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.

Randomly generate new response values.

`random`

uses the predictor values in the original
sample data you use to fit the repeated measures model `rm`

in
table `t`

.

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.

Define a table with new values for the predictor variables.

tnew =
Age IQ Group Gender
___ __ _____ ______
16 93 'B' 'Male'

Randomly generate new response values using the values
in the new table `tnew`

.

ysim =
159.0920 114.8927 -6.5618 46.9944 38.6707 -5.6725 70.8690 11.7813