PBIB.test: Analysis of the Partially Balanced Incomplete Block Design
In agricolae: Statistical Procedures for Agricultural Research

PBIB.test

R Documentation

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

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 difference.
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.
The PBIB.test() function can be called inside a function (improvement by Nelson Nazzicari, Ph.D. Bioinformatician)

Value

`ANOVA`	Analysis of variance
`method`	Estimation method: REML, ML and VC
`parameters`	Design parameters
`statistics`	Statistics of the model
`model`	Object: estimation model
`Fstat`	Criterion AIC and BIC
`comparison`	Comparison between treatments
`means`	Statistical summary of the study variable
`groups`	Formation of treatment groups
`vartau`	Variance-Covariance Matrix

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

Examples

require(agricolae)
# alpha design 
Genotype<-c(paste("gen0",1:9,sep=""),paste("gen",10:30,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
# plot(model,las=2)

agricolae documentation built on Oct. 23, 2023, 1:06 a.m.