How do I extract variables that have a low p-value in R

Question

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")]

Answer 1

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}