4 5 6 1 4 Statsmodels Iolib Summary2 Summary Col

Leo Migdal

-Dec 4, 2025, 9:49 AM

4 5 6 1 4 statsmodels iolib summary2 summary col

Summarize multiple results instances side-by-side (coefs and SEs) results : statsmodels results instance or list of result instances float format for coefficients and standard errors Default : ‘%.4f’ model_names : list of strings of length len(results) if the names are not unique, a roman number will be appended to all model names Communities for your favorite technologies.

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Bring the best of human thought and AI automation together at your work. Instantly share code, notes, and snippets. Add the contents of a Numpy array to summary table add_base(results[, alpha, float_format, ...]) Try to construct a basic summary instance. add_df(df[, index, header, float_format, align])

Add the contents of a DataFrame to summary table There was an error while loading. Please reload this page. I report results of my models via statsmodels.iolib.summary2.summary_col. However, I have to work around the default implementation to fit my own requirements. These include:

I would like to modify summary_col along the lines of something like this: The features listed above a independent of each other. I'd be happy to raise a PR for each, but I'd prefer to check beforehand whether these would fit your roadmap — and if you have a better way to tackle my issues. Regarding including adj-R² only and N, I am aware I can use info_dict (what I'm currently doing). I know as well that I can write my own aggregator method, based on statsmodels.iolib.summary.Summary attributes. The summary_col function in statsmodels makes nice regression tables easy to create.

When you add a categorical variable to your model, it automatically adds a variable for each level. Sometimes, these coefficients have meaning and are of interest. However, this isn’t always true. For example, in an earlier page noted that you can modify a model from \(profits=a+b*investment+c*X+u\), where the focus is on understanding how investments translate to profits, to \(profits=a+b*investment+c*X+d*C(gsector)+e*C(year)+u\). The latter model is better, but the coefficients on gsector and year are not the focus (and are difficult to interpret). Aside: When a categorical variable has many levels, it is often called a “fixed effect”.

So the latter model, which adds industry and year to a regression as a categorical variable, is said to include “industry fixed effects” and “year fixed effect”. The point of industry fixed effects is usually not to understand the coefficients on the industry dummy variables. It is to “control for industry”, and it changes the interpretation of \(b\): It is the relationship between investment and profits, holding fixed the industry. The same goes for the year fixed effects. Thus, in the improved model, \(b\) shows the relationship for two firms in the same industry in the same year. When a categorical variable has a lot of levels, and seeing those values is not important, the output tables are easier to read if you drop those coefficients.

4 5 6 1 4 Statsmodels Iolib Summary2 Summary Col

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I Would Like To Modify Summary_col Along The Lines Of