rmvt | R Documentation |
Function generates data from the multivariate t distribution given a covariance matrix, non-centrality parameter (or mode), and degrees of freedom.
rmvt(n, sigma, df, delta = rep(0, nrow(sigma)), Kshirsagar = FALSE)
n |
number of observations to generate |
sigma |
positive definite covariance matrix |
df |
degrees of freedom. |
delta |
the vector of non-centrality parameters of length |
Kshirsagar |
logical; triggers whether to generate data with non-centrality parameters or to adjust the simulated data to the mode of the distribution. The default uses the mode |
a numeric matrix with columns equal to ncol(sigma)
Phil Chalmers rphilip.chalmers@gmail.com
Chalmers, R. P., & Adkins, M. C. (2020). Writing Effective and Reliable Monte Carlo Simulations
with the SimDesign Package. The Quantitative Methods for Psychology, 16
(4), 248-280.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.20982/tqmp.16.4.p248")}
Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte
Carlo simulation. Journal of Statistics Education, 24
(3), 136-156.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10691898.2016.1246953")}
runSimulation
# random t values given variances [3,6], covariance 2, and df = 15
sigma <- matrix(c(3,2,2,6), 2, 2)
x <- rmvt(1000, sigma = sigma, df = 15)
head(x)
summary(x)
plot(x[,1], x[,2])
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