View source: R/gof_power_cont.R

gof_power_cont | R Documentation |

Find the power of various gof tests for continuous data.

```
gof_power_cont(
pnull,
rnull,
ralt,
param_alt,
w = function(x) -99,
phat = function(x) -99,
TS,
TSextra = NA,
alpha = 0.05,
Range = c(-Inf, Inf),
B = c(1000, 1000),
nbins = c(100, 10),
rate = 0,
maxProcessors,
minexpcount = 5,
ChiUsePhat = TRUE
)
```

`pnull` |
function to find cdf under null hypothesis |

`rnull` |
function to generate data under null hypothesis |

`ralt` |
function to generate data under alternative hypothesis |

`param_alt` |
vector of parameter values for distribution under alternative hypothesis |

`w` |
(Optional) function to calculate weights, returns -99 if no weights |

`phat` |
=function(x) -99, function to estimate parameters from the data, or -99 if no parameters aare estimated |

`TS` |
user supplied function to find test statistics, if any |

`TSextra` |
=NA, list provided to TS |

`alpha` |
=0.05, the level of the hypothesis test |

`Range` |
=c(-Inf, Inf) limits of possible observations, if any |

`B` |
=c(1000, 1000), number of simulation runs to find power and null distribution |

`nbins` |
=c(100,10), number of bins for chi square tests. |

`rate` |
=0 rate of Poisson if sample size is random, 0 if sample size is fixed |

`maxProcessors` |
maximum of number of processors to use, 1 if no parallel processing is needed or number of cores-1 if missing |

`minexpcount` |
=5 minimal expected bin count required |

`ChiUsePhat` |
=TRUE, if TRUE param is estimated parameter, otherwise minimum chi square method is used. |

A numeric matrix of power values.

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