Description Usage Arguments Details Value Author(s) References See Also Examples

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.

1 2 | ```
cordiff.dep(r.x1y, r.x2y, r.x1x2, n,
alternative = c("two.sided", "less", "greater"))
``` |

`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. |

This function is inspired from the `cordif.dep`

.

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

Benjamin Haibe-Kains

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

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
## 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")
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.