resampling.cv {agricolae} | R Documentation

## Resampling to find the optimal number of markers

### Description

This process finds the curve of CV for a different number of markers which allows us to determine the number of optimal markers for a given relative variability. A method of the curvature.

### Usage

    resampling.cv(A, size, npoints)

### Arguments

`A` | data frame or matrix of binary data
`size` | number of re-samplings
`npoints` | Number of points to consider the model

### Value

lm(formula = CV ~ I(1/marker))
Table with variation coefficient by number of markers

### Author(s)

Felipe de Mendiburu

### References

Efron, B., Tibshirani, R. (1993) An Introduction to the Boostrap. Chapman and Hall/CRC

### See Also

`cv.similarity`, `similarity`

### Examples

    library(agricolae)
    #example table of molecular markers
    data(markers)
    study<-resampling.cv(markers,size=1,npoints=15)
    #
    # Results of the model
    summary(study$model)
    coef<-coef(study$model)
    py<-predict(study$model)
    Rsq<-summary(study$model)$"r.squared"
    table.cv <- data.frame(study$table.cv,estimate=py)
    print(table.cv)

    # Plot CV
    #startgraph
    limy<-max(table.cv[,2])+10
    plot(table.cv[,c(1,2)],col="red",frame=FALSE,xlab="number of markers",
    ylim=c(10,limy), ylab="CV",cex.main=0.8,main="Estimation of the number of markers")
    ty<-quantile(table.cv[,2],1)
    tx<-median(table.cv[,1])
    tz<-quantile(table.cv[,2],0.95)
    text(tx,ty, cex=0.8,as.expression(substitute(CV == a + frac(b,markers),
    list(a=round(coef[1],2),b=round(coef[2],2)))) )
    text(tx,tz,cex=0.8,as.expression(substitute(R^2==r,list(r=round(Rsq,3)))))

    # Plot CV = a + b/n.markers
    fy<-function(x,a,b) a+b/x
    x<-seq(2,max(table.cv[,1]),length=50)
    y <- coef[1] + coef[2]/x
    lines(x,y,col="blue")
    #grid(col="brown")
    rug(table.cv[,1])
    #endgraph

[Package agricolae version 1.2-9 Index]