path.analysis {agricolae} | R Documentation

## Path Analysis

### Description

If the cause and effect relationship is well defined, it is possible to represent the whole system of variables in a diagram form known as path-analysis. The function calculates the direct and indirect effects and uses the variables correlation or covariance.

### Usage

    path.analysis(corr.x, corr.y)

### Arguments

`corr.x` | Matrix of correlations of the independent variables
`corr.y` | vector of dependent correlations with each one of the independent ones

### Details

It is necessary first to calculate the correlations.

### Value

Direct and indirect effects and residual Effect^2.

### Author(s)

Felipe de Mendiburu

### References

Biometrical Methods in Quantitative Genetic Analysis, Singh, Chaudhary. 1979

### See Also

`correlation`

### Examples

    # Path analysis. Multivarial Analysis. Anderson. Prentice Hall, pag 616
    library(agricolae)
    # Example 1
    corr.x<- matrix(c(1,0.5,0.5,1),c(2,2))
    corr.y<- rbind(0.6,0.7)
    names<-c("X1","X2")
    dimnames(corr.x)<-list(names,names)
    dimnames(corr.y)<-list(names,"Y")
    path.analysis(corr.x,corr.y)
    # Example 2
    # data of the progress of the disease related bacterial wilt to the ground
    # for the component CE Ca K2 Cu
    data(wilt)
    data(soil)
    x<-soil[,c(3,12,14,20)]
    y<-wilt[,14]
    cor.y<-correlation(y,x)$correlation
    cor.x<-correlation(x)$correlation
    path.analysis(cor.x,cor.y)

[Package agricolae version 1.2-9 Index]