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I'm running a list of ordered logit models with different variables etc. I want to convert the output into a tidy tibble to use in ggplot etc. (I also want to save the 'regular model output' so I want to do this separately.)

I want to do this in an automated way, using purrr or lapply or some such, to be able to first 'run all the models' (automating that is another question for later) and then 'tidy all the models', the latter presumably generating a list of tibbles.

I've tried the following, but it throws: Error: No tidy method recognized for this list.

Where am I going wrong? I'm using Python 3.

I know that polr does not give p-values because they are not very reliable. Nevertheless, I would like to add them to my modelsummary (Vignette) output. I know to get the values as follows:

Cross posted on CrossValidated.

I am trying to bootstrap my results for a variation of the 2SLS approach (2SRI), based on this link. For some reason, the bootstrap does not produce any results.

I have done an ANOVA test on ordinal logistic regression, and I'm trying to export the results in Word, but tab_df doesn't print stars on p-values. How can I add stars to p-values? This is the code:

I'm using the polr function to run an ordered logistic regression. Reglog is my data frame and Reglog$wtp is the response variable. Here is a list of all the variables in my model.

1. wtp <- Reglog$WTP
2. perception<- Reglog$Persepsi
3. income <- Reglog$pendapatan
4. age <- Reglog$usia
5. education <- Reglog$usia
6. experience <- Reglog$pengalaman
7. home <- Reglog$rumah

At the first, wtp was chr, so I converted it into an ordered factor.

I am running an ordinal regression model. I have 8 explanatory variables, 4 of them categorical ('0' or '1') , 4 of them continuous. Beforehand I want to be sure there's no multicollinearity, so I use the variance inflation factor (vif function from the car package) :

I want to test for significant differences of the intercepts in an ordered logit model.

The goal is to make a single ordered logistic regression using multiple data frames with the same structure.

In the example below I joined two data frames to be able to do a single ordered logistic regression. However, I would like a solution that automates the process, since there are 417 data frames.