# sasLM-package: 'SAS' Linear Model In sasLM: 'SAS' Linear Model

 sasLM-package R Documentation

## 'SAS' Linear Model

### Description

This is a core implementation of 'SAS' procedures for linear models - GLM, REG, and ANOVA. Some packages provide type II and type III SS. However, the results of nested and complex designs are often different from those of 'SAS'. A different result does not necessarily mean incorrectness. However, many want the same result with 'SAS'. This package aims to achieve that. Reference: Littell RC, Stroup WW, Freund RJ (2002, ISBN:0-471-22174-0).

### Details

This will serve those who want SAS PROC GLM, REG, and ANOVA in R.

### Author(s)

Kyun-Seop Bae k@acr.kr

### Examples

``````## SAS PROC GLM Script for Typical Bioequivalence Data
# PROC GLM DATA=BEdata;
#   CLASS SEQ SUBJ PRD TRT;
#   MODEL LNCMAX = SEQ SUBJ(SEQ) PRD TRT;
#   RANDOM SUBJ(SEQ)/TEST;
#   LSMEANS TRT / DIFF=CONTROL("R") CL ALPHA=0.1;
#   ODS OUTPUT LSMeanDiffCL=LSMD;

# DATA LSMD;  SET LSMD;
#   PE = EXP(DIFFERENCE);
#   LL = EXP(LowerCL);
#   UL = EXP(UpperCL);
# PROC PRINT DATA=LSMD; RUN;
##

## SAS PROC GLM equivalent
BEdata = af(BEdata, c("SEQ", "SUBJ", "PRD", "TRT")) # Columns as factor
formula1 = log(CMAX) ~ SEQ/SUBJ + PRD + TRT # Model
GLM(formula1, BEdata) # ANOVA tables of Type I, II, III SS
RanTest(formula1, BEdata, Random="SUBJ") # Hypothesis test with SUBJ as random
ci0 = CIest(formula1, BEdata, "TRT", c(-1, 1), 0.90) # 90\$ CI
exp(ci0[, c("Estimate", "Lower CL", "Upper CL")]) # 90% CI of GMR

## 'nlme' or SAS PROC MIXED is preferred for an unbalanced case
## SAS PROC MIXED equivalent
# require(nlme)
# Result = lme(log(CMAX) ~ SEQ + PRD + TRT, random=~1|SUBJ, data=BEdata)
# summary(Result)
# VarCorr(Result)
# ci = intervals(Result, 0.90) ; ci
# exp(ci\$fixed["TRTT",])
##
``````

sasLM documentation built on Nov. 19, 2023, 5:12 p.m.