Make normally distributed vectors with specified relationships. See vignette("rnorm_multi", package = "faux") for details.
Usage
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
Examples
# 4 10-item vectors each correlated r = .5
rnorm_multi(10, 4, r = 0.5)
#> X1 X2 X3 X4
#> 1 0.09382122 -0.50664862 -0.521255335 -0.734695416
#> 2 0.89526593 0.88454525 0.757609421 1.473452579
#> 3 1.30449645 -0.31040190 0.404083912 -0.004584287
#> 4 -0.85700886 1.10145390 -0.490797966 0.544807451
#> 5 -0.72615810 0.21564451 -0.004508819 0.887187386
#> 6 0.79510223 1.24553740 1.208772396 0.847168701
#> 7 -1.08686802 0.20542374 -0.001241857 0.193074464
#> 8 -0.39906602 0.53042333 -0.601213552 -0.236948812
#> 9 0.84926756 0.71676114 -0.222803659 -0.006101537
#> 10 0.91817794 -0.07973927 0.020993893 0.886064173
# 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)
#> A B C
#> A 1.0000000 0.1992337 -0.5894921
#> B 0.1992337 1.0000000 0.4518370
#> C -0.5894921 0.4518370 1.0000000
# 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)
#> A B C
#> A 1.0000000 0.2478804 -0.5018917
#> B 0.2478804 1.0000000 0.4988469
#> C -0.5018917 0.4988469 1.0000000