Introduction Example for PROC HPMIXED

This script generates a simulated dataset (Sim table) containing information on species, farms, and animals. It then uses the HPMIXED (High-Performance Mixed Models) procedure to fit a linear mixed model. The model evaluates yield (Yield) based on fixed effects (Species, Species*Farm Interaction) and a random effect (Animal). The code also includes hypothesis tests and contrast definitions.

Data Analysis

Type : CREATION_INTERNE

The data is entirely generated by the DATA 'Sim' step using loops and random functions (rannor, ranuni) to create 40,000 observations.

1 Code Block

DATA STEP Data

Explanation :
Creation of the 'Sim' working table. Use of arrays to store animal characteristics and generation of 40,000 simulated observations.

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1	DATA Sim;
2	keep Species Farm Animal Yield;
3	array AnimalEffect{3000};
4	array AnimalFarm{3000};
5	array AnimalSpecies{3000};
6	DO i = 1 to dim(AnimalEffect);
7	AnimalEffect{i} = sqrt(4.0)*rannor(12345);
8	AnimalFarm{i} = 1 + int(100*ranuni(12345));
9	AnimalSpecies{i} = 1 + int(5*ranuni(12345));
10	END;
11	DO i = 1 to 40000;
12	Animal = 1 + int(3000*ranuni(12345));
13	Species = AnimalSpecies{Animal};
14	Farm = AnimalFarm{Animal};
15	Yield = 1 + Species + Farm/10 + AnimalEffect{Animal}
16	+ sqrt(8.0)*rannor(12345);
17	OUTPUT;
18	END;
19	RUN;

2 Code Block

PROC HPMIXED

Explanation :
Call to the HPMIXED procedure. Definition of classification variables, statistical model (MODEL), random effects (RANDOM), and specification of additional tests and contrasts.

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1	PROC HPMIXED DATA=Sim;
2	class Species Farm Animal;
3	model Yield = Species Species*Farm;
4	random Animal;
5	test Species*Farm;
6	contrast 'Species1 = Species2 = Species3'
7	Species 1 0 -1,
8	Species 0 1 -1;
9	RUN;

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