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.

Predict responses for the three species.

Y =
5.0060 3.4280 1.4620 0.2460
5.9360 2.7700 4.2600 1.3260
6.5880 2.9740 5.5520 2.0260

Navigate to the folder containing sample data.

Load the sample data.

The matrix `Y`

contains response data for 16
individuals. The response is the blood level of a drug measured at
five time points (time = 0, 2, 4, 6, and 8). Each row of `Y`

corresponds
to an individual, and each column corresponds to a time point. The
first eight subjects are female, and the second eight subjects are
male. This is simulated data.

Define a variable that stores gender information.

Store the data in a proper table array format to perform
repeated measures analysis.

Define the within-subjects variable.

Fit a repeated measures model, where the blood levels
are the responses and gender is the predictor variable.

Predict the responses at intermediate times.

Plot the predictions along with the estimated marginal
means.

Navigate to the folder containing sample data.

Load the sample data.

The matrix `Y`

contains response data for 16
individuals. The response is the blood level of a drug measured at
five time points (time = 0, 2, 4, 6, and 8). Each row of `Y`

corresponds
to an individual, and each column corresponds to a time point. The
first eight subjects are female, and the second eight subjects are
male. This is simulated data.

Define a variable that stores gender information.

Store the data in a proper table array format to perform
repeated measures analysis.

Define the within-subjects variable.

Fit a repeated measures model, where the blood levels
are the responses and gender is the predictor variable.

Predict the responses at intermediate times.

Plot the predictions and the confidence intervals for
predictions along with the estimated marginal means.