General problem:
I am trying to visualize model estimates (coefficients) of a hierarchical generalized mixed model together with the factorial design of an experiment in one plot using R.
I do not intend to plot the predicted values for the response variables for different coefficient values (as discussed here), I intend to actually plot the coefficients from the model, as these are my DV. I also asked a related question (together with more background information, the model and an example figure; focusing on appropriatedness) on StackExchange Psychology & Neuroscience here, and another related question (focusing on data visualization) at StackExchange Cross Validated here.
Concrete Question:
One option would be to extract model parameters and sum up intercepts and estimates by hand/code. Are there recommondations on how to best accomplish it? My actual attempt would be
BROOM <- data.frame(broom.mixed::tidy(mymodel))
rownames(BROOM) <- BROOM$term
"combination of conditions A" <- BROOM["(Intercept)", "estimate"]
"combination of conditions B" <- BROOM["(Intercept)", "estimate"] + BROOM["condition B", "estimate"]
...
and then assign the values "combination of conditions X" to corresponding cells of a dataframe, and use ggplot to plot it the way I use to plot descriptives. The code above works fine. I will add a minimal reproducible example at a later edit if needed. My question is quite general whether there are more efficient ways to call model parameters (lme4::glmer model) so that they can be used in calculations.
Any hints and answers would be appreciated.