Description Usage Arguments Details Value Note Author(s)

View source: R/fRedsRutils.R View source: R/C-3PR_ASCII.R

Converts most common test statistics into most common (signed) effect sizes.

Converts most common test statistics into most common (signed) effect sizes.

1 2 3 4 5 6 7 8 9 10 11 | ```
any2any(testInfo, df1 = NULL, df2 = NULL, N = NULL, n1 = NULL,
n2 = NULL, esType = NA, var.lor = NA, CIcalc = TRUE, CL = 0.95,
rID = 0, q = 1, alternative = "two", keepDirection = TRUE,
keepSign = TRUE, keepSignNames = c("r", "l.r", "u.r", "fisher.z", "l.z",
"u.z"))
any2any(testInfo, df1 = NULL, df2 = NULL, N = NULL, n1 = NULL,
n2 = NULL, esType = NA, var.lor = NA, CIcalc = TRUE, CL = 0.95,
rID = 0, q = 1, alternative = "two", keepDirection = TRUE,
keepSign = TRUE, keepSignNames = c("r", "l.r", "u.r", "fisher.z", "l.z",
"u.z"))
``` |

`df1` |
Degrees of freedom |

`df2` |
NULL or degrees of freedom of the denominator for the f-distribution. |

`N` |
Number of data points used in calculation of test-statistic. |

`n1` |
Number of data points in sample 1. |

`n2` |
Number of data points in sample 2. |

`esType` |
Type of test statistic. One of: "t", "lm.t", "f", "lm.f", "r", "X2", "Z", "lm.Z" |

`CIcalc` |
If |

`CL` |
Confidence Limit (default: .95). |

`rID` |
Correlation among predictor values in a linear model. |

`q` |
Number of predictors in the model. |

`alternative` |
Alternative hypothesis (defult = "two"). |

`keepSign` |
Return effect size with sign of test statistic? (default = TRUE). |

`keepSignNames` |
Which effect sizes should keep the sign if |

`st` |
Value(s) of a test statistic. |

`st` |
Value(s) of a test statistic. |

`df1` |
Degrees of freedom |

`df2` |
NULL or degrees of freedom of the denominator for the f-distribution. |

`N` |
Number of data points used in calculation of test-statistic. |

`n1` |
Number of data points in sample 1. |

`n2` |
Number of data points in sample 2. |

`esType` |
Type of test statistic. One of: "t", "lm.t", "f", "lm.f", "r", "X2", "Z", "lm.Z" |

`CIcalc` |
If |

`CL` |
Confidence Limit (default: .95). |

`rID` |
Correlation among predictor values in a linear model. |

`q` |
Number of predictors in the model. |

`alternative` |
Alternative hypothesis (defult = "two"). |

`keepSign` |
Return effect size with sign of test statistic? (default = TRUE). |

`keepSignNames` |
Which effect sizes should keep the sign if |

The procedure to calculate a variety of effect sizes is as follows:

If

`CIcalc == FALSE`

,`package::compute.es`

will be used to convert the test statistic to a large number of effect size estimates. The confidence intervals around the effect size estimates will be based meta-analytic estimates of effect size variance (e.g., for type - "t": tes).If

`CIcalc == TRUE`

,`package::MBESS`

will be used to calculate the confidence interval for the test statistic based on its noncentral distribution (e.g., for type - "t": conf.limits.nct). Subsequently the test statistic, as well as its lower and upper confidence limit will each be passed to`compute.es`

seperately.If

`keepSign == TRUE`

the sign of the test statistic will be copied to all the effect sizes in`keepSignNames`

.

The procedure to calculate a variety of effect sizes is as follows:

If

`CIcalc == FALSE`

,`package::compute.es`

will be used to convert the test statistic to a large number of effect size estimates. The confidence intervals around the effect size estimates will be based meta-analytic estimates of effect size variance (e.g., for type - "t": tes).If

`CIcalc == TRUE`

,`package::MBESS`

will be used to calculate the confidence interval for the test statistic based on its noncentral distribution (e.g., for type - "t": conf.limits.nct). Subsequently the test statistic, as well as its lower and upper confidence limit will each be passed to`compute.es`

seperately.If

`keepSign == TRUE`

the sign of the test statistic will be copied to all the effect sizes in`keepSignNames`

.

The effect sizes calculated by `compute.es`

corresponding to the test statistic(s), with either meta-analytic, or, exact CI.

The effect sizes calculated by `compute.es`

corresponding to the test statistic(s), with either meta-analytic, or, exact CI.

The prefix "lm" is currently disregarded, but will be implemented in future versions to indicate the test statistic is in fact a fixed factor in a linear model.

The prefix "lm" is currently disregarded, but will be implemented in future versions to indicate the test statistic is in fact a fixed factor in a linear model.

Fred Hasselman (inspired by RP:P function `any2r`

by CHJ Hartgerink)

Fred Hasselman (inspired by RP:P function `any2r`

by CHJ Hartgerink)

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