PBIB.test {agricolae}

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

See Also

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)