I have a logistic model with plenty of interactions in R.
I want to extract only the variables and interactions that are either interactions or just predictor variables that are significant.
It's fine if I can just look at every interaction that's significant as I can still look at which non-significant fields were used to get them.
Thank you.
This is the most I have
broom::tidy(logmod)[,c("term", "estimate", "p.value")]
Here is a way. After fitting the logistic model use a logical condition to get the significant predictors and a regex (logical grep) to get the interactions. These two index vectors can be combined with &, in the case below returning no significant interactions at the alpha == 0.05 level.
fit <- glm(am ~ hp + qsec*vs, mtcars, family = binomial)
summary(fit)
#>
#> Call:
#> glm(formula = am ~ hp + qsec * vs, family = binomial, data = mtcars)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -1.93876 -0.09923 -0.00014 0.05351 1.33693
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 199.02697 102.43134 1.943 0.0520 .
#> hp -0.12104 0.06138 -1.972 0.0486 *
#> qsec -10.87980 5.62557 -1.934 0.0531 .
#> vs -108.34667 63.59912 -1.704 0.0885 .
#> qsec:vs 6.72944 3.85348 1.746 0.0808 .
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 43.230 on 31 degrees of freedom
#> Residual deviance: 12.574 on 27 degrees of freedom
#> AIC: 22.574
#>
#> Number of Fisher Scoring iterations: 8
alpha <- 0.05
pval <- summary(fit)$coefficients[,4]
sig <- pval <= alpha
intr <- grepl(":", names(coef(fit)))
coef(fit)[sig]
#> hp
#> -0.1210429
coef(fit)[sig & intr]
#> named numeric(0)
Created on 2022-09-15 with reprex v2.0.2
