Statistics and Machine Learning Toolbox™ provides two additional data types. Work with ordered and
unordered discrete, nonnumeric data using the
ordinal data types. Store multiple variables,
including those with different data types, into a single object using the
dataset array data type. However, these data types
are unique to Statistics and Machine Learning Toolbox. For greater cross-product compatibility, use the
table data types,
respectively, available in MATLAB®. For more information see Create Categorical Arrays (MATLAB),
Create and Work with Tables (MATLAB), or watch Tables and Categorical Arrays.
|(Not Recommended) Convert matrix to dataset array|
|(Not Recommended) Convert cell array to dataset array|
|(Not Recommended) Convert structure array to dataset array|
|(Not Recommended) Convert table to dataset array|
|(Not Recommended) Convert dataset array to cell array|
|(Not Recommended) Convert dataset array to structure|
|Convert dataset array to table|
|(Not Recommended) Write dataset array to file|
|(Not Recommended) Find dataset array elements with missing values|
|(Not Recommended) Merge observations|
|(Not Recommended) Arrays for statistical data|
Nominal and ordinal arrays store data that have a finite set of discrete levels, which might or might not have a natural order.
Easily manipulate category levels, carry out statistical analysis, and reduce memory requirements.
Grouping variables are utility variables used to group or categorize observations.
Dummy variables let you adapt categorical data for use in classification and regression analysis.
Learn about MATLAB functions that support nominal and ordinal arrays.
Create nominal and ordinal arrays using
Categorize numeric data into a categorical ordinal array using
Change the labels for category levels in nominal or ordinal arrays using
Add and drop levels from a nominal or ordinal array.
Merge categories in a nominal or ordinal array using
Reorder the category levels in nominal or ordinal arrays using
Determine sorting order for ordinal arrays.
Plot data grouped by the levels of a categorical variable.
Compute summary statistics grouped by levels of a categorical variable.
Test for significant differences between category (group) means using a t-test, two-way ANOVA (analysis of variance), and ANOCOVA (analysis of covariance) analysis.
Index and search data by its category, or group.
Perform a regression with categorical covariates using categorical arrays and
Dataset arrays store data with heterogeneous types.
Create a dataset array from a numeric array or heterogeneous variables existing in the MATLAB workspace.
Create a dataset array from the contents of a tab-delimited or a comma-separated text, or an Excel file.
Add and delete observations in a dataset array.
Add and delete variables in a dataset array.
Work with dataset array variables and their data.
Select an observation or subset of observations from a dataset array.
Sort observations (rows) in a dataset array using the command line.
Merge dataset arrays using
Reformat dataset arrays using
Find, clean, and delete observations with missing data in a dataset array.
Perform calculations on dataset arrays, including averaging and summarizing with a grouping variable.
Export a dataset array from the MATLAB workspace to a text or spreadsheet file.
The MATLAB Variables editor provides a convenient interface for viewing, modifying, and plotting dataset arrays.
Learn the many ways to index into dataset arrays.
This example shows how to perform linear and stepwise regression analyses using dataset arrays.