# rnorm_multi: Multiple correlated normal distributions In faux: Simulation for Factorial Designs

## Description

Make normally distributed vectors with specified relationships. See `vignette("rnorm_multi", package = "faux")` for details.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```rnorm_multi( n = 100, vars = NULL, mu = 0, sd = 1, r = 0, varnames = NULL, empirical = FALSE, as.matrix = FALSE, seed = NULL ) ```

## Arguments

 `n` the number of samples required `vars` the number of variables to return `mu` a vector giving the means of the variables (numeric vector of length 1 or vars) `sd` the standard deviations of the variables (numeric vector of length 1 or vars) `r` the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector) `varnames` optional names for the variables (string vector of length vars) defaults if r is a matrix with column names `empirical` logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance `as.matrix` logical. If true, returns a matrix `seed` DEPRECATED use set.seed() instead before running this function

## Value

a tbl of vars vectors

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```# 4 10-item vectors each correlated r = .5 rnorm_multi(10, 4, r = 0.5) # set r with the upper right triangle b <- rnorm_multi(100, 3, c(0, .5, 1), 1, r = c(0.2, -0.5, 0.5), varnames=c("A", "B", "C")) cor(b) # set r with a correlation matrix and column names from mu names c <- rnorm_multi( n = 100, mu = c(A = 0, B = 0.5, C = 1), r = c( 1, 0.2, -0.5, 0.2, 1, 0.5, -0.5, 0.5, 1) ) cor(c) ```

faux documentation built on Sept. 14, 2021, 1:08 a.m.