Dear Philip,
check it out:
Plot Data by Group
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);
Plot data grouped by the factor species.
plot(rm,'group','species')
Change the line style for each group.
plot(rm,'group','species','LineStyle',{'-','--',':'})
Plot Data Grouped by Two Factors 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);
Plot data with Group coded by color and Gender coded by line type.
plot(R,'group',{'Group' 'Gender'},'Color','rrbbgg',... 'LineStyle',{'-' ':' '-' ':' '-' ':'},'Marker','.')
Morteza Hajitabar Firuzjaei
