# sim.data: Generates data for illustrative purposes In denpro: Visualization of Multivariate Functions, Sets, and Data

## Description

Returns a random sample from some distributions, to illustrate some visulization tools. Returns also the density (as a piecewise constant function) for some examples, or the distribution function.

## Usage

 ```1 2 3 4``` ```sim.data(n = NULL, seed = 1, N = NULL, type = "mulmod", M = NULL, sig = NULL, p = NULL, d = NULL, cova = NULL, marginal = NULL, t = NULL, df = NULL, distr = FALSE, noisedim = 1, sig1 = 0.5, sig2 = 1.5, diff = 0.1, dist = 4) ```

## Arguments

 `n` positive integer; size of the sample to be generated `seed` real number; seed for the random number generator. `N` 2*1 vector of positive integers; the size of the grid where the piecewise constant function is evaluated `type` "mixt", "mulmod", "fox", "tetra3d", "penta4d", "cross", "gauss", "student", "gumbel", "1d2modal", or "claw". `M` mixnum*d-matrix; rows of M are means of the Gaussians in the mixture. We have a mixture of "mixnum" Gaussians, whose dimension is d. `sig` mixnum*d-matrix; rows of sig are the diagonals of the covariance matrices of the mixtures. `p` mixnum-vector; weights for the members of the mixture. The sum of elements of "p" is 1. `d` positive integer; dimension of the vectors of the sample to be generated, need to be given only when type="mixt" and d=1 `cova` Covariance matrix for the Gauss or Student copulas `marginal` NULL, "gauss", or "student"; this parameter is used to give the marginal distribution for the Gauss or Student copulas; if marginal=NULL, then the uniform marginals are used `t` if marginal="student", gives the degrees of freedom `df` degrees of freedom for the Student copula `distr` internal (implemented for "1d2modal") TRUE, if one wants the distribution function instead of the density function `noisedim` the number of noise dimension in the projection pursuit example ("fssk") `sig1` standard deviation for "cross" and "diff1d" `sig2` second standard deviation for "cross" `diff` parameter for "diff1d"; the difference between the Gaussians in the 1D mixture `dist` a positive real number; gives the distance between the mixture centers in the 4D mixture of Gaussians "penta4d"

## Details

When type="mixt", generates data from a mixture of Gaussians. When type="mulmod", the density is 3-modal. When type="fox", the density has multimodal level sets.

## Value

If "n" is not NULL, then the function returns a n*d-data matrix or a n*2-data matrix, if "N" is not NULL, then the function returns a piecewise constant function on the grid of size N[1]*N[2], if the both are NULL, then the function returns the mean, covariance, and the weights of the mixture components

Jussi Klemela

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30``` ```d<-2 mixnum<-3 M<-matrix(0,mixnum,d) M[1,]<-c(0,0) M[2,]<-c(4,0) M[3,]<-c(0,4) sig<-matrix(1,mixnum,d) p0<-1/mixnum p<-p0*rep(1,mixnum) n<-100 dendat<-sim.data(type="mixt",n=n,M=M,sig=sig,p=p,seed=1) plot(dendat) dendat<-sim.data(n=100) plot(dendat) N<-c(20,20) pcf<-sim.data(N=N) dp<-draw.pcf(pcf,pnum=c(30,30)) contour(dp\$x,dp\$y,dp\$z,drawlabels=FALSE) sim.data() type="fox" dendat<-sim.data(n=100,type=type) plot(dendat) pcf<-sim.data(N=N,type=type) dp<-draw.pcf(pcf,pnum=c(30,30)) contour(dp\$x,dp\$y,dp\$z,drawlabels=FALSE) ```

denpro documentation built on May 29, 2017, 11:06 p.m.