Description Usage Arguments Details Value References Examples

Computes the maximum likelihood estimate of the correlation between two possibly left-censored vectors. It is equivalent the the Pearson product-moment correlation for uncensored data.

1 |

`x` |
any data that can be converted to a left-censored data object. |

`y` |
any data that can be converted to a left-censored data object. |

`Full` |
how to compute the mean and standard deviation of |

`na.rm` |
logical, remove missing values before computing the correlation? |

`Full`

may be either logical or a numeric vector. If `Full`

is `TRUE`

,
then estimate the means and standard deviations for `x`

and `y`

.
If `Full`

is `FALSE`

, use the initial maximum likelihood estimate for those
statistics. Otherwise `Full`

can be a named vector containing `mnx`

, the mean
for `x`

; `sdx`

, the standard deviation for `x`

; `mny`

, the mean
for `y`

; `sdy`

, the standard deviation for `y`

. `Full`

can be set
to `FALSE`

if the optimization fails at large censoring levels or to improve
processing speed for large sample sizes.

A vector with these names:

`cor` |
the correlation between |

`mnx` |
the mean of |

`sdx` |
the standard deviation of |

`mny` |
the mean of |

`sdy` |
the standard deviation of |

`cx` |
the proportion of censored values of |

`cy` |
the proportion of censored values of |

`cxy` |
the proportion of censored values common to |

`n` |
the number of observations. |

`ll0` |
the log likelihood for cor=0 |

`llcor` |
the log likelihood for cor=cor |

Lyles, R.H., Williams, J.K., and Chuachoowong R., 2001, Correlating two viral load assays with known detection limits: Biometrics, v. 57 no. 4, p. 1238–1244.

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USGS-R/smwrQW documentation built on Sept. 22, 2018, 4:35 a.m.

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