Here is an improvement to what I sent earlier today.
g <- function(y) {
s <- apply(y, 2,
function(z) {
z <- z[!is.na(z)]
n <- length(z)
if(n==0) c(NA,NA,NA,0) else
if(n==1) c(z, NA,NA,1) else {
m <- mean(z)
s <- sd(z)
c(Mean=m, CV=s/m, SE=s/sqrt(n), N=n)
}
})
w <- as.vector(s)
names(w) <- as.vector(outer(rownames(s), colnames(s), paste, sep=''))
w
}
library(Hmisc)
dat2 <- with(dat1,
summarize(cbind(BdA, sla),
llist(wshed, site, species),
g,
subset=species %in% c('b','c','p'),
stat.name=NULL)
)options(digits=3) dat2 # is a data frame
wshed site species MeanBdA CVBdA SEBdA NBdA Meansla CVsla SEsla Nsla 1 1 A b 100.5 0.133 4.23 10 195 0.1813 11.20 10 2 1 A c 99.7 0.101 3.17 10 206 0.1024 6.68 10 3 1 A p 101.4 0.239 7.65 10 188 0.1580 9.39 10 4 1 B b 109.9 0.118 4.09 10 203 0.1433 9.21 10 5 1 B c 98.4 0.193 6.01 10 221 0.1250 8.72 10 6 1 B p 102.9 0.216 7.03 10 203 0.1446 9.29 10 7 2 A b 95.8 0.241 7.31 10 195 0.2011 12.40 10 8 2 A c 98.7 0.194 6.04 10 207 0.1274 8.33 10 9 2 A p 102.2 0.217 7.01 10 191 0.1709 10.31 10 10 2 B b 97.8 0.235 7.27 10 191 0.2079 12.58 10 11 2 B c 100.9 0.164 5.24 10 194 0.0987 6.07 10 12 2 B p 103.0 0.144 4.69 10 209 0.0769 5.35 9
# Another approach, but does casewise deletion of NAs and computes all
# possible marginal summaries
dat2 <- summary(cbind(BdA, sla) ~ wshed + site + species,
data=dat1, fun=g, method='cross')
print.data.frame(dat2) # after Sept 2004 just say dat2
# dat2 is similar to but not really a data frame, with a matrix of statistics S
# Remove method='cross' to just get all one-way summaries
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
