Strauss | A Qt based DAW for windows | Audio Utils library

First, please learn how to make a minimal reproducible example. This will make it easier for people to help you solve the problem, without having to guess what was in your data.

In your example, the columns named Area and Subhabitat in the hyperframe H are character vectors, but in your code, the call to mppm would require that they are factors. I assume you converted them to factors in order to be able to fit the model fittest8. (Another reason to make a working example)

You said that your example was similar to one on page 700 of the spatstat book which does work. In that case, a good strategy is to modify your example to make it as similar as possible to the example that works, because this will narrow down the possible cause.

A working example of the problem, similar to the one in the book, is:

This is not currently implemented in a neat way for mppm, that is, for fitting models to several point pattern datasets. It is on the "to do" list. (For fitting models to a single point pattern dataset, see the last paragraph below.)

Your code is OK, except for one problem: it assumes that it is valid to compare the log pseudolikelihood values of two models fitted with different values of r. That's not always true because, by default, ppm and mppm use the border method of edge correction, and by default, the border distance rbord is chosen to be equal to the interaction distance r. In your code, rbord is different for each model, so the pseudolikelihoods are not strictly comparable (effectively the models are based on different "sample sizes").

To avoid this problem, you can either explicitly set the border distance rbord to be equal to the maximum value of r that will be used:

Problem: you are writing to file in the end only once but processing more student data in the for loop before. The x variable only stores the final value and writes the student 002 data only.

There are several ways to do this.

If you print a fitted model (obtained from ppm or from mppm) simply by typing the name of the object, the printed output contains a description of the fitted model including the model parameters.

If you apply the function parameters to a fitted model obtained from ppm you will obtain a list of the parameter values with appropriate names.

In German, ß is considered basically equal to ss.

While the xxx_unicode_ci collation respects that, the xxx_general_ci does not, so you switched from a collation that considers "straus@email.com" not the same as "strauß@email.com" to one that does.

Actually, xxx_general_ci treats ß and s the same, so it would complain about "straus@email.com" and "strauß@email.com" instead.

See the documentation:

_general_ci Versus _unicode_ci Collations

For any Unicode character set, operations performed using the xxx_general_ci collation are faster than those for the xxx_unicode_ci collation. For example, comparisons for the utf8_general_ci collation are faster, but slightly less correct, than comparisons for utf8_unicode_ci. The reason is that utf8_unicode_ci supports mappings such as expansions; that is, when one character compares as equal to combinations of other characters. For example, ß is equal to ss in German and some other languages. utf8_unicode_ci also supports contractions and ignorable characters. utf8_general_ci is a legacy collation that does not support expansions, contractions, or ignorable characters. It can make only one-to-one comparisons between characters.

Here's a conceptual example, but you can apply the same principle to your case, with the following JSON: