## [1] "There are 239847 individuals without missing data in this analysis."
## Estimate SE
## (Intercept) -3.28 0.02
## CA_GroupHigh_CA 0.14 0.06
## CA_GroupLow_CA 0.10 0.09
## Sex -0.51 0.03
## scale(Age_Center_0, scale = FALSE) -0.06 0.00
## I(scale((Age_Center_0), scale = FALSE)^2) 0.00 0.00
## CA_GroupHigh_CA:Sex 0.36 0.12
## CA_GroupLow_CA:Sex -0.57 0.18
## CA_GroupHigh_CA:scale(Age_Center_0, scale = FALSE) 0.01 0.01
## CA_GroupLow_CA:scale(Age_Center_0, scale = FALSE) 0.00 0.01
## Sex:scale(Age_Center_0, scale = FALSE) -0.03 0.00
## CA_GroupHigh_CA:I(scale((Age_Center_0), scale = FALSE)^2) 0.00 0.00
## CA_GroupLow_CA:I(scale((Age_Center_0), scale = FALSE)^2) 0.00 0.00
## Sex:I(scale((Age_Center_0), scale = FALSE)^2) 0.00 0.00
## CA_GroupHigh_CA:Sex:scale(Age_Center_0, scale = FALSE) 0.01 0.01
## CA_GroupLow_CA:Sex:scale(Age_Center_0, scale = FALSE) -0.01 0.02
## CA_GroupHigh_CA:Sex:I(scale((Age_Center_0), scale = FALSE)^2) 0.00 0.00
## CA_GroupLow_CA:Sex:I(scale((Age_Center_0), scale = FALSE)^2) 0.00 0.00
## p OR
## (Intercept) 0.00e+00 0.04
## CA_GroupHigh_CA 1.26e-02 1.15
## CA_GroupLow_CA 2.49e-01 1.11
## Sex 6.80e-55 0.60
## scale(Age_Center_0, scale = FALSE) 1.43e-232 0.94
## I(scale((Age_Center_0), scale = FALSE)^2) 2.16e-05 1.00
## CA_GroupHigh_CA:Sex 2.06e-03 1.43
## CA_GroupLow_CA:Sex 1.13e-03 0.57
## CA_GroupHigh_CA:scale(Age_Center_0, scale = FALSE) 2.50e-01 1.01
## CA_GroupLow_CA:scale(Age_Center_0, scale = FALSE) 6.71e-01 1.00
## Sex:scale(Age_Center_0, scale = FALSE) 5.81e-16 0.97
## CA_GroupHigh_CA:I(scale((Age_Center_0), scale = FALSE)^2) 7.11e-01 1.00
## CA_GroupLow_CA:I(scale((Age_Center_0), scale = FALSE)^2) 8.44e-01 1.00
## Sex:I(scale((Age_Center_0), scale = FALSE)^2) 9.24e-03 1.00
## CA_GroupHigh_CA:Sex:scale(Age_Center_0, scale = FALSE) 6.29e-01 1.01
## CA_GroupLow_CA:Sex:scale(Age_Center_0, scale = FALSE) 7.61e-01 0.99
## CA_GroupHigh_CA:Sex:I(scale((Age_Center_0), scale = FALSE)^2) 6.97e-01 1.00
## CA_GroupLow_CA:Sex:I(scale((Age_Center_0), scale = FALSE)^2) 5.56e-01 1.00
There is no influential observations in our data.
## # A tibble: 3 × 13
## .rownames Same_Sex_Behavior_0 CA_Group Sex `scale(Age…`[,1] `I(scale((…`[,1]
## <chr> <int> <fct> <dbl> <dbl> <I<dbl>>
## 1 61797 1 Low_CA 0.5 12.3 152.
## 2 71948 1 Low_CA 0.5 12.2 148.
## 3 244815 1 Low_CA 0.5 11.8 140.
## # … with 7 more variables: .fitted <dbl>, .resid <dbl>, .std.resid <dbl>,
## # .hat <dbl>, .sigma <dbl>, .cooksd <dbl>, index <int>
## # A tibble: 86 × 13
## .rownames Same_Sex_Behavior… CA_Group Sex `scale(Age…`[,1] `I(scale((…`[,1]
## <chr> <int> <fct> <dbl> <dbl> <I<dbl>>
## 1 2035 1 Average… 0.5 11.5 132.
## 2 3763 1 Low_CA 0.5 11.0 121.
## 3 6446 1 Average… 0.5 13.3 178.
## 4 8911 1 Average… 0.5 12.4 154.
## 5 17930 1 Average… 0.5 12.9 167.
## 6 19671 1 Average… 0.5 12.9 167.
## 7 25090 1 Average… 0.5 11.0 121.
## 8 28074 1 Low_CA 0.5 9.01 81.1
## 9 37634 1 Average… 0.5 11.4 130.
## 10 42431 1 Average… 0.5 11.5 132.
## # … with 76 more rows, and 7 more variables: .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 4.887317 2
## Sex 2.319507 1
## scale(Age_Center_0, scale = FALSE) 1.884005 1
## I(scale((Age_Center_0), scale = FALSE)^2) 1.772423 1
## CA_Group:Sex 5.455021 2
## CA_Group:scale(Age_Center_0, scale = FALSE) 5.693867 2
## Sex:scale(Age_Center_0, scale = FALSE) 2.162439 1
## CA_Group:I(scale((Age_Center_0), scale = FALSE)^2) 13.789572 2
## Sex:I(scale((Age_Center_0), scale = FALSE)^2) 3.563730 1
## CA_Group:Sex:scale(Age_Center_0, scale = FALSE) 5.780792 2
## CA_Group:Sex:I(scale((Age_Center_0), scale = FALSE)^2) 14.529879 2
## GVIF^(1/(2*Df))
## CA_Group 1.486852
## Sex 1.522993
## scale(Age_Center_0, scale = FALSE) 1.372590
## I(scale((Age_Center_0), scale = FALSE)^2) 1.331324
## CA_Group:Sex 1.528267
## CA_Group:scale(Age_Center_0, scale = FALSE) 1.544727
## Sex:scale(Age_Center_0, scale = FALSE) 1.470523
## CA_Group:I(scale((Age_Center_0), scale = FALSE)^2) 1.927027
## Sex:I(scale((Age_Center_0), scale = FALSE)^2) 1.887784
## CA_Group:Sex:scale(Age_Center_0, scale = FALSE) 1.550589
## CA_Group:Sex:I(scale((Age_Center_0), scale = FALSE)^2) 1.952385
## [1] "There are 239847 individuals without missing data in this analysis."
## Estimate SE p OR
## (Intercept) -3.27 0.02 0.00e+00 0.04
## G_std 0.03 0.01 7.50e-03 1.03
## Sex -0.61 0.02 1.91e-141 0.54
## scale(Age_Center_0, scale = FALSE) -0.06 0.00 6.10e-259 0.94
## I(scale(Age_Center_0, scale = FALSE)^2) 0.00 0.00 1.57e-04 1.00
## G_std:Sex 0.23 0.02 9.72e-30 1.26
## G_std:scale(Age_Center_0, scale = FALSE) 0.00 0.00 4.35e-03 1.00
## Sex:scale(Age_Center_0, scale = FALSE) -0.02 0.00 2.26e-18 0.98
## G_std:I(scale(Age_Center_0, scale = FALSE)^2) 0.00 0.00 5.74e-01 1.00
## G_std:Sex:scale(Age_Center_0, scale = FALSE) 0.01 0.00 1.15e-03 1.01
## Using data Same_Sex_Behavior_DF_items_0 from global environment. This could
## cause incorrect results if Same_Sex_Behavior_DF_items_0 has been altered
## since the model was fit. You can manually provide the data to the "data ="
## argument.
## Using data Same_Sex_Behavior_DF_items_0 from global environment. This could
## cause incorrect results if Same_Sex_Behavior_DF_items_0 has been altered
## since the model was fit. You can manually provide the data to the "data ="
## argument.