Data Types
Statistics and Machine Learning Toolbox™ provides two additional data types. Work with ordered and
unordered discrete, nonnumeric data using the nominal
and
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 categorical
or table
data types,
respectively, available in MATLAB®. For more information see Create Categorical Arrays,
Create Tables and Assign Data to Them, or watch Tables and Categorical Arrays.
Functions
Classes
dataset | (Not Recommended) Arrays for statistical data |
Topics
Categorical Data
- Nominal and Ordinal Arrays
Nominal and ordinal arrays store data that have a finite set of discrete levels, which might or might not have a natural order. - Advantages of Using Nominal and Ordinal Arrays
Easily manipulate category levels, carry out statistical analysis, and reduce memory requirements. - Grouping Variables
Grouping variables are utility variables used to group or categorize observations. - Dummy Variables
Dummy variables let you adapt categorical data for use in classification and regression analysis. - Other MATLAB Functions Supporting Nominal and Ordinal Arrays
Learn about MATLAB functions that support nominal and ordinal arrays. - Create Nominal and Ordinal Arrays
Create nominal and ordinal arrays usingnominal
andordinal
, respectively. - Categorize Numeric Data
Categorize numeric data into a categorical ordinal array usingordinal
. - Change Category Labels
Change the labels for category levels in nominal or ordinal arrays usingsetlabels
. - Add and Drop Category Levels
Add and drop levels from a nominal or ordinal array. - Merge Category Levels
Merge categories in a nominal or ordinal array usingmergelevels
. - Reorder Category Levels
Reorder the category levels in nominal or ordinal arrays usingreorderlevels
. - Sort Ordinal Arrays
Determine sorting order for ordinal arrays. - Plot Data Grouped by Category
Plot data grouped by the levels of a categorical variable. - Summary Statistics Grouped by Category
Compute summary statistics grouped by levels of a categorical variable. - Test Differences Between Category Means
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 Using Nominal and Ordinal Arrays
Index and search data by its category, or group. - Linear Regression with Categorical Covariates
Perform a regression with categorical covariates using categorical arrays andfitlm
.
Dataset Arrays
- Dataset Arrays
Dataset arrays store data with heterogeneous types. - Create a Dataset Array from Workspace Variables
Create a dataset array from a numeric array or heterogeneous variables existing in the MATLAB workspace. - Create a Dataset Array from a File
Create a dataset array from the contents of a tab-delimited or a comma-separated text, or an Excel file. - Add and Delete Observations
Add and delete observations in a dataset array. - Add and Delete Variables
Add and delete variables in a dataset array. - Access Data in Dataset Array Variables
Work with dataset array variables and their data. - Select Subsets of Observations
Select an observation or subset of observations from a dataset array. - Sort Observations in Dataset Arrays
Sort observations (rows) in a dataset array using the command line. - Merge Dataset Arrays
Merge dataset arrays usingjoin
. - Stack or Unstack Dataset Arrays
Reformat dataset arrays usingstack
andunstack
. - Clean Messy and Missing Data
Find, clean, and delete observations with missing data in a dataset array. - Calculations on Dataset Arrays
Perform calculations on dataset arrays, including averaging and summarizing with a grouping variable. - Export Dataset Arrays
Export a dataset array from the MATLAB workspace to a text or spreadsheet file. - Dataset Arrays in the Variables Editor
The MATLAB Variables editor provides a convenient interface for viewing, modifying, and plotting dataset arrays. - Index and Search Dataset Arrays
Learn the many ways to index into dataset arrays. - Regression Using Dataset Arrays
This example shows how to perform linear and stepwise regression analyses using dataset arrays.