R: Finding the Variance Analysis of the Balanced Incomplete...

BIB.test {agricolae} | R Documentation

## Finding the Variance Analysis of the Balanced Incomplete Block Design

### Description

Analysis of variance BIB and comparison mean adjusted.

### Usage

    BIB.test(block, trt, y, test = c("lsd","tukey","duncan","waller","snk"),
    alpha = 0.05, group = TRUE,console=FALSE)

### Arguments

`block` | blocks
`trt` | Treatment
`y` | Response
`test` | Comparison treatments
`alpha` | Significant test
`group` | logical
`console` | logical, print output

### Details

Test of comparison treatment. lsd: Least significant difference. tukey: Honestly significant differente. duncan: Duncan's new multiple range test waller: Waller-Duncan test. snk: Student-Newman-Keuls (SNK)

### Value

`parameters` | Design parameters
`statistics` | Statistics of the model
`comparison` | Comparison between treatments
`means` | Adjusted mean and statistics summary
`groups` | Grouping of treatments

### Author(s)

F. de Mendiburu

### References

Design of Experiments. Robert O. Kuehl. 2nd ed., Duxbury, 2000 Linear Estimation and Design of Experiments. D.D. Joshi. WILEY EASTERN LIMITED 1987, New Delhi, India. Introduction to experimental statistics. Ching Chun Li McGraw - Hill Book Company, Inc. New York. 1964

### See Also

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

### Examples

    library(agricolae)
    # Example Design of Experiments. Robert O. Kuehl. 2da. Edicion. 2001
    run<-gl(10,3)
    psi<-c(250,325,475,250,475,550,325,400,550,400,475,550,325,475,550,
    250,400,475,250,325,400,250,400,550,250,325,550,325,400,475)
    monovinyl<-c(16,18,32,19,46,45,26,39,61,21,35,55,19,47,48,20,33,31,13,13,34,21,
     30,52,24,10,50,24,31,37)
    out<-BIB.test(run,psi,monovinyl,test="waller",group=FALSE)
    print(out)
    bar.err(out$means,variation="range",ylim=c(0,60),bar=FALSE,col=0)
    out<-BIB.test(run,psi,monovinyl,test="waller",group=TRUE)
    out<-BIB.test(run,psi,monovinyl,test="tukey",group=TRUE,console=TRUE)
    out<-BIB.test(run,psi,monovinyl,test="tukey",group=FALSE,console=TRUE)
    rm(run,psi,monovinyl,out)
    # Example linear estimation and design of experiments. D.D. Joshi. 1987
    # Professor of Statistics, Institute of Social Sciences Agra, India
    # 6 varieties of wheat crop in a BIB whit 10 blocks of 3 plots each.
    y <-c(69,77,72,63,70,54,65,65,57,59,50,45,68,75,59,38,60,60,62,
     55,54,65,62,65,61,39,54,67,63,56)
    varieties<-gl(6,5)
    block <- c(1,2,3,4,5,1,2,6,7,8,1,3,6,9,10,2,4,7,9,10,3,5,7,8,9,4,5,6,8,10)
    BIB.test(block, varieties, y)
    # Example Introduction to experimental statistics. Ching Chun Li. 1964
    # pag. 395 table. 27.2
    # 7 trt, k=3 and b=7.
    y <-c(10,15,11,4,12,15,5,14,10,14,19,19,8,10,17,6,11,12,5,14,21)
    block<-gl(7,3)
    trt <- c(1,2,4,2,3,5,3,4,6,4,5,7,1,5,6,2,6,7,1,3,7)
    out<-BIB.test(block, trt, y, test="duncan")
    bar.group(out$groups,col="blue",density=4,ylim=c(0,max(y)))
    rm(y,block,trt,out)

[Package agricolae version 1.2-8 Index]