R: Split-split-Plot analysis

ssp.plot {agricolae} | R Documentation

## Split-split-Plot analysis

### Description

The variance analysis of a split-split plot design is divided into three parts: the main-plot, subplot and sub-subplot analysis.

### Usage

    ssp.plot(block, pplot, splot, ssplot, Y)

### Arguments

`block` | replications
`pplot` | Factor main plot
`splot` | Factor subplot
`ssplot` | Factor sub-subplot
`Y` | Variable, response

### Details

The split-split-plot design is an extension of the split-plot design to accommodate a third factor: one factor in main-plot, other in subplot and the third factor in sub-subplot

### Value

`block` | vector, numeric or character
`pplot` | vector, numeric or character
`splot` | vector, numeric or character
`ssplot` | vector, nnumeric or character
`Y` | vector, numeric

### Author(s)

Felipe de Mendiburu

### References

Statistical procedures for agricultural research. Kwanchai A. Gomez, Arturo A. Gomez. Second Edition. 1984.

### See Also

`sp.plot`, `strip.plot`, `design.split`, `design.strip`

### Examples

    # Statistical procedures for agricultural research, pag 143
    # Grain Yields of Three Rice Varieties Grown under
    #Three Management practices and Five Nitrogen levels; in a
    #split-split-plot design with nitrogen as main-plot,
    #management practice as subplot, and variety as sub-subplot
    #factores, with three replications.
    library(agricolae)
    yield<-c(3.320,3.766,4.660,3.188,3.625,5.232,5.468,5.759,6.215,
    4.246,5.255,6.829,3.132,5.389,5.217,3.864,4.311,5.915,4.752,
    4.809,5.170,5.788,6.130,7.106,4.842,5.742,5.869,4.375,4.315,
    5.389,4.507,4.875,5.400,4.756,5.295,6.046,4.422,5.308,6.318,
    4.863,5.345,6.011,4.678,5.896,7.309,6.101,5.096,6.573,5.595,
    6.357,7.016,5.442,6.398,6.953,6.209,6.992,7.565,6.860,6.857,
    7.254,5.122,4.873,5.495,6.780,5.925,7.442,5.988,6.533,6.914,
    6.768,7.856,7.626,6.894,6.974,7.812,4.815,4.166,4.225,5.390,
    5.163,4.478,6.509,6.569,7.991,5.779,6.164,7.362,6.573,7.422,
    8.950,5.355,7.442,7.018,6.706,8.592,8.480,8.452,8.662,9.112,
    8.042,9.080,9.660,9.314,9.224,10.360,5.536,6.462,8.020,6.546,
    7.646,9.942,6.698,8.526,9.140,7.414,9.016,8.966,8.508,9.680,
    9.896,5.244,5.584,7.642,7.092,7.212,8.714,8.650,8.514,9.320,
    6.902,7.778,9.128,8.032,9.294,9.712)
    block<-rep(gl(3,15),3)
    nitrogen<-rep(c(rep(0,3),rep(50,3),rep(80,3),rep(110,3),rep(140,3)),9)
    management<-rep(c("m1","m2","m3"),45)
    variety<-gl(3,45)
    model<-ssp.plot(block,nitrogen,management,variety,yield)
    gla<-model$gl.a; glb<-model$gl.b; glc<-model$gl.c
    Ea<-model$Ea; Eb<-model$Eb; Ec<-model$Ec
    LSD.test(yield,nitrogen,gla,Ea,console=TRUE)
    LSD.test(yield,management,glb,Eb,console=TRUE)
    LSD.test(yield,variety,glc,Ec,console=TRUE)
    # with aov
    A<-data.frame(block, nitrogen,management,variety,yield)
    A[,2]<-as.factor(A[,2])
    AOV<-aov(yield ~  block + nitrogen*management*variety + Error(block/nitrogen/management),data=A)
    summary(AOV)

[Package agricolae version 1.2-6 Index]