\name{sasLM-package}
\alias{sasLM-package}
\alias{sasLM}
\docType{package}
\title{'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'. Different results does not necessarily mean incorrectness. However, many wants the same results to '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. 
}
\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
EMS(formula1, BEdata) # EMS table
T3test(formula1, BEdata, Error="SEQ:SUBJ") # Hypothesis test
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",])
##
}

\author{Kyun-Seop Bae k@acr.kr}
