PBIB.test {agricolae} 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

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

BIB.test, DAU.test, duncan.test, durbin.test, friedman, HSD.test, kruskal, LSD.test, Median.test, REGW.test, scheffe.test, SNK.test, waerden.test, waller.test, plot.group

### 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)


[Package agricolae version 1.3-7 Index]