Analysis of the Partially Balanced Incomplete Block Design

Description

Analysis of variance PBIB and comparison mean adjusted. Applied to resoluble designs: Lattices and alpha design.

Usage

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PBIB.test(block,trt,replication,y,k, method=c("REML","ML","VC"), 
test = c("lsd","tukey"), alpha=0.05, console=FALSE, group=TRUE)

Arguments

block

blocks

trt

Treatment

replication

Replication

y

Response

k

Block size

method

Estimation method: REML, ML and VC

test

Comparison treatments

alpha

Significant test

console

logical, print output

group

logical, groups

Details

Method of comparison treatment. lsd: least significant difference. tukey: Honestly significant differente. Estimate: specifies the estimation method for the covariance parameters. The REML is the default method. The REML specification performs residual (restricted) maximum likelihood, and The ML specification performs maximum likelihood, and the VC specifications apply only to variance component models.

Value

block

Vector, consecutive numbers by replication

trt

Vector numeric or character

replication

Vector

y

numeric vector

k

numeric constant

method

Character: REML, ML and VC

test

Character: comparison methods lsd and tukey

alpha

Numeric

group

Logic

Author(s)

F. de Mendiburu

References

1. Iterative Analysis of Generalizad Lattice Designs. E.R. Williams (1977) Austral J. Statistics 19(1) 39-42.

2. Experimental design. Cochran and Cox. Second edition. Wiley Classics Library Edition published 1992

See Also

BIB.test, design.alpha

Examples

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require(agricolae)
# alpha design 
Genotype<-paste("geno",1:30,sep="")
ntr<-length(Genotype)
r<-2
k<-3
s<-10
obs<-ntr*r
b <- s*r
book<-design.alpha(Genotype,k,r,seed=5)
book$book[,3]<- gl(20,3)
dbook<-book$book
# dataset
yield<-c(5,2,7,6,4,9,7,6,7,9,6,2,1,1,3,2,4,6,7,9,8,7,6,4,3,2,2,1,1,2,
     1,1,2,4,5,6,7,8,6,5,4,3,1,1,2,5,4,2,7,6,6,5,6,4,5,7,6,5,5,4)
rm(Genotype)
# not run
# analysis
# require(nlme) # method = REML or LM in PBIB.test and require(MASS) method=VC
model <- with(dbook,PBIB.test(block, Genotype, replication, yield, k=3, method="VC"))
# model$ANOVA
# bar.group(model$groups,ylim=c(0,9), density=20, las=2)

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