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grpstats

Class: RepeatedMeasuresModel

Compute descriptive statistics of repeated measures data by group

Description

example

statstbl = grpstats(rm,g) returns the count, mean, and variance for the data used to fit the repeated measures model rm, grouped by the factors, g.

example

statstbl = grpstats(rm,g,stats) returns the statistics specified by stats for the data used to fit the repeated measures model rm, grouped by the factors, g.

Input Arguments

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Repeated measures model, returned as a RepeatedMeasuresModel object.

For properties and methods of this object, see RepeatedMeasuresModel.

Name of grouping factor or factors, specified as a character vector, string array, or cell array of character vectors.

Example: 'Drug'

Example: {'Drug','Sex'}

Data Types: char | string | cell

Statistics to compute, specified as one of the following:

  • Character vector or string scalar specifying the name of the statistics to compute. Names can be one of the following.

    NameDescription
    'mean'Mean
    'sem'Standard error of the mean
    'numel'Count or number of elements
    'gname'Group name
    'std'Standard deviation
    'var'Variance
    'min'Minimum
    'max'Maximum
    'range'Maximum minus minimum
    'meanci'95% confidence interval for the mean
    'predci'95% prediction interval for a new observation
  • Function handle — The function you specify must accept a vector of response values for a single group, and compute descriptive statistics for it. A function should typically return a value that has one row. A function must return the same size output each time grpstats calls it, even if the input for some groups is empty.

  • A string array or cell array of character vectors and function handles.

Example: @median

Example: @skewness

Example: 'gname'

Example: {'gname','range','predci'}

Output Arguments

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Statistics values for each group, returned as a table.

Examples

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Load the sample data.

load fisheriris

The column vector, species consists of iris flowers of three different species: setosa, versicolor, and virginica. The double matrix meas consists of four types of measurements on the flowers: the length and width of sepals and petals in centimeters, respectively.

Store the data in a table array.

t = table(species,meas(:,1),meas(:,2),meas(:,3),meas(:,4),...
'VariableNames',{'species','meas1','meas2','meas3','meas4'});
Meas = dataset([1 2 3 4]','VarNames',{'Measurements'});

Fit a repeated measures model, where the measurements are the responses and the species is the predictor variable.

rm = fitrm(t,'meas1-meas4~species','WithinDesign',Meas);

Compute group counts, mean, and standard deviation with respect to species.

grpstats(rm,'species')
ans=3×4 table
       species        GroupCount     mean      std  
    ______________    __________    ______    ______

    {'setosa'    }       200        2.5355    1.8483
    {'versicolor'}       200         3.573    1.7624
    {'virginica' }       200         4.285    1.9154

Now, compute the range of data and 95% confidence intervals for the group means for the factor species. Also display the group name.

grpstats(rm,'species',{'gname','range','predci'})
ans=3×5 table
       species            gname         GroupCount    range           predci       
    ______________    ______________    __________    _____    ____________________

    {'setosa'    }    {'setosa'    }       200         5.7      -1.1185      6.1895
    {'versicolor'}    {'versicolor'}       200           6     0.088976       7.057
    {'virginica' }    {'virginica' }       200         6.5       0.4985      8.0715

Load the sample data.

load repeatedmeas

The table between includes the between-subject variables age, IQ, group, gender, and eight repeated measures y1 through y8 as responses. The table within includes the within-subject variables w1 and w2. This is simulated data.

Fit a repeated measures model, where the repeated measures y1 through y8 are the responses, and age, IQ, group, gender, and the group-gender interaction are the predictor variables. Also specify the within-subject design matrix.

rm = fitrm(between,'y1-y8 ~ Group*Gender + Age + IQ','WithinDesign',within);

Compute group counts, mean, standard deviation, skewness, and kurtosis of data grouped by the factors Group and Gender.

GS = grpstats(rm,{'Group','Gender'},{'mean','std',@skewness,@kurtosis})
GS=6×7 table
    Group    Gender    GroupCount     mean       std      skewness    kurtosis
    _____    ______    __________    _______    ______    ________    ________

      A      Female        40         16.554    21.498     0.35324     3.7807 
      A      Male          40         9.8335    20.602    -0.38722     2.7834 
      B      Female        40         11.261    25.779    -0.49177     4.1484 
      B      Male          40         3.6078    24.646     0.55447     2.7966 
      C      Female        40        -11.335    27.186      1.7499     6.1429 
      C      Male          40        -14.028    31.984      1.7362      5.141 

Tips

  • grpstats computes results separately for each group. The results do not depend on the fitted repeated measures model. It computes the results on all available data, without omitting entire rows that contain NaNs.

See Also

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