ggeffects | Estimated Marginal Means and Marginal Effects | Data Visualization library

I fitted a glm model and had to transform some variables with log1p. I now want to create a ggpredict plot with a backtransformed scale. I transformed the variables before using the glm function.

here's a sample of a few variables of my original data and my code:

I would like to get a plot like ggplot2::geom_smooth(method = "lm", fullrange = T) with ggeffects::ggpredict(). The fit should only span the data and not the whole plot.

Can somebody help?

Example

Can someone advise how to analyse if y is significantly decreasing with increasing x using R?

I am working with ggeffects package I have the following syntax

I have a question about predictions using ggeffects, which is giving me completely different results if I use a traditional lm fit or an extracted parsnip model fit (despite having the same coefficients). Here is an example...

I am not entirely sure what I am doing wrong/what to look up. My objective is to use ggpredict and ggplot to display the relationship between time and the proportion of years burnt. I'm guessing it is something to do with the time variable being log transformed?

I have a dataset in a long format of 60 repeated measurements taken within 19 patients (ID). Patients have had a differing amount of measurements (2 measurements [n=11], followed by 5 measurements [n=5], 3 [n=1], 4 [n=1], and 6 measurements [n=1], with varying time-intervals (fu_time measured in years). The data looks as follows:

I'd like to plot the relationship between the number of ladenant response variable in function of Bioma (categorical) and temp (numeric) using binomial negative generalized linear mixed models (GLMMs) without success. I try to do:

These are three different ways to run an individual fixed effect method which gives more or less the same results (see below). My main question is how to get predictive probabilities or average marginal effects using the second model (model_plm) or the third model(model_felm). I know how to do it using the first model (model_lm) and show an example below using ggeffects, but that only works when i have a small sample.

As i have over a million individual, my model only works using model_plm and model_felm. If i use model_lm, it takes a lot of time to run with one million individuals since they are controlled for in the model. I also get the following error: Error: vector memory exhausted (limit reached?). I checked many threads on StackOverflow to work around that error but nothing seems to solve it.

I was wondering whether there is an efficient way to work around this issue. My main interest is to extract the predicted probabilities of the interaction residence*union. I usually extract predictive probabilities or average marginal effects using one of these packages: ggeffects,emmeans or margins.