agricolae (version 1.2-6)

order.group: Ordering the treatments according to the multiple comparison

Description

This function allows us to compare the treatments averages or the adding of their ranges with the minimal significant difference which can vary from one comparison to another one. This function is used by the HSD, LSD, Kruskal-Wallis, Friedman or Durbin procedures.

Usage

order.group(trt, means, N, MSerror, Tprob, std.err, parameter=1, snk=0, 
DFerror=NULL,alpha=NULL,sdtdif=NULL,vartau=NULL,console)

Arguments

trt

Treatments

means

Means of treatment

N

Replications

MSerror

Mean square error

Tprob

minimum value for the comparison

std.err

standard error

parameter

Constante 1 (Sd), 0.5 (Sx)

snk

Constante = 1 (Student Newman Keuls)

DFerror

Degrees of freedom of the experimental error

alpha

Level of risk for the test

sdtdif

standard deviation of difference in BIB

vartau

matrix var-cov in PBIB

console

logical, print output

Value

trt

Factor

means

Numeric

N

Numeric

MSerror

Numeric

Tprob

value between 0 and 1

std.err

Numeric

parameter

Constant

snk

Constant

DFerror

Numeric

alpha

Numeric

sdtdif

Numeric

vartau

Numeric, matrix

See Also

order.stat

Examples

# NOT RUN {
library(agricolae)
treatments <- c("A","B","C","D","E","F")
means<-c(20,40,35,72,49,58)
std.err<-c(1.2, 2, 1.5, 2.4, 1, 3.1)
minimun<-c(15,38,30,68,43,54)
maximun<-c(23,45,39,76,53,61)
replications <- c(4,4,3,4,3,3)
MSerror <- 55.8
value.t <- 2.1314
groups<-order.group(treatments,means,replications,MSerror,value.t,std.err,console=TRUE)
Means<-data.frame(treatments,means,std.err,r=replications,Min = minimun,
Max = maximun)
rownames(Means)<-Means[,1]
Means<-Means[,-1]
par(mfrow=c(2,2))
bar.group(groups,ylim=c(0,80))
bar.err(Means,variation="SD", bar=FALSE,col="green",ylim=c(0,80),
main="Standard deviation")
bar.err(Means,variation="SE", bar=FALSE,col=colors()[15],ylim=c(0,80),
main="Standard error",)
out<-bar.err(Means,variation="range", bar=FALSE,col=colors()[25],ylim=c(0,80),
space=2,main="Range")
points(out$index,out$means,pch=17,col="orange",cex=2)
# }