## [1] "There are 121993 individuals without missing data in this analysis."
## Std Beta SE p
## (Intercept) 0.02 0.00 1.35e-04
## CA_GroupHigh_CA 0.01 0.01 3.36e-01
## CA_GroupLow_CA -0.12 0.03 7.45e-06
## scale(max_age_MHQ) 0.07 0.00 1.72e-111
## Sex 0.02 0.01 2.73e-03
## I(scale(max_age_MHQ)^2) -0.02 0.00 1.65e-10
## CA_GroupHigh_CA:scale(max_age_MHQ) 0.00 0.01 8.32e-01
## CA_GroupLow_CA:scale(max_age_MHQ) 0.07 0.02 2.40e-04
## CA_GroupHigh_CA:Sex -0.05 0.02 1.86e-02
## CA_GroupLow_CA:Sex 0.01 0.04 7.81e-01
## scale(max_age_MHQ):Sex -0.06 0.01 1.20e-25
## CA_GroupHigh_CA:I(scale(max_age_MHQ)^2) 0.01 0.01 3.94e-01
## CA_GroupLow_CA:I(scale(max_age_MHQ)^2) -0.01 0.02 5.52e-01
## CA_GroupHigh_CA:scale(max_age_MHQ):Sex -0.01 0.02 6.65e-01
## CA_GroupLow_CA:scale(max_age_MHQ):Sex 0.02 0.04 5.92e-01
Please not that violations of assumptions are likely due to the ordinal characteristic of the neuroticism score
If there is no pattern in the residual plot. This suggests that we can assume linear relationship between the predictors and the outcome variables.
It’s good if you see a horizontal line with equally spread points.
The normal probability plot of residuals should approximately follow a straight line.
If there is no outliers that exceed 3 standard deviations, it is good.
A rule of thumb is that an observation has high influence if Cook’s distance exceeds 4/(n - p - 1)(P. Bruce and Bruce 2017), where n is the number of observations and p the number of predictor variables. The Residuals vs Leverage plot can help us to find influential observations if any. On this plot, outlying values are generally located at the upper right corner or at the lower right corner. Those spots are the places where data points can be influential against a regression line.
## [1] "There are 121993 individuals without missing data in this analysis."
## Std Beta SE p
## (Intercept) 0.00 0.00 2.83e-01
## G_std 0.02 0.00 1.25e-12
## Sex 0.02 0.01 4.73e-03
## scale(max_age_MHQ) 0.08 0.00 1.23e-134
## I(scale(max_age_MHQ)^2) -0.02 0.00 4.20e-09
## G_std:Sex -0.01 0.01 1.76e-01
## G_std:scale(max_age_MHQ) -0.01 0.00 2.39e-05
## Sex:scale(max_age_MHQ) -0.06 0.01 7.18e-25
## G_std:I(scale(max_age_MHQ)^2) 0.00 0.00 4.41e-01
## Sex:I(scale(max_age_MHQ)^2) 0.00 0.01 4.29e-01
## G_std:Sex:scale(max_age_MHQ) -0.01 0.00 2.15e-01
## G_std:Sex:I(scale(max_age_MHQ)^2) 0.00 0.00 4.33e-01
## `geom_smooth()` using formula 'y ~ x'