generate.benchmark.data: Generating functional dependencies
In knnIndep: Independence tests and benchmarks

Description Usage Arguments Value Author(s) See Also Examples

Generate functional dependencies for benchmarking tests of independence. This function can generate 8 types of functional dependence: linear, quadratic, cubic, two sine functions, x^(1/4), step function and a circular dependence.

1	generate.benchmark.data(typ, noises, n, project = FALSE, windx = 1, windy = 1)

`typ`	decimal, which type of dependence to generate. 1: linear 2: quadratic 3: cubic 4: sine period pi/4 5: sine period pi/16 6: x^(1/4) 7: circle 8: step function
`noises`	vector of noise values to apply to the generated dependence. The noise is normally distributed.
`n`	decimal, size of sample to return.
`project`	boolean (default FALSE), wether to project the generated dependence onto a torus
`windx`	decimal, how many times the dependence should wind around the torus in x-direction. Only used if `project` is `TRUE`
`windy`	decimal, how many times the dependence should wind around the torus in y-direction. Only used if `project` is `TRUE`

list with two elements

`x`	matrix of x-coordinates, each column corresponds to a noise level from `noises`
`y`	matrix of y-coordinates, each column corresponds to a noise level from `noises`

Sebastian Dümcke duemcke@mpipz.mpg.de

generate.patchwork.copula for generating non-functional dependence and run.tests for benchmarking tests of independence

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#generate a quadratic dependence of 10 points with two noise levels 0.3 and 0.6
generate.benchmark.data(2,c(.3,.6),10)
plot(generate.benchmark.data(4,.2,1000))

$x
           [,1]       [,2]
 [1,] 0.4225692 0.68005846
 [2,] 0.3012236 0.88798707
 [3,] 0.2560643 0.53826192
 [4,] 0.8122777 0.47021578
 [5,] 0.9862404 0.55878343
 [6,] 0.2195186 0.44723613
 [7,] 0.8306193 0.42413410
 [8,] 0.2084403 0.84349363
 [9,] 0.3815134 0.08726359
[10,] 0.1585132 0.90444383

$y
             [,1]       [,2]
 [1,]  0.13471794 -0.7054456
 [2,] -0.05734843  0.5011241
 [3,]  0.31522694 -0.4058433
 [4,]  0.57407746  0.2201121
 [5,]  0.83880061 -1.1436079
 [6,]  0.64645499 -0.2617601
 [7,]  0.66637810 -0.5883212
 [8,]  0.14914134 -0.5083607
 [9,]  0.55445046  0.3576194
[10,]  0.84936922  0.8578760