Description Usage Arguments Details Value References See Also Examples

Calculates a partial or semipartial correlation using one of the specified robust methods Taba linear or Taba rank correlation.

1 2 3 | ```
taba.partial(x, y, ..., regress, method = c("taba", "tabarank", "tabwil", "tabwilrank"),
alternative = c("less", "greater", "two.sided"),
semi = c("none", "x", "y"), omega)
``` |

`x` |
A numeric vector of length greater than 2 must be same length as y and covariates listed in ... |

`y` |
A numeric vector of length greater than 2 must be same length as x and covariates listed in ... |

`...` |
Numeric vectors used as covariates of length equal to x and y |

`regress` |
A string variable " |

`method` |
A character string of |

`alternative` |
Character string specifying the alternative hypothesis must be one
of |

`semi` |
A character string specifying which variable (x or y) should be adjusted. |

`omega` |
Numeric allowing the user to alter the tuning constant. If one is not specified, the function will default to 0.45 for Taba and Taba rank, and 0.1 for TabWil and TabWil rank. Range is between 0 and 1. |

This function calculates the partial or semipartial association of two
numeric vectors, or columns of a matrix or data frame composed
of more than two numeric elements, adjusting for covariates of length equal to
x and y. Covariates are combined colomn-wise and can be numeric vectors, matricies,
or data frames with numeric cells. Each column in the matrix or data frame will be
treated as a different covariate, and must have different names from x and y.
Missing values in x, y, or any of the covariates are deleted row-wise.
The default for this function is a two sided test using Taba linear partial
correlation, with the tuning constant `omega`

equal to 0.45 for Taba and
Taba rank, and 0.1 for TabWil and TabWil rank. Range is between 0 and 1.
The variable you are not controlling must be continuous when using semipartial correlation.

This function returns the robust association between two numeric vectors, adjusting for specified covariates. In addition, this function can provide the semipartial correlation, if specified.

The paper is under review for possible publication.

`taba`

for calculating Taba linear or Taba rank (monotonic) correlations
`taba.test`

for testing Taba linear or Taba rank (monotonic) correlations
`taba.gpartial`

for generalized partial correlations
`taba.matrix`

for calculating correlation, p-value, and distance matricies

1 2 3 4 5 6 7 | ```
x = rnorm(100)
y = rnorm(100)
z1 = rnorm(100)
z2 = rnorm(100)
z3 = rnorm(100)
taba.partial(x, y, z1, z2, z3, method = "tabwilrank")
taba.partial(x, y, z2, alternative = "less", semi = "x")
``` |

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