PBIB.test: Analysis of the Partially Balanced Incomplete Block Design

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/PBIB.test.R

Description

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

Usage

1
2
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.

Value

ANOVA

analysis of variance

method

estimation method: REML, ML and VC

parameters

treatments, block Size, blocks, replication, alpha signification and test comparison

statistics

efficiency, coefficient of variation

model

object: estimation model

Fstat

criterion AIC and BIC

comparison

data.frame treatments comparison

means

data.frame means of treatments

groups

significant treatment groups

vartau

matrix of variance and covariance

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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
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)

Example output

alpha design (0,1) - Serie  I 

Parameters Alpha design
=======================
treatmeans : 30
Block size : 3
Blocks     : 10
Replication: 2 

Efficiency factor
(E ) 0.6170213 

<<< Book >>>

<<< to see the objects: means, comparison and groups. >>>

agricolae documentation built on Aug. 5, 2017, 5:02 p.m.