R: Analysis of the Partially Balanced Incomplete Block Design

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.

### 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

    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)

[Package agricolae version 1.2-6 Index]