Hello Everyone,
I've been learning to use R in my spare time over the past several months. I've
read about 7-8 books on the subject. Lately I've been testing what I've learned
by trying to replicate the analyses from some of my SAS books. This helps me
make sure I know how to use R properly and also helps me to understand how the
two programs are similar and different.
Below is an attempt to replicate some SAS analyses. This was a success in that
I was able to reproduce the results using R. I have a feeling though that it
may not represent an ideal approach. So I was hoping a few people might be
willing to look at what I've done and to suggest improvements or alternatives.
One thing I'm struggling with is setting the reference level. I inserted a
comment about this in the R code.
I've also pasted in the original SAS code in case some people are also SAS
users and might find this helpful or interesting.
Thanks,
Paul
R Code:
##################################
#### Chapter 6: One-Way ANOVA ####
##################################
#### Example 6.1: One-Way ANOVA using HAM-A Scores in GAD ####
connection <- textConnection("
101 LO 21 104 LO 18
106 LO 19 110 LO .
112 LO 28 116 LO 22
120 LO 30 121 LO 27
124 LO 28 125 LO 19
130 LO 23 136 LO 22
137 LO 20 141 LO 19
143 LO 26 148 LO 35
152 LO . 103 HI 16
105 HI 21 109 HI 31
111 HI 25 113 HI 23
119 HI 25 123 HI 18
127 HI 20 128 HI 18
131 HI 16 135 HI 24
138 HI 22 140 HI 21
142 HI 16 146 HI 33
150 HI 21 151 HI 17
102 PB 22 107 PB 26
108 PB 29 114 PB 19
115 PB . 117 PB 33
118 PB 37 122 PB 25
126 PB 28 129 PB 26
132 PB . 133 PB 31
134 PB 27 139 PB 30
144 PB 25 145 PB 22
147 PB 36 149 PB 32
")
gad <- data.frame(scan(connection,
list(patno=0,dosegrp="",hama=0),na.strings="."))
gad
#### Summary Statistics ####
summaryfn <- function(x) data.frame(
mean=mean(x,na.rm=T),std.dev=sd(x,na.rm=T),n=sum(!is.na(x)))
by(gad$hama,gad$dosegrp,summaryfn)
#### ANOVA ####
model1 <- aov(hama~dosegrp,data=gad)
summary.aov(model1)
#### Multiple Comparisons ####
library(multcomp)
#### Pairwise T-Tests ####
cht <- glht(model1, linfct = mcp(dosegrp = "Tukey"))
summary(cht, test = univariate())
#### Dunnett's Test ####
#Not sure how to set the reference group to placebo
#cht <- glht(model1, linfct = mcp(dosegrp = "Dunnett"))
#summary(cht, test = univariate())
model2 <- aov(hama ~ dosegrp - 1, data = gad)
K <- rbind(c(1, 0, -1), c(0, 1, -1))
rownames(K) <- c("HI vs PL", "LO vs PL")
colnames(K) <- names(coef(model2))
summary(glht(model2, linfct = K))
#### Treatment vs. Placebo ####
#SAS produces an F-test but this is equivalent
K <- rbind(c(0.5, 0.5, -1))
rownames(K) <- c("TR vs PL")
colnames(K) <- names(coef(model2))
summary(glht(model2, linfct = K))
SAS code:
*-----------------------------------------------------------------------------------*;
** Chapter 6: One-Way ANOVA;
/* Example 6.1: HAM-A Scores in GAD */
DATA GAD;
INPUT PATNO DOSEGRP $ HAMA @@;
DATALINES;
101 LO 21 104 LO 18
106 LO 19 110 LO .
112 LO 28 116 LO 22
120 LO 30 121 LO 27
124 LO 28 125 LO 19
130 LO 23 136 LO 22
137 LO 20 141 LO 19
143 LO 26 148 LO 35
152 LO . 103 HI 16
105 HI 21 109 HI 31
111 HI 25 113 HI 23
119 HI 25 123 HI 18
127 HI 20 128 HI 18
131 HI 16 135 HI 24
138 HI 22 140 HI 21
142 HI 16 146 HI 33
150 HI 21 151 HI 17
102 PB 22 107 PB 26
108 PB 29 114 PB 19
115 PB . 117 PB 33
118 PB 37 122 PB 25
126 PB 28 129 PB 26
132 PB . 133 PB 31
134 PB 27 139 PB 30
144 PB 25 145 PB 22
147 PB 36 149 PB 32
;
PROC SORT DATA = GAD; BY DOSEGRP;
PROC MEANS MEAN STD N DATA = GAD;
BY DOSEGRP;
VAR HAMA;
TITLE1 'One-Way ANOVA';
TITLE2 'EXAMPLE 6.1: HAM-A Scores in GAD';
RUN;
PROC GLM DATA = GAD;
CLASS DOSEGRP;
MODEL HAMA = DOSEGRP;
MEANS DOSEGRP/T DUNNETT('PB');
CONTRAST 'ACTIVE vs. PLACEBO' DOSEGRP 0.5 0.5 -1;
RUN;
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