https://github.com/cran/agricolae
Tip revision: c99a0a5a3738ff9e44e07b539fe0b1d59039fe43 authored by Felipe de Mendiburu on 21 December 2012, 00:00:00 UTC
version 1.1-3
version 1.1-3
Tip revision: c99a0a5
LSD.test.R
`LSD.test` <-
function (y, trt, DFerror, MSerror, alpha = 0.05, p.adj = c("none",
"holm", "hochberg", "bonferroni", "BH", "BY", "fdr"), group = TRUE,
main = NULL)
{
p.adj <- match.arg(p.adj)
clase <- c("aov", "lm")
name.y <- paste(deparse(substitute(y)))
name.t <- paste(deparse(substitute(trt)))
if ("aov" %in% class(y) | "lm" %in% class(y)) {
A <- y$model
DFerror <- df.residual(y)
MSerror <- deviance(y)/DFerror
y <- A[, 1]
ipch <- pmatch(trt, names(A))
nipch<- length(ipch)
for(i in 1:nipch){
if (is.na(ipch[i]))
return(cat("Name: ", trt, "\n", names(A)[-1], "\n"))
}
name.t<- names(A)[ipch][1]
trt <- A[, ipch]
if (nipch > 1){
trt <- A[, ipch[1]]
for(i in 2:nipch){
name.t <- paste(name.t,names(A)[ipch][i],sep=":")
trt <- paste(trt,A[,ipch[i]],sep=":")
}}
name.y <- names(A)[1]
}
junto <- subset(data.frame(y, trt), is.na(y) == FALSE)
Mean<-mean(junto[,1])
CV<-sqrt(MSerror)*100/Mean
means <- tapply.stat(junto[, 1], junto[, 2], stat = "mean")
sds <- tapply.stat(junto[, 1], junto[, 2], stat = "sd")
nn <- tapply.stat(junto[, 1], junto[, 2], stat = "length")
mi<-tapply.stat(junto[,1],junto[,2],stat="min") # change
ma<-tapply.stat(junto[,1],junto[,2],stat="max") # change
std.err <- sds[, 2]/sqrt(nn[, 2])
Tprob <- qt(1 - alpha/2, DFerror)
LCL <- means[, 2] - Tprob * std.err
UCL <- means[, 2] + Tprob * std.err
means <- data.frame(means, std.err, r = nn[, 2],
LCL, UCL,Min.=mi[,2],Max.=ma[,2])
names(means)[1:2] <- c(name.t, name.y)
ntr <- nrow(means)
nk <- choose(ntr, 2)
if (p.adj != "none") {
a <- 1e-06
b <- 1
for (i in 1:100) {
x <- (b + a)/2
xr <- rep(x, nk)
d <- p.adjust(xr, p.adj)[1] - alpha
ar <- rep(a, nk)
fa <- p.adjust(ar, p.adj)[1] - alpha
if (d * fa < 0)
b <- x
if (d * fa > 0)
a <- x
}
Tprob <- qt(1 - x/2, DFerror)
}
nr <- unique(nn[, 2])
cat("\nStudy:", main)
cat("\n\nLSD t Test for", name.y, "\n")
if (p.adj != "none")
cat("P value adjustment method:", p.adj, "\n")
cat("\nMean Square Error: ", MSerror, "\n\n")
cat(paste(name.t, ",", sep = ""), " means and individual (",
(1 - alpha) * 100, "%) CI\n\n")
print(data.frame(row.names = means[, 1], means[, -1]))
cat("\nalpha:", alpha, "; Df Error:", DFerror)
cat("\nCritical Value of t:", Tprob, "\n")
if (group) {
if (length(nr) == 1) {
LSD <- Tprob * sqrt(2 * MSerror/nr)
cat("\nLeast Significant Difference", LSD)
}
else {
nr1 <- 1/mean(1/nn[, 2])
LSD <- Tprob * sqrt(2 * MSerror/nr1)
cat("\nLeast Significant Difference", LSD)
cat("\nHarmonic Mean of Cell Sizes ", nr1)
}
cat("\nMeans with the same letter are not significantly different.")
cat("\n\nGroups, Treatments and means\n")
groups <- order.group(means[, 1], means[, 2], means[,
4], MSerror, Tprob, means[, 3])
w <- order(means[, 2], decreasing = TRUE)
groups <- data.frame(groups[,1:3])
comparison=NULL
statistics<-data.frame(Mean=Mean,CV=CV,MSerror=MSerror,LSD=LSD)
}
if (!group) {
LSD=" "
comb <- combn(ntr, 2)
nn <- ncol(comb)
dif <- rep(0, nn)
pvalue <- dif
sdtdif <- dif
sig <- rep(" ", nn)
for (k in 1:nn) {
i <- comb[1, k]
j <- comb[2, k]
# if (means[i, 2] < means[j, 2]) {
# comb[1, k] <- j
# comb[2, k] <- i
# }
dif[k] <-means[i, 2] - means[j, 2]
sdtdif[k] <- sqrt(MSerror * (1/means[i, 4] + 1/means[j,
4]))
pvalue[k] <- 2 * (1 - pt(abs(dif[k])/sdtdif[k], DFerror))
}
if (p.adj != "none")
pvalue <- round(p.adjust(pvalue, p.adj), 6)
LCL1 <- dif - Tprob * sdtdif
UCL1 <- dif + Tprob * sdtdif
for (k in 1:nn) {
if (pvalue[k] <= 0.001)
sig[k] <- "***"
else if (pvalue[k] <= 0.01)
sig[k] <- "**"
else if (pvalue[k] <= 0.05)
sig[k] <- "*"
else if (pvalue[k] <= 0.1)
sig[k] <- "."
}
tr.i <- means[comb[1, ], 1]
tr.j <- means[comb[2, ], 1]
comparison <- data.frame(Difference = dif, pvalue = pvalue,
"sig."=sig, LCL = LCL1, UCL = UCL1)
rownames(comparison) <- paste(tr.i, tr.j, sep = " - ")
cat("\nComparison between treatments means\n\n")
print(comparison)
groups <- NULL
statistics<-data.frame(Mean=Mean,CV=CV,MSerror=MSerror)
}
parameters<-data.frame(Df=DFerror,ntr = ntr, t.value=Tprob)
if(p.adj!="none") names(parameters)[3]<-p.adj
rownames(parameters)<-" "
rownames(statistics)<-" "
rownames(means)<-means[,1]
means<-means[,-1]
output<-list(statistics=statistics,parameters=parameters,
means=means,comparison=comparison,groups=groups)
invisible(output)
}