CODEWITHSUNDEEP
rregressiondata-sciencedata-analysis
krish___na
krish___na

Reputation: 722

Why convert numbers to factors while model bulding

I was following a tutorial on model building using logistic regression. In the tutorial, columns having numeric data type and with levels 3, were converted into factors using as.factor function. I wanted to know the reason for this conversion.

Upvotes: 1

Views: 964

Answers (1)

IRTFM
IRTFM

Reputation: 263301

If vectors of class-"numeric" with a small number of unique values are left in that form, logistic regression, i.e. glm( form, family="binomial", ...), will return a single coefficient. Generally, that is not what the data will support, so the authors of that tutorial are advising that these vectors be converted to factors so that the default handling of categorical values by the glm function will occur. It's possible that those authors already know for a fact that the underlying data-gathering process has encoded categorical data with numeric levels and the data input process was not "told" to process as categorical. That could have been done using the colClasses parameter to whichever read.* function was used.

The default handling of factors by most R regression routines uses the first level as part of the baseline (Intercept) estimate and estimates a coefficient for each of the other levels. If you had left that vector as numeric you would have gotten an estimate that could have been interpreted as the slope of an effect of an ordinal variable. The statistical tests associated with such an encoding of an ordinal relationship is often called a "linear test of trend" and is sometimes a useful result when the data situation in the "real world" can be interpreted as an ordinal relationship.

Upvotes: 3

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