Matvec Users’ Guide

78 CHAPTER 11. GENERALIZED LINEAR MIXED MODEL ANALYSES
The statements for fitting the logistic growth function are
D=Data();
D.input("rat.dat","y x");
Logistic=Model();
// A t* b
Logistic.equation("y=intercept,x=intercept,x=intercept");
Logistic.param(-1); //m=-1
Init=Vector(700,7,.75);
Logistic.init(Init);
Logistic.link("richards",1)
Logistic.variance("residual",identity(3,3)*1000);
Logistic.fitdata(D);
Logistic.glim(20)
Logistic.vce_aireml(40)
K=identity(3,3);
Logistic.contrast(K)
Logistic.save("rat_log.out")
The Logistic.param(-1); statement is used to fix the shape parameter at −1. The model statement is
used to describe the linear predictors for the three growth curve parameters. In addition, the first dependent
variable is used to identify the weight variable and the second dependent variable is used to identify the
age variable. The estimated growth curve parameters are A = 702.871 ± 13.9397, t∗ = 6.4519 ± 0.143226,
and b = 0.688566 ± 0.057372. The fitted curve is illustrated in Figure 11.3. The Richards model is describe
700
600
500
Weight
400
300
200
100
0
0 2 4 6 8 10 12 14
Age
Figure 11.3: Logistic growth curve
similarly be adding a fourth linear predictor to the model equation and by dropping the .param() statement.
The program is given below:
Richards=Model();
// A t* b m