ssp.plot | R Documentation |
The variance analysis of a split-split plot design is divided into three parts: the main-plot, subplot and sub-subplot analysis.
ssp.plot(block, pplot, splot, ssplot, Y)
block |
replications |
pplot |
Factor main plot |
splot |
Factor subplot |
ssplot |
Factor sub-subplot |
Y |
Variable, response |
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. The model is mixed, the blocks are random and the study factors are fixed applied according to the design.
ANOVA: Splip Split plot analysis
Felipe de Mendiburu
Statistical procedures for agricultural research. Kwanchai A. Gomez, Arturo A. Gomez. Second Edition. 1984.
sp.plot
, strip.plot
, design.split
,
design.strip
# 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
op<-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
ssp$block<-factor(ssp$block)
ssp$nitrogen<-factor(ssp$nitrogen)
ssp$management<-factor(ssp$management)
ssp$variety<-factor(ssp$variety)
AOV<-aov(yield ~ block + nitrogen*management*variety + Error(block/nitrogen/management),data=ssp)
summary(AOV)
par(op)
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