Description Usage Arguments Details Value Examples

Calculates r from d and then translates r to r2 to calculate the non-central confidence interval for r2 using the F distribution.

1 | ```
d.to.r(d, n1, n2, a = 0.05)
``` |

`d` |
effect size statistic |

`n1` |
sample size group one |

`n2` |
sample size group two |

`a` |
significance level |

The correlation coefficient (r) is calculated by dividing Cohen's d by the square root of the total sample size squared - divided by the product of the sample sizes of group one and group two.

r = d / sqrt(d^2 + (n1 + n2)^2 / (n1*n2))

Learn more on our example page.

Provides the effect size (correlation coefficient) with associated confidence intervals, the t-statistic, F-statistic, and other estimates appropriate for d to r translation. Note this CI is not based on the traditional r-to-z transformation but rather non-central F using the ci.R function from MBESS.

`r` |
correlation coefficient |

`rlow` |
lower level confidence interval r |

`rhigh` |
upper level confidence interval r |

`R2` |
coefficient of determination |

`R2low` |
lower level confidence interval of R2 |

`R2high` |
upper level confidence interval of R2 |

`se` |
standard error |

`n` |
sample size |

`dfm` |
degrees of freedom of mean |

`dfe` |
degrees of freedom error |

`t` |
t-statistic |

`F` |
F-statistic |

`p` |
p-value |

`estimate` |
the r statistic and confidence interval in APA style for markdown printing |

`estimateR2` |
the R^2 statistic and confidence interval in APA style for markdown printing |

`statistic` |
the t-statistic in APA style for markdown printing |

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 26 27 28 29 30 31 32 33 34 35 36 37 | ```
#The following example is derived from the "indt_data" dataset, included
#in the MOTE library.
#A forensic psychologist conducted a study to examine whether
#being hypnotized during recall affects how well a witness
#can remember facts about an event. Eight participants
#watched a short film of a mock robbery, after which
#each participant was questioned about what he or she had
#seen. The four participants in the experimental group
#were questioned while they were hypnotized. The four
#participants in the control group recieved the same
#questioning without hypnosis.
t.test(correctq ~ group, data = indt_data)
#You can type in the numbers directly, or refer to the dataset,
#as shown below.
d.ind.t(m1 = 17.75, m2 = 23, sd1 = 3.30,
sd2 = 2.16, n1 = 4, n2 = 4, a = .05)
d.ind.t(17.75, 23, 3.30, 2.16, 4, 4, .05)
d.ind.t(mean(indt_data$correctq[indt_data$group == 1]),
mean(indt_data$correctq[indt_data$group == 2]),
sd(indt_data$correctq[indt_data$group == 1]),
sd(indt_data$correctq[indt_data$group == 2]),
length(indt_data$correctq[indt_data$group == 1]),
length(indt_data$correctq[indt_data$group == 2]),
.05)
#Contrary to the hypothesized result, the group that underwent
#hypnosis were significantly less accurate while reporting
#facts than the control group with a large effect size, t(6) = -2.66,
#p = .038, d_s = 1.88.
d.to.r(d = -1.88, n1 = 4, n2 = 4, a = .05)
``` |

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