# simulateDist: Data simulation for different distributions. In HannahVMeyer/PhenotypeSimulator: Flexible Phenotype Simulation from Different Genetic and Noise Models

## Description

Wrapper function to simulate data from different distribution with different parameter settings.

## Usage

 ```1 2 3 4 5 6 7 8``` ```simulateDist( x, dist = c("unif", "norm", "bin", "cat_norm", "cat_unif"), m = NULL, std = 1, categories = NULL, prob = NULL ) ```

## Arguments

 `x` The number [integer] of observations to simulate. `dist` Name of distribution [string] from which the observations are drawn. 'norm' is the normal distribution, 'unif' the uniform distribution 'bin' the binomial distribution, "cat_norm" samples categorical variables according to a normal distribution and "cat_unif" according to a uniform distribution. For "cat_norm", length(category)/2 is used mean for the normal distribution unless specified otherwise. `m` Mean of the normal distribution [double]/the mean between min and max for the uniform distribution [double]/ the rank of the category to be used as mean for "cat_norm" [integer]. `std` Standard deviation of the normal distribution or the distance of min/max from the mean for the uniform distribution [double]. `categories` Number of categories [integer] for simulating categorical variables (for distr="cat_norm" or "cat_unif"). `prob` Probability [double] of success for each trial (for distr="bin").

## Value

Numeric vector of length [x] with the sampled values

`runif`, `rnorm`, `rbinom` for documentation of the underlying distributions.
 ```1 2 3 4 5``` ```normal <- simulateDist(x=10, dist="norm", m=2, std=4) cat_normal <- simulateDist(x=10, dist="cat_norm", categories=5) cat_uniform <- simulateDist(x=10, dist="cat_unif", categories=5) uniform <- simulateDist(x=10, dist="unif", m=4, std=1) binomial <- simulateDist(x=10, dist="bin", prob=0.4) ```