Documentation

Manipulate Category Levels

When working with categorical variables and their levels, you’ll encounter some typical challenges. This table summarizes the functions you can use with nominal or ordinal arrays to manipulate category levels. For additional functions, type methods nominal or methods ordinal at the command line, or see the nominal and ordinal reference pages.

Analysis Using Nominal and Ordinal Arrays

You can use nominal and ordinal arrays in a variety of statistical analyses. For example, you might want to compute descriptive statistics for data grouped by the category levels, conduct statistical tests on differences between category means, or perform regression analysis using categorical predictors.

Statistics and Machine Learning Toolbox™ functions that accept a grouping variable as an input argument accept nominal and ordinal arrays. This includes descriptive functions such as:

You can also use nominal and ordinal arrays as input arguments to analysis functions and methods based on models, such as:

When you use a nominal or ordinal array as a predictor in these functions, the fitting function automatically recognizes the categorical predictor, and constructs appropriate dummy indicator variables for analysis. Alternatively, you can construct your own dummy indicator variables using dummyvar.

Reduce Memory Requirements

The levels of categorical variables are often defined as text, which can be costly to store and manipulate in a cell array of character vectors or char array. Nominal and ordinal arrays separately store category membership and category labels, greatly reducing the amount of memory required to store the variable.

For example, load some sample data:

load('fisheriris')

Convert species to a nominal array:

species = nominal(species);

There is a 95% reduction in memory required to store the variable.