Statistics and Machine Learning Toolbox™ provides two additional
data types. Work with ordered and unordered discrete, nonnumeric data
types. Store multiple variables, including those with different data
types, into a single object using the
data type. However, these data types are unique to Statistics and Machine Learning Toolbox.
For greater cross-product compatibility, use the
types, respectively, available in MATLAB®. For more information
see Create Categorical Arrays (MATLAB), Create and Work with Tables (MATLAB), or
and Categorical Arrays.
|Convert matrix to dataset array|
|Convert cell array to dataset array|
|Convert structure array to dataset array|
|Convert table to dataset array|
|Convert dataset array to cell array|
|Convert dataset array to structure|
|Convert dataset array to table|
|Write dataset array to file|
|Find dataset array elements with missing values|
|Arrays for statistical data|
This example shows how to create nominal arrays using
Categorize numeric data into a categorical ordinal array using ordinal.
Change the labels for category levels in categorical
Add and drop levels from a categorical array.
Merge categories in a categorical array using
This example shows how to reorder the category levels in an ordinal array 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.
Perform a regression with categorical covariates using
categorical arrays and
Index and search data by its category, or group.
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.
Categorical 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 indicator variables let you adapt categorical data for use in regression analysis.
Dataset arrays store data with heterogeneous types.