agricolae (version 1.1-8)

strip.plot: Strip-Plot analysis

Description

The variance analysis of a strip-plot design is divided into three parts: the horizontal-factor analysis, the vertical-factor analysis, and the interaction analysis.

Usage

strip.plot(BLOCK, COL, ROW, Y)

Arguments

BLOCK

replications

COL

Factor column

ROW

Factor row

Y

Variable, response

Value

• BLOCK
  vector, numeric
• COL
  vector, numeric
• ROW
  vector, numeric
• Y
  vector, numeric

Details

The strip-plot design is specifically suited for a two-factor experiment in which the desired precision for measuring the interaction effects between the two factors is higher than that for measuring the main efect two factors

References

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

See Also

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

Examples

# Yield
library(agricolae)
data(huasahuasi)
YIELD<-huasahuasi$YIELD
attach(YIELD)
market <- y1da + y2da
non_market <- y3da
yield <- market + non_market
model<-strip.plot(block, clon, trt, yield)
comparison<-LSD.test(yield,clon,model$gl.a,model$Ea)
comparison<-LSD.test(yield,trt,model$gl.b,model$Eb)
detach(YIELD)
# simple effects
A<-model$data
a<-nlevels(A$clon)
b<-nlevels(A$trt)
r<-nlevels(A$block)
Ea<-model$Ea; Eb<-model$Ec; Ec<-model$Ec;
gla<-model$gl.a; glb<-model$gl.b; glc<-model$gl.c;
B <-tapply.stat(A[,4],A[,2:3],mean)
std<-tapply.stat(A[,4],A[,2:3],function(x) sd(x)/sqrt(length(x)))
B<-data.frame(B[,1:2],yield=B[,3],std=std[,3])
cmab<-(b-1)*Ec + Ea
cmba<-(a-1)*Ec + Eb
# order.group
# Tukey
ta <- qtukey(0.95,a,gla)
tb <- qtukey(0.95,b,glb)
tc <- qtukey(0.95,a*b,glc)
tab<- ((b-1)*Ec*tc + Ea*ta)/cmab
tba<- ((a-1)*Ec*tc + Eb*tb)/cmba
# Comparison of clones by treatment
groups<-by(B,B[,2], function(x) order.group(x$clon,x$yield,N=b*r, cmab,tab, 
        std.err=x$std,parameter=0.5,console=FALSE))
groups
# Comparison of treatments by clon
groups<-by(B,B[,1], function(x) order.group(x$trt,x$yield,N=a*r,cmba,tba, 
        std.err=x$std,parameter=0.5,console=FALSE))
groups
# LSD t-student
ta <- qt(0.975,gla)
tb <- qt(0.975,glb)
tc <- qt(0.975,glc)
tab<- ((b-1)*Ec*tc + Ea*ta)/cmab
tba<- ((a-1)*Ec*tc + Eb*tb)/cmba
# Comparison of clones by treatment
groups<-by(B,B[,2], function(x) order.group(x$clon,x$yield,N=b*r,cmab,tab, 
        std.err=x$std,console=FALSE))
groups
# Comparison of treatments by clon
groups<-by(B,B[,1], function(x) order.group(x$trt,x$yield,N=a*r,cmba,tba, 
        std.err=x$std,console=FALSE))
groups