# sample.dep.2: Simulate a sample of groups of dependent normal observations In warnes/exp.ssize: Estimate Effectivenes of Microarry Sample Size Estimation via Simulation

## Description

Given a list of covariance matrices, this function simulate normal observation based on the covariance matrix from first until the last one in the list of cov.matrix

## Usage

 `1` ```sample.dep.2(ngenes.dep, n, var.ratio, cov.matrix, delta, distrn = "normal") ```

## Arguments

 `ngenes.dep` integer number of genes to sample the dependent expression `n` positive integer number of replicates for each gene each group (control/treatment) `var.ratio` a positive number for the ratio of variance between treatment and control `cov.matrix` a list of covariance matrices based on which normal samples are generated `delta` effect size for sample size calculation the absolute distance from null value for expression values of genes coming from the true alternative hypothesis group `distrn` distribution of expression data, set to be normal for here

## Value

returns a matrix with ngenes.dep rows and 2*n columns with the first n columns being samples from control group and last n columns being from treatment group for each gene (row). each group of genes consists of subgroups of genes that are dependent

## Author(s)

Peng Liu [email protected]

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```library(MASS) n.dep <- 9; n <- 100; delta <- 5; var.ratio <- 1; n.matrix <- 3; cov.matrix <- list(n.matrix); for ( i in c(1:n.matrix)) { cov.matrix[[i]] <- diag(rep(i*0.1, 3)) } cov.matrix[[3]][1,1] <- cov.matrix[[2]][1,1] <- 1 cov.matrix[[3]][2,1] <- cov.matrix[[3]][1,2] <- 0.15 test.sample.dep <- sample.dep.2( n.dep , n, var.ratio, cov.matrix, delta, distrn = "normal") ```

warnes/exp.ssize documentation built on May 28, 2017, 12:58 a.m.