agricolae (version 1.2-8)

ssp.plot: Split-split-Plot analysis

Description

The variance analysis of a split-split plot design is divided into three parts: the main-plot, subplot and sub-subplot analysis.

Usage

ssp.plot(block, pplot, splot, ssplot, Y)

Arguments

block

replications

pplot

Factor main plot

splot

Factor subplot

ssplot

Factor sub-subplot

Y

Variable, response

Value

ANOVA: Splip Split plot analysis

Details

The split-split-plot design is an extension of the split-plot design to accommodate a third factor: one factor in main-plot, other in subplot and the third factor in sub-subplot

References

Statistical procedures for agricultural research. Kwanchai A. Gomez, Arturo A. Gomez. Second Edition. 1984.

See Also

sp.plot, strip.plot, design.split, design.strip

Examples

# NOT RUN {
# Statistical procedures for agricultural research, pag 143
# Grain Yields of Three Rice Varieties Grown under 
#Three Management practices and Five Nitrogen levels; in a
#split-split-plot design with nitrogen as main-plot, 
#management practice as subplot, and variety as sub-subplot 
#factores, with three replications.
library(agricolae)
f <- system.file("external/ssp.csv", package="agricolae")
ssp<-read.csv(f)
model<-with(ssp,ssp.plot(block,nitrogen,management,variety,yield))
gla<-model$gl.a; glb<-model$gl.b; glc<-model$gl.c
Ea<-model$Ea; Eb<-model$Eb; Ec<-model$Ec
par(mfrow=c(1,3),cex=0.6)
out1<-with(ssp,LSD.test(yield,nitrogen,gla,Ea,console=TRUE))
out2<-with(ssp,LSD.test(yield,management,glb,Eb,console=TRUE))
out3<-with(ssp,LSD.test(yield,variety,glc,Ec,console=TRUE))
plot(out1,xlab="Nitrogen",las=1,variation="IQR")
plot(out2,xlab="Management",variation="IQR")
plot(out3,xlab="Variety",variation="IQR")
# with aov
AOV<-aov(yield ~  block + nitrogen*management*variety + Error(block/nitrogen/management),data=ssp)
summary(AOV)
# }