Description Usage Arguments Details Value References See Also Examples

Converts between different measures of effect size (i.e., Cohen's d, log odds ratio, Pearson correlation r, and Fisher's z).

1 | ```
transform_es(y, SE, from, to)
``` |

`y` |
estimate of the effect size (can be vectorized). |

`SE` |
optional: standard error of the effect-size estimate. Must have the
same length as |

`from` |
type of effect-size measure provided by the argument |

`to` |
which type of effect size should be returned (see |

The following chain of transformations is adopted from Borenstein et al. (2009):
`logOR <--> d <--> r <--> z`

.
The conversion from `"d"`

to `"r"`

assumes equal sample sizes per condition (n1=n2).

Note that in in a Bayesian meta-analysis, the prior distributions need to be adapted to the type of effect size. The function `meta_default`

provides modified default prior distributions for different effect size measures which are approximately transformation-invariant (but results may still differ depending on which type of effect size is used for analysis).

If `SE`

is missing, a vector of transformed effect sizes. Otherwise,
a matrix with two columns including effect sizes and standard errors.

Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Converting among effect sizes. In Introduction to Meta-Analysis (pp. 45–49). John Wiley & Sons, Ltd. doi: 10.1002/9780470743386.ch7

1 2 3 4 5 | ```
# transform a single value of Cohen's
transform_es(y = 0.50, SE = 0.20, from = "d", to = "logOR")
# towels data set:
transform_es(y = towels$logOR, SE = towels$SE, from = "logOR", to = "d")
``` |

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