emmeans | Estimated marginal means | Development Tools library

I am very new shiny user. I wanted to show the results of post-hoc test using emmeans function. Here's the code of my shiny app.

I hoping get some can help solving this mystery in my mind. The 7th coefficient in my glmer() call is 0.28779305 on the logit scale.

This coefficient can be also obtained by using contrast() in the emmeans package. However, this package apparently outputs the 7th coefficient on a different scale, the response scale.

I wonder how to convert the estimate given by the contrast() call so it matches the 7th coefficient in my glmer() call?

ps. This answer provides some insight but I don't see a way the coefficient from these two packages might be related.

I am trying to use the effects package to create plots of effects in a linear mixed model. I specify the model

I have a dataset from participants that provided liking ratings (on a scale from 0-100) of stimuli associated with rewards of different magnitudes (factor pval, with levels small/medium/large) and delay (factor time, with levels delayed/immediate). A subset of the data looks like this:

The entirety of my code is running perfectly well. When I try to use Knitr to generate PDF/ HTML I get a error that says

Error in Anova(mod1, type = "III") : could not find function "Anova"

Any suggestions on what I can do differently?

I am doing a one-way ANCOVA analysis on root colonisation data using the following example https://www.datanovia.com/en/lessons/ancova-in-r/. I am trying to remove the trendline from the resulting plot as my treatments are separate groups so I don't think it is necessary. I have tried using each of the following but I can't get any to work with this code. Is there another way I can go about this? Thanks!

I would like to plot estimated marginal means from a three-way factorial experiment with letters indicating significantly different means, adjusted for multiple comparisons. My current workflow is to fit the model with lmer(), calculate estimated marginal means with emmeans(), then implement the compact letter display algorithm with cld().

My problem is that the graph is too busy when you plot all three-way interactions on the same plot. So I would like to split up the plot and generate different sets of letters for each subplot, starting with "a". The problem is that when I use the by argument in cld to split it up, it does a separate correction for multiple comparisons within each by group. Because there are now fewer tests within each group, this results in a less conservative correction. But if I try to manually split up the output of cld() without a by group, I would have to manually re-implement the letter algorithm for each subplot. I guess I could do that but it seems cumbersome. I am trying to share this code with a client for him to modify later, so that solution would probably be too complex. Does anyone have an easy way to either:

1. Get the output of cld() to use one combined correction for all by groups.
2. Using a relatively simple method, reduce the compact letter display for each subgroup to the minimal necessary number of letters.

Load packages and data.

I have this example where I'd like to have multiple comparisons across treatments. Here is the data:

I am looking for an alternative approach to plotting results from pairwise comparisons than traditional bar plots. If possible, I would like to create a plot like the one shown below [1], but for a model that includes an interaction effect. R code for the plot below is online [2]. Is there a way to revise or add onto this code to include an interaction effect?

Example of my data set (too large to include in its entirety but I can send upon request) and the model used: