管理数据

数据导入和导出以及分组变量

使用多种不同的文件格式将数据传入和传出 MATLAB^®。有效的格式包括表格数据、制表符分隔的文件、Microsoft^® Excel^® 电子表格和 SAS^® XPORT 文件。有关支持的文件格式及其关联的导入和导出函数的表，请参阅支持的导入和导出的文件格式。您也可以使用导入工具以交互方式导入数据。Statistics and Machine Learning Toolbox™ 支持 MATLAB 中可用的许多（但不是全部）数据类型。有关详细信息，请参阅Supported Data Types。

nominal、ordinal 和 dataset 数据类型是 Statistics and Machine Learning Toolbox 所独有的，不再推荐使用。要获得更好的跨产品兼容性，请使用 MATLAB 中提供的 categorical 或 table 数据类型。有关详细信息，请参阅创建分类数组或创建表并为其分配数据，或观看表和分类数组。

函数

全部展开

数据导入和导出

`caseread`	从文件中读取个案名称
`casewrite`	Write case names to file
`tblread`	Read tabular data from file
`tblwrite`	Write tabular data to file
`tdfread`	Read tab-delimited file
`xptread`	Create table from data stored in SAS XPORT format file

分类数据

`dummyvar`	Create dummy variables
`onehotencode`	Encode data labels into one-hot vectors (自 R2021b 起)
`onehotdecode`	Decode probability vectors into class labels (自 R2021b 起)
`gplotmatrix`	Matrix of scatter plots by group
`grp2idx`	Create index vector from grouping variable
`gscatter`	分组散点图
`nominal`	(Not Recommended) Arrays for nominal data
`ordinal`	(Not Recommended) Arrays for ordinal data

数据集数组

`mat2dataset`	(Not Recommended) Convert matrix to dataset array
`cell2dataset`	(Not Recommended) Convert cell array to dataset array
`struct2dataset`	(Not Recommended) Convert structure array to dataset array
`table2dataset`	(Not Recommended) Convert table to dataset array
`dataset2cell`	(Not Recommended) Convert dataset array to cell array
`dataset2struct`	(Not Recommended) Convert dataset array to structure
`dataset2table`	Convert dataset array to table
`export`	(Not Recommended) Write dataset array to file
`ismissing`	(Not Recommended) Find dataset array elements with missing values
`join`	(Not Recommended) Merge dataset array observations

类

dataset (Not Recommended) Arrays for statistical data

主题

Statistics and Machine Learning Toolbox 示例数据集
使用各种数据集来尝试 Statistics and Machine Learning Toolbox 中可用的软件功能。
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.
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.
Linear Regression with Categorical Covariates
Perform a regression with categorical covariates using categorical arrays and fitlm.

管理数据

函数

数据导入和导出

分类数据

数据集数组

类

主题

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