polr - choose of method and validating the model

Hi R community,

I am doing ordered logistic regression in R and I am using the polr() function from the MASS package.

First, I am becoming in drought of whatever or not I should specify method. Do you know what method R takes as a default when running the polr function? I can't find this information.

Secondly, for validating the ordered logistic regression how is it easiest to go about doing that. I have only done it for linear regression, and there you can easily plot the lm() model and get 4 plots that are nicely showing you if you fulfill the model assumptions or not.

Thank you in advance.


The MASS::polr function signature is

polr(formula, data, weights, start, ..., subset, na.action,
contrasts = NULL, Hess = FALSE, model = TRUE,
method = c("logistic", "probit", "loglog", "cloglog", "cauchit"))

Read that as there are the five methods taken as a default.

Go on to the Arguments section

method: logistic or probit or (complementary) log-log or cauchit (corresponding to a Cauchy latent variable)

Moving on to details

The vcov method uses the approximate Hessian: for reliable results the model matrix should be sensibly scaled with all columns having range the order of one

and other nuggets all within help(polr).

Finally, a couple of text books are provided for further reference.

This answer is not a rtfm, but a reminder that closely reading the help pages can be a big help when trying to navigate. As a beginner I had an aversion to reading them. I though that help() needed its own help page. I was abashed to find out that it does— help(help). It was only when I began to think of R as it presents to the user primarily as a functional language that I realized that the help should be read as presenting the arguments for f(x) = y and the return values. Since then, I've had a much easier time.

I run through the basics of assessing glm results here.

Thank you a lot for the comments.

I still find it hard with understanding which method that is chosen. You say all 5 methods are defaults - Dose R justify the method it self based on the data put into the model?

For validating the model . In the link you send, lm() is used for the model and thus you can simply plot(model) and get the 4 plots: 1, Residuals vs. fitted, 2. Normal Q-Q, 3. Scale-location (standardized residuals), and 4. residuals vs. leverage. Any idea of how to get that with polr()?

Thanks again.

The use of lm() on the model was to illustrate that the diagnostic plots do not work with glm() models using family = binomial.

Statistics is hard, and there is no magic wand to unlock the right answer. R provides an amazing set of tools but those tools aren't enough to without learning how to use them.

I do not see how this answer apply to me.

I have never asked nor looked for a magic wand, so please don't indicate that. I have asked 2 questions, and if you can't answer or don't want to use the time to answer, please just let the question slide.

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