(Note: This question helps to inform the current one)
I would like to identify variables that are significant at the 95% level in a logistic regression but have very little to no impact on the response. I've read the CV questions on interpreting regression output. And have also read the Stanford and UCLA links on interpretation. I used the combination of knowledge I've gained to create a table to determine which predictors are either not significant or have little to no effect on the response. But I am not sure I am coming to the correct conclusions, especially how confidence intervals play a role in log odds ratios:
library(broom) #for tidy model output mdl1 <- glm(am ~ mpg + disp, mtcars, family=binomial) out <- tidy(mdl1) out[-1] <- round(out[-1], 4) out$significant <- out$p.value < 0.05 cbind(out[-(1:2)], round(exp(cbind(OR = coef(mdl1), confint(mdl1))), 4)) # Waiting for profiling to be done... # std.error statistic p.value significant OR 2.5 % 97.5 % # (Intercept) 4.7601 -0.4741 0.6354 FALSE 0.1047 0.0000 1192.7499 # mpg 0.1684 1.0095 0.3127 FALSE 1.1853 0.8727 1.7252 # disp 0.0078 -0.9749 0.3296 FALSE 0.9924 0.9749 1.0064
This appears to be a good start. I know the log odds, p values, and confidence intervals for each variable. This case would be easy since none of the predictors are significant. But let's ignore that for the moment. If I they were significant and I wanted to see how the confidence intervals can help determine the effect of the predictors, can I use confidence intervals that include
I ask because this Brandon Foltz tutorial says to remove such variables (around the five minute mark). So these variables would be removed because they satisfy the condition that with 95% confidence the true coefficient includes
1.00 which would indicate a non-effect on the response.
Is this two-step process a good way of using logistic regression output to understand the effect of the predictors?