How do you get the means from GLM in r

Question

Looking to get the means of effects in R from a GLM. I can reliably get the predicted effects using, , however I really could do with the means.

library(ggeffects)
data(Cowles, package = "carData")
cowles.mod <- glm(volunteer ~ sex + neuroticism*extraversion, data=Cowles, family=binomial)

summary(cowles.mod)

## Call:
##   glm(formula = volunteer ~ sex + neuroticism * extraversion, family = binomial, 
##       data = Cowles)
## 
## Deviance Residuals: 
##   Min       1Q   Median       3Q      Max  
## -1.4749  -1.0602  -0.8934   1.2609   1.9978  
## 
## Coefficients:
##   Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              -2.358207   0.501320  -4.704 2.55e-06 ***
##   sexmale                  -0.247152   0.111631  -2.214  0.02683 *  
##   neuroticism               0.110777   0.037648   2.942  0.00326 ** 
##   extraversion              0.166816   0.037719   4.423 9.75e-06 ***
##   neuroticism:extraversion -0.008552   0.002934  -2.915  0.00355 ** 
##   ---
##   Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
## Null deviance: 1933.5  on 1420  degrees of freedom
## Residual deviance: 1897.4  on 1416  degrees of freedom
## AIC: 1907.4
## 
## Number of Fisher Scoring iterations: 4

pr <- ggpredict(cowles.mod, c("neuroticism", "extraversion"), type = "fe")

Answer 1

I don't have access to your data so I used different data to illustrate the answer

require(dplyr)
require(tidyr)

data(mtcars)


cars.mod <- lm(mpg ~ ., data = mtcars)

cars_mod_df <- cars.mod %>%  tidy 

cars_mod_df 



        term    estimate   std.error  statistic    p.value
1  (Intercept) 12.30337416 18.71788443  0.6573058 0.51812440
2          cyl -0.11144048  1.04502336 -0.1066392 0.91608738
3         disp  0.01333524  0.01785750  0.7467585 0.46348865
4           hp -0.02148212  0.02176858 -0.9868407 0.33495531
5         drat  0.78711097  1.63537307  0.4813036 0.63527790
6           wt -3.71530393  1.89441430 -1.9611887 0.06325215
7         qsec  0.82104075  0.73084480  1.1234133 0.27394127
8           vs  0.31776281  2.10450861  0.1509915 0.88142347
9           am  2.52022689  2.05665055  1.2254035 0.23398971
10        gear  0.65541302  1.49325996  0.4389142 0.66520643
11        carb -0.19941925  0.82875250 -0.2406258 0.81217871