This function runs a set of independence tests on a benchmark consisting of different functional dependence types (see `generate.benchmark.data`

)

1 |

`fun` |
function or character naming a function. A function should have two vectors of coordinates as first two arguments |

`args` |
list of additional arguments to the functions |

`types` |
numeric, which type of dependence to benchmark (see |

`noises` |
matrix of noise to add to each dependence. It should have |

`size` |
numeric, size of the data sets to generate (default 320 points) |

`nsim` |
numeric, how many replicate simulations to run under the null model and H1, default 500 |

`...` |
additional arguments to pass on to function |

This function makes use of `mclapply`

so `MC_CORES`

should be set to a number greater than 1 for parallelization

This function returns a list data structure that can be further processed with the functions of this package, `calculate.power,generate.roc`

Sebastian Dümcke duemcke@mpipz.mpg.de

`calculate.power,generate.roc`

, `generate.benchmark.data`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
noises = cbind(lin=c(.1,.5,.8),circ=c(.2,.4,.6))
mycor = function(...) cor(...)^2
results.cor=run.tests(mycor,args=list(),types=c(1,7),noises=noises,nsim=50,size=100)
results = run.tests("novelTest.extreme",args=list(maxi=10),types=c(1,7),noises=noises,nsim=25,
size=100)
## Not run:
x11()
par(mfrow=c(1,ncol(noises)))
roc.plot(generate.roc(results,pval=FALSE),legend=noises)
## End(Not run)
power = t(drop(calculate.power(results,.95,`>`)))
power.cor = t(drop(calculate.power(results.cor,.95,`>`)))
#cor is excellent at linear relationships, not so much for circular relationships:
#(increasing power is an artifact of low number of simulation, increase nsim in run.tests)
power.plot(list(cor=power.cor, novelTest=power),noises,show.legend="topright",mains=c("Linear",
"Circle"))
``` |

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