Description Usage Arguments Value Author(s) References Examples

The function `lgor_rd`

computes covariance between mean difference and standardized mean difference. See `mix.vcov`

for effect sizes of the same or different types.

1 2 3 |

`smd ` |
Standardized mean difference for outcome 2. |

`r ` |
Correlation coefficient of the two outcomes. |

`n1c ` |
Number of participants reporting outcome 1 in the control group. |

`n2c ` |
Number of participants reporting outcome 2 in the control group. |

`n1t ` |
Number of participants reporting outcome 1 in the treatment group. |

`n2t ` |
Number of participants reporting outcome 2 in the treatment group. |

`n12c ` |
Number of participants reporting both outcome 1 and outcome 2 in the control group. By default, it is equal to the smaller number between |

`n12t ` |
Number defined in a similar way as |

`sd1t ` |
Sample standard deviation of outcome 1 for the treatment group. |

`sd2t ` |
Sample standard deviation of outcome 2 for the treatment group. |

`sd1c ` |
Defined in a similar way as |

`sd2c ` |
Defined in a similar way as |

`g` |
Computed Hedge's g from the input argument smd for outcome 2. |

`v` |
Computed covariance. |

Min Lu

Ahn, S., Lu, M., Lefevor, G.T., Fedewa, A. & Celimli, S. (2016). Application of meta-analysis in sport and exercise science. In N. Ntoumanis, & N. Myers (Eds.), *An Introduction to Intermediate and Advanced Statistical Analyses for Sport and Exercise Scientists* (pp.233-253). Hoboken, NJ: John Wiley and Sons, Ltd.

Wei, Y., & Higgins, J. (2013). Estimating within study covariances in multivariate meta-analysis with multiple outcomes. *Statistics in Medicine, 32*(7), 119-1205.

1 2 3 4 5 6 7 8 9 10 11 | ```
## a simple example
md_smd(smd = 1, r = 0.71, n1c = 34, n2c = 35, n1t = 25, n2t = 32,
sd1t = 0.6, sd2t = 0.4, sd1c = 8, sd2c = 0.9)
## calculate covariances for variable SBP and DBP in Geeganage2010 data
attach(Geeganage2010)
SBP_DBP <- unlist(lapply(1:nrow(Geeganage2010), function(i){md_smd(smd = SMD_DBP, r = 0.71,
n1c = nc_SBP[i], n2c = nc_DBP[i], n1t = nt_SBP[i], n2t = nt_DBP[i],
sd1t = sdt_SBP[i], sd2t = sdt_DBP[i],
sd1c = sdc_SBP[i], sd2c = sdc_SBP[i])$v}))
SBP_DBP
## the function mix.vcov() should be used for dataset
``` |

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