## when using proc mixed to treat subject as random effects or fixed effects, why get same result?

Occasional Contributor
Posts: 7

# when using proc mixed to treat subject as random effects or fixed effects, why get same result?

PROC MIXED DATA=TRY;
CLASS TREATMENT PERIOD SEQUENCE SUBJECT;
MODEL CONC=TREATMENT PERIOD SEQUENCE/SOLUTION;
RANDOM SUBJECT(SEQUENCE);
LSMEANS TREATMENT/PDIFF=control("A","B") CL ALPHA=0.1 ;
RUN;

PROC MIXED DATA=TRY;
CLASS TREATMENT PERIOD SEQUENCE SUBJECT;
MODEL CONC=TREATMENT PERIOD SEQUENCE SUBJECT(SEQUENCE)/SOLUTION;
LSMEANS TREATMENT/PDIFF=control("A","B") CL ALPHA=0.1 ;
RUN;

 The SAS System

If WE treat subject(sequence ) as fixed effect

The Mixed Procedure

 Model Information Data Set WORK.TRY Dependent Variable CONC Covariance Structure Diagonal Estimation Method REML Residual Variance Method Profile Fixed Effects SE Method Model-Based Degrees of Freedom Method Residual

 Class Level Information Class Levels Values TREATMENT 2 A B PERIOD 2 1 2 SEQUENCE 2 1 2 SUBJECT 10 001 002 003 004 005 006 007 008 009 010

 Dimensions Covariance Parameters 1 Columns in X 17 Columns in Z 0 Subjects 1 Max Obs Per Subject 20

 Number of Observations Number of Observations Read 20 Number of Observations Used 20 Number of Observations Not Used 0

 Covariance Parameter Estimates Cov Parm Estimate Residual 0.4089

 Fit Statistics -2 Res Log Likelihood 25.7 AIC (smaller is better) 27.7 AICC (smaller is better) 28.4 BIC (smaller is better) 27.8

 Solution for Fixed Effects Effect TREATMENT SUBJECT PERIOD SEQUENCE Estimate Standard Error DF t Value Pr > |t| Intercept 3.1780 0.4953 8 6.42 0.0002 TREATMENT A 1.0800 0.2860 8 3.78 0.0054 TREATMENT B 0 . . . . PERIOD 1 -0.1160 0.2860 8 -0.41 0.6956 PERIOD 2 0 . . . . SEQUENCE 1 -0.7100 0.6395 8 -1.11 0.2991 SEQUENCE 2 0 . . . . SUBJECT(SEQUENCE) 001 1 -0.1750 0.6395 8 -0.27 0.7913 SUBJECT(SEQUENCE) 002 1 -0.4000 0.6395 8 -0.63 0.5490 SUBJECT(SEQUENCE) 003 1 0.3850 0.6395 8 0.60 0.5638 SUBJECT(SEQUENCE) 004 1 1.5200 0.6395 8 2.38 0.0448 SUBJECT(SEQUENCE) 007 1 0 . . . . SUBJECT(SEQUENCE) 005 2 -0.5500 0.6395 8 -0.86 0.4148 SUBJECT(SEQUENCE) 006 2 0.5000 0.6395 8 0.78 0.4568 SUBJECT(SEQUENCE) 008 2 -1.0100 0.6395 8 -1.58 0.1529 SUBJECT(SEQUENCE) 009 2 -1.6400 0.6395 8 -2.56 0.0334 SUBJECT(SEQUENCE) 010 2 0 . . . .

 Type 3 Tests of Fixed Effects Effect Num DF Den DF F Value Pr > F TREATMENT 1 8 14.26 0.0054 PERIOD 1 8 0.16 0.6956 SEQUENCE 1 8 0.11 0.7457 SUBJECT(SEQUENCE) 8 8 3.12 0.0641

 Least Squares Means Effect TREATMENT Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper TREATMENT A 3.7080 0.2022 8 18.34 <.0001 0.1 3.3320 4.0840 TREATMENT B 2.6280 0.2022 8 13.00 <.0001 0.1 2.2520 3.0040

 Differences of Least Squares Means Effect TREATMENT _TREATMENT Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper TREATMENT B A -1.0800 0.2860 8 -3.78 0.0054 0.1 -1.6118 -0.5482
Occasional Contributor
Posts: 7

## Re: when using proc mixed to treat subject as random effects or fixed effects, why get same result?

continuing from my previous question, same dataset
Occasional Contributor
Posts: 7

## Why when we use proc mixed procedure and treat subject as random or fixed effects,same answer got .

PROC MIXED DATA=TRY;
CLASS TREATMENT PERIOD SEQUENCE SUBJECT;
MODEL CONC=TREATMENT PERIOD SEQUENCE/SOLUTION;
RANDOM SUBJECT(SEQUENCE);
LSMEANS TREATMENT/PDIFF=control("A","B") CL ALPHA=0.1 ;
RUN;

PROC MIXED DATA=TRY;
CLASS TREATMENT PERIOD SEQUENCE SUBJECT;
MODEL CONC=TREATMENT PERIOD SEQUENCE SUBJECT(SEQUENCE)/SOLUTION;
LSMEANS TREATMENT/PDIFF=control("A","B") CL ALPHA=0.1 ;
RUN;

 The SAS System

If WE treat subject(sequence ) as fixed effect

The Mixed Procedure

 Model Information Data Set WORK.TRY Dependent Variable CONC Covariance Structure Diagonal Estimation Method REML Residual Variance Method Profile Fixed Effects SE Method Model-Based Degrees of Freedom Method Residual

 Class Level Information Class Levels Values TREATMENT 2 A B PERIOD 2 1 2 SEQUENCE 2 1 2 SUBJECT 10 001 002 003 004 005 006 007 008 009 010

 Dimensions Covariance Parameters 1 Columns in X 17 Columns in Z 0 Subjects 1 Max Obs Per Subject 20

 Number of Observations Number of Observations Read 20 Number of Observations Used 20 Number of Observations Not Used 0

 Covariance Parameter Estimates Cov Parm Estimate Residual 0.4089

 Fit Statistics -2 Res Log Likelihood 25.7 AIC (smaller is better) 27.7 AICC (smaller is better) 28.4 BIC (smaller is better) 27.8

 Solution for Fixed Effects Effect TREATMENT SUBJECT PERIOD SEQUENCE Estimate Standard Error DF t Value Pr > |t| Intercept 3.1780 0.4953 8 6.42 0.0002 TREATMENT A 1.0800 0.2860 8 3.78 0.0054 TREATMENT B 0 . . . . PERIOD 1 -0.1160 0.2860 8 -0.41 0.6956 PERIOD 2 0 . . . . SEQUENCE 1 -0.7100 0.6395 8 -1.11 0.2991 SEQUENCE 2 0 . . . . SUBJECT(SEQUENCE) 001 1 -0.1750 0.6395 8 -0.27 0.7913 SUBJECT(SEQUENCE) 002 1 -0.4000 0.6395 8 -0.63 0.5490 SUBJECT(SEQUENCE) 003 1 0.3850 0.6395 8 0.60 0.5638 SUBJECT(SEQUENCE) 004 1 1.5200 0.6395 8 2.38 0.0448 SUBJECT(SEQUENCE) 007 1 0 . . . . SUBJECT(SEQUENCE) 005 2 -0.5500 0.6395 8 -0.86 0.4148 SUBJECT(SEQUENCE) 006 2 0.5000 0.6395 8 0.78 0.4568 SUBJECT(SEQUENCE) 008 2 -1.0100 0.6395 8 -1.58 0.1529 SUBJECT(SEQUENCE) 009 2 -1.6400 0.6395 8 -2.56 0.0334 SUBJECT(SEQUENCE) 010 2 0 . . . .

 Type 3 Tests of Fixed Effects Effect Num DF Den DF F Value Pr > F TREATMENT 1 8 14.26 0.0054 PERIOD 1 8 0.16 0.6956 SEQUENCE 1 8 0.11 0.7457 SUBJECT(SEQUENCE) 8 8 3.12 0.0641

 Least Squares Means Effect TREATMENT Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper TREATMENT A 3.7080 0.2022 8 18.34 <.0001 0.1 3.3320 4.0840 TREATMENT B 2.6280 0.2022 8 13.00 <.0001 0.1 2.2520 3.0040

 Differences of Least Squares Means Effect TREATMENT _TREATMENT Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper TREATMENT B A -1.0800 0.2860 8 -3.78 0.0054 0.1 -1.6118 -0.5482