Make normally distributed vectors with specified relationships. See vignette("rnorm_multi", package = "faux") for details.
rnorm_multi(
n = 100,
vars = NULL,
mu = 0,
sd = 1,
r = 0,
varnames = NULL,
empirical = FALSE,
as.matrix = FALSE,
seed = NULL
)the number of samples required
the number of variables to return
a vector giving the means of the variables (numeric vector of length 1 or vars)
the standard deviations of the variables (numeric vector of length 1 or vars)
the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)
optional names for the variables (string vector of length vars) defaults if r is a matrix with column names
logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance
logical. If true, returns a matrix
DEPRECATED use set.seed() instead before running this function
a tbl of vars vectors
# 4 10-item vectors each correlated r = .5
rnorm_multi(10, 4, r = 0.5)
#> X1 X2 X3 X4
#> 1 0.01802558 0.25636386 0.58019692 0.08028537
#> 2 -1.82812785 -0.65769098 -2.83027285 -2.21576959
#> 3 0.90812009 0.18597855 1.09394285 0.69308582
#> 4 0.82690811 0.81208392 1.84790155 0.25361022
#> 5 -2.40031154 -1.86456845 0.45984866 -0.10324507
#> 6 0.90321967 0.09842935 0.05955834 0.07273941
#> 7 0.31865535 -1.34698381 -1.17313109 -1.81671727
#> 8 -1.54373252 -1.16776736 -1.42108043 0.02971391
#> 9 -1.41455579 -1.04093303 1.08889055 -0.18530451
#> 10 0.13024824 -0.08992106 -0.02768663 -0.91742788
# 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 1.00000000 0.04846831 -0.6560759
#> B 0.04846831 1.00000000 0.4773825
#> C -0.65607589 0.47738251 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 0.2183597 -0.5386588
#> B 0.2183597 1.0000000 0.4736930
#> C -0.5386588 0.4736930 1.0000000