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 -0.46894971 -0.8566868 0.8700007 -0.277605284
#> 2 0.71062932 -0.1333277 -0.3604500 0.330803256
#> 3 0.09549815 0.5060484 0.7286468 1.096548802
#> 4 -1.70330820 -0.4282653 0.1207761 -0.668776867
#> 5 -1.78865304 0.5372459 0.3838310 -0.237989406
#> 6 -0.53906501 -0.4029880 -0.6413800 -1.086578963
#> 7 0.75730935 0.3217305 1.1213584 -0.004724092
#> 8 1.95126575 0.7318149 0.2999517 1.302404256
#> 9 -1.33463881 0.8130180 -1.7403755 -0.693447886
#> 10 0.29850924 -1.5535492 -0.9020073 -1.424050271
# 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 0.2242373 -0.5357229
#> B 0.2242373 1.0000000 0.5216985
#> C -0.5357229 0.5216985 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 -0.0188244 -0.6871065
#> B -0.0188244 1.0000000 0.4922153
#> C -0.6871065 0.4922153 1.0000000