gof_power | R Documentation |

Find the power of various gof tests for continuous data.

```
gof_power(
pnull,
vals = NA,
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(50, 10),
rate = 0,
maxProcessors,
minexpcount = 5,
ChiUsePhat = TRUE
)
```

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

`vals` |
=NA, values of rv, if data is discrete, NA if data is continuous |

`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 |

`TS` |
user supplied function to find test statistics |

`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.

```
# Power of tests when null hypothesis specifies the standard normal distribution but
# true data comes from a normal distribution with mean different from 0.
pnull = function(x) pnorm(x)
rnull = function() rnorm(50)
ralt = function(mu) rnorm(50, mu)
TSextra = list(qnull=function(x) qnorm(x))
gof_power(pnull, NA, rnull, ralt, c(0.25, 0.5), TSextra=TSextra, B=c(500, 500))
# Power of tests when null hypothesis specifies normal distribution and
# mean and standard deviation are estimated from the data.
# Example is not run because it takes several minutes.
# true data comes from a normal distribution with mean different from 0.
pnull = function(x, p=c(0, 1)) pnorm(x, p[1], ifelse(p[2]>0.001, p[2], 0.001))
rnull = function(p=c(0, 1)) rnorm(50, p[1], ifelse(p[2]>0.001, p[2], 0.001))
phat = function(x) c(mean(x), sd(x))
TSextra = list(qnull = function(x, p=c(0, 1)) qnorm(x, p[1],
ifelse(p[2]>0.001, p[2], 0.001)))
gof_power(pnull, NA, rnull, ralt, c(0, 1), phat=phat, TSextra=TSextra,
B=c(200, 200), maxProcessor=2)
# Power of tests when null hypothesis specifies Poisson rv with rate 100 and
# true rate is 100.5
vals = 0:250
pnull = function() ppois(0:250, 100)
rnull =function () table(c(0:250, rpois(1000, 100)))-1
ralt =function (p) table(c(0:250, rpois(1000, p)))-1
gof_power(pnull, vals, rnull, ralt, param_alt=100.5, B=c(500,500))
# Power of tests when null hypothesis specifies a Binomial n=10 distribution
# with the success probability estimated
vals = 0:10
pnull=function(p) pbinom(0:10, 10, ifelse(0<p&p<1, p, 0.001))
rnull=function(p) table(c(0:10, rbinom(1000, 10, ifelse(0<p&p<1, p, 0.001))))-1
ralt=function(p) table(c(0:10, rbinom(1000, 10, p)))-1
phat=function(x) mean(rep(0:10,x))/10
gof_power(pnull, vals, rnull, ralt, c(0.5, 0.6), phat=phat,
B=c(200, 200), maxProcessor=2)
```

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