# R/t.test.cluster.s In harrelfe/Hmisc: Harrell Miscellaneous

#### Documented in print.t.test.clustert.test.cluster

```t.test.cluster <- function(y, cluster, group, conf.int=.95)
{
## See:
## Donner A, Birkett N, Buck C, Am J Epi 114:906-914, 1981.
## Donner A, Klar N, J Clin Epi 49:435-439, 1996.
## Hsieh FY, Stat in Med 8:1195-1201, 1988.

group <- as.factor(group)
cluster <- as.factor(cluster)
s <- !(is.na(y)|is.na(cluster)|is.na(group))
y <- y[s];
cluster <- cluster[s];
group <- group[s]
n <- length(y)

if(n<2)
stop("n<2")

gr <- levels(group)
if(length(gr)!=2)
stop("must have exactly two treatment groups")

n <- table(group)
nc <- tapply(cluster, group, function(x)length(unique(x)))
bar <- tapply(y, group, mean)

u <- unclass(group)
y1 <- y[u==1];
y2 <- y[u==2]

c1 <- factor(cluster[u==1]);
c2 <- factor(cluster[u==2]) #factor rids unused lev

b1 <- tapply(y1, c1, mean);
b2 <- tapply(y2, c2, mean)

m1 <- table(c1);
m2 <- table(c2)

if(any(names(m1)!=names(b1)))
stop("logic error 1")

if(any(names(m2)!=names(b2)))
stop("logic error 2")

if(any(m2 < 2))
stop(paste('The following clusters contain only one observation:',
paste(names(m2[m2 < 2]), collapse=' ')))

M1 <- mean(y1);
M2 <- mean(y2)

ssc1 <- sum(m1*((b1-M1)^2));
ssc2 <- sum(m2*((b2-M2)^2))

if(nc[1]!=length(m1))
stop("logic error 3")

if(nc[2]!=length(m2))
stop("logic error 4")

df.msc <- sum(nc)-2
msc <- (ssc1+ssc2)/df.msc
v1 <- tapply(y1,c1,var);
v2 <- tapply(y2,c2,var)

ssw1 <- sum((m1-1)*v1);
ssw2 <- sum((m2-1)*v2)

df.mse <- sum(n)-sum(nc)
mse <- (ssw1+ssw2)/df.mse
na <- (sum(n)-(sum(m1^2)/n[1]+sum(m2^2)/n[2]))/(sum(nc)-1)
rho <- (msc-mse)/(msc+(na-1)*mse)
r <- max(rho, 0)
C1 <- sum(m1*(1+(m1-1)*r))/n[1]
C2 <- sum(m2*(1+(m2-1)*r))/n[2]
v <- mse*(C1/n[1]+C2/n[2])
dif <- diff(bar)
se <- sqrt(v)
zcrit <- qnorm((1+conf.int)/2)
cl <- c(dif-zcrit*se, dif+zcrit*se)
z <- dif/se
P <- 2*pnorm(-abs(z))

stats <-
matrix(NA, nrow=20, ncol=2,
dimnames=list(c("N","Clusters","Mean",
"SS among clusters within groups",
"SS within clusters within groups",
"MS among clusters within groups","d.f.",
"MS within clusters within groups","d.f.",
"Na","Intracluster correlation",
"Variance Correction Factor","Variance of effect",
"Effect (Difference in Means)",
"S.E. of Effect",paste(format(conf.int),"Confidence limits"),
"Z Statistic","2-sided P Value"), gr))

stats[1,] <- n
stats[2,] <- nc
stats[3,] <- bar
stats[4,] <- c(ssc1, ssc2)
stats[5,] <- c(ssw1, ssw2)
stats[6,1] <- msc
stats[7,1] <- df.msc
stats[8,1] <- mse
stats[9,1] <- df.mse
stats[10,1] <- na
stats[11,1] <- rho
stats[12,] <- c(C1, C2)
stats[13,1] <- v
stats[15,1] <- de
stats[16,1] <- dif
stats[17,1] <- se
stats[18,] <- cl
stats[19,1] <- z
stats[20,1] <- P

attr(stats,'class') <- "t.test.cluster"
stats
}

print.t.test.cluster <- function(x, digits, ...)
{
##   if(!missing(digits)).Options\$digits <- digits      6Aug00
if(!missing(digits)) {
oldopt <- options('digits')
options(digits=digits)
on.exit(options(oldopt))
}

cstats <- t(apply(x,1,format))
##   cstats <- format(x)
attr(cstats,'class') <- NULL
cstats[is.na(x)] <- ""
invisible(print(cstats, quote=FALSE))
}
```
harrelfe/Hmisc documentation built on May 19, 2024, 4:13 a.m.