysis."
## Estimate SE p OR
## (Intercept) 1.32 0.01 0.00e+00 3.74
## G_std -0.01 0.01 1.22e-01 0.99
## Sex 0.67 0.01 0.00e+00 1.95
## scale(max_age_insomnia, scale = FALSE) 0.04 0.00 0.00e+00 1.04
## I(scale(max_age_insomnia, scale = FALSE)^2) 0.00 0.00 3.17e-03 1.00
## G_std:Sex 0.00 0.01 8.39e-01 1.00
## G_std:scale(max_age_insomnia, scale = FALSE) 0.00 0.00 7.43e-14 1.00
## Sex:scale(max_age_insomnia, scale = FALSE) 0.02 0.00 1.63e-71 1.02
## G_std:I(scale(max_age_insomnia, scale = FALSE)^2) 0.00 0.00 3.81e-01 1.00
## G_std:Sex:scale(max_age_insomnia, scale = FALSE) 0.00 0.00 6.39e-01 1.00
## Using data Sleep_DF_no_NA from global environment. This could cause
## incorrect results if Sleep_DF_no_NA has been altered since the model was
## fit. You can manually provide the data to the "data =" argument.
## Using data Sleep_DF_no_NA from global environment. This could cause
## incorrect results if Sleep_DF_no_NA has been altered since the model was
## fit. You can manually provide the data to the "data =" argument.