pbcor | R Documentation |

The `pbcor`

function computes the percentage bend correlation coefficient, `wincor`

the Winsorized correlation,
`pball`

the percentage bend correlation matrix, `winall`

the Winsorized correlation matrix.

pbcor(x, y = NULL, beta = 0.2, ci = FALSE, nboot = 500, alpha = 0.05, ...) pball(x, beta = 0.2, ...) wincor(x, y = NULL, tr = 0.2, ci = FALSE, nboot = 500, alpha = 0.05, ...) winall(x, tr = 0.2, ...)

`x` |
a numeric vector, a matrix or a data frame. |

`y` |
a second numeric vector (for correlation functions). |

`beta` |
bending constant. |

`tr` |
amount of Winsorization. |

`ci` |
whether boostrap CI should be computed or not. |

`nboot` |
number of bootstrap samples for CI computation. |

`alpha` |
alpha level for CI computation. |

`...` |
currently ignored. |

It tested is whether the correlation coefficient equals 0 (null hypothesis) or not. Missing values are deleted pairwise. The tests are sensitive to heteroscedasticity. The test statistic H in `pball`

tests the hypothesis that all correlations are equal to zero.

`pbcor`

and `wincor`

return an object of class `"pbcor"`

containing:

`cor` |
robust correlation coefficient |

`test` |
value of the test statistic |

`p.value` |
p-value |

`n` |
number of effective observations |

`cor_ci` |
bootstrap confidence interval |

`call` |
function call |

`pball`

and `winall`

return an object of class `"pball"`

containing:

`pbcorm` |
robust correlation matrix |

`p.values` |
p-values |

`H` |
H-statistic |

`H.p.value` |
p-value H-statistic |

`cov` |
variance-covariance matrix |

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

`twocor`

x1 <- subset(hangover, subset = (group == "control" & time == 1))$symptoms x2 <- subset(hangover, subset = (group == "control" & time == 2))$symptoms pbcor(x1, x2) pbcor(x1, x2, beta = 0.1, ci = TRUE) wincor(x1, x2) wincor(x1, x2, tr = 0.1, ci = TRUE) require(reshape) hanglong <- subset(hangover, subset = group == "control") hangwide <- cast(hanglong, id ~ time, value = "symptoms")[,-1] pball(hangwide) winall(hangwide)

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