Description Usage Arguments Details Value See Also Examples

Performs partial correlations between x and y, controlled for z.

1 | ```
parCor(x,y,z)
``` |

`x` |
First variable |

`y` |
Second variable |

`z` |
Control variable. This variable is coerced into a factor; in the TripleR context z usually is the group id. |

Performs partial correlations between x and y, controlled for z. The control variable is coerced into a factor; in the TripleR context z usually is the group id. Do *not* use this function with a continuous control variable - results will be wrong! Degrees of freedom for the *t* test are reduced by *g* - 1 (*g* is the number of groups).

`par.cor` |
partial correlation |

`df` |
degrees of freedom for the t test |

`t.value` |
t value |

`p` |
p value |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
data(multiGroup)
data(multiNarc)
# the function 'head' shows the first few lines of a data structure:
head(multiNarc)
# calculate SRA effects for extraversion ratings
RR.style("p")
RR1 <- RR(ex ~ perceiver.id * target.id | group.id, multiGroup, na.rm=TRUE)
# merge variables to one data set
dat <- merge(RR1$effects, multiNarc, by="id")
# We now have a combined data set with SRA effects and external self ratings:
head(dat)
# function parCor(x, y, z) computes partial correlation between x and y,
# controlled for group membership z
d1 <- parCor(dat$ex.t, dat$narc, dat$group.id)
d1
# disattenuate for target effect reliability
parCor2 <- d1$par.cor * (1/sqrt(attr(RR1$effects$ex.t, "reliability")))
parCor2
``` |

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