Load the sample data.

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.

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

Perform data grouped by the factor species.

The estimated marginal means seem to differ with group. You
can compute the standard error and the 95% confidence intervals for
the marginal means using the `margmean`

method.

Load the sample data.

The table `between`

includes the between-subject
variables age, IQ, group, gender, and eight repeated measures *y*1
through *y*8 as responses. The table `within`

includes
the within-subject variables *w*1 and *w*2.
This is simulated data.

Fit a repeated measures model, where the repeated measures *y*1
through *y*8 are the responses, and age, IQ, group,
gender, and the group-gender interaction are the predictor variables.
Also specify the within-subject design matrix.

Plot the estimated marginal means based on the factors `Group`

and `Gender`

.

Plot the estimated marginal means based on the factor `Group`

and
grouped by `Gender`

.