Description Usage Arguments Details Value Author(s) Examples

Generate two nxp data sets: a training set and a test set, as well as outcome vectors y and yte of length n indicating the class labels of the training and test observations.

1 | ```
CountDataSet(n, p, K, param, sdsignal)
``` |

`n` |
Number of observations desired. |

`p` |
Number of features desired. Note that 30% of the features will differ between classes, though some of those differences may be small. |

`K` |
Number of classes desired. Note that the function requires that n be at least equal to 4K – i.e. there must be at least 4 observations per class on average. |

`param` |
The dispersion parameter for the negative binomial distribution. The negative binomial distribution is parameterized using "mu" and "size" in the R function "rnbinom". That is, Y ~ NB(mu, param) means that E(Y)=mu and Var(Y) = mu+mu^2/param. So when param is very large this is essentially a Poisson distribution, and when param is smaller then there is a lot of overdispersion relative to the Poisson distribution. |

`sdsignal` |
The extent to which the classes are different. If this equals zero then there are no class differences and if this is large then the classes are very different. |

This is based in part on a function in the DESeq Bioconductor package (Anders and Huber 2010 Genome Biology) for generating a simulated RNA sequencing data set.

`x` |
nxq data matrix. May have q<p because features with 0 total counts are removed. |

`y` |
class labels for the n observations in x. |

`xte` |
nxq data matrix of test observations; the q features are those with >0 total counts in x. So q<=p. |

`yte` |
class labels for the n observation in xte. |

Daniela Witten, based on software written by Anders and Huber in the DESeq Bioconductor package.

1 2 3 | ```
set.seed(1)
dat <- CountDataSet(n=20,p=100,sdsignal=2,K=4,param=10)
dd <- PoissonDistance(dat$x,type="mle", transform=TRUE)
``` |

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