cordiff.dep {genefu} | R Documentation

## Function to estimate whether two dependent correlations differ

### Description

This function tests for statistical differences between two dependent correlations using the formula provided on page 56 of Cohen & Cohen (1983). The function returns a t-value, the DF and the p-value.

### Usage

    cordiff.dep(r.x1y, r.x2y, r.x1x2, n,
      alternative = c("two.sided", "less", "greater"))

### Arguments

`r.x1y` | The correlation between x1 and y where y is typically your outcome variable.
`r.x2y` | The correlation between x2 and y where y is typically your outcome variable.
`r.x1x2` | The correlation between x1 and x2 (the correlation between your two predictors).
`n` | The sample size.
`alternative` | A character string specifying the alternative hypothesis, must be one of "two.sided" default), "greater" or "less". You can specify just the initial letter.

### Details

This function is inspired from the cordif.dep.

### Value

Vector of three values: t statistics, degree of freedom, and p-value.

### References

Cohen, J. & Cohen, P. (1983) "Applied multiple regression/correlation analysis for the behavioral sciences (2nd Ed.)" Hillsdale, nJ: Lawrence Erlbaum Associates.

### See Also

stats::cor, stats::t.test, compareProtoCor

### Examples

    # load VDX dataset
    data(vdxs)
    # retrieve ESR1, AURKA and MKI67 gene expressions
    x1 <- data.vdxs[ ,"208079_s_at"]
    x2 <- data.vdxs[ ,"205225_at"]
    y <- data.vdxs[ ,"212022_s_at"]
    # is MKI67 significantly more correlated to AURKA than ESR1?
    cc.ix <- complete.cases(x1, x2, y)
    cordiff.dep(r.x1y=abs(cor(x=x1[cc.ix], y=y[cc.ix], use="everything",
      method="pearson")), r.x2y=abs(cor(x=x2[cc.ix], y=y[cc.ix],
      use="everything", method="pearson")), r.x1x2=abs(cor(x=x1[cc.ix],
      y=x2[cc.ix], use="everything", method="pearson")), n=sum(cc.ix),
      alternative="greater")

[Package genefu version 2.26.0 Index]