Simulate from a Multivariate Normal Distribution
mvrnorm.RdProduces one or more samples from the specified multivariate normal distribution.
mvrnorm(n = 1, mu, Sigma, tol = 1e-06, empirical = FALSE)Arguments
- n
the number of samples required.
- mu
a vector giving the means of the variables.
- Sigma
a positive-definite symmetric matrix specifying the covariance matrix of the variables.
- tol
tolerance (relative to largest variance) for numerical lack of positive-definiteness in Sigma.
- empirical
logical. If true, mu and Sigma specify the empirical not population mean and covariance matrix.
Value
If n = 1 a vector of the same length as mu, otherwise an n by length(mu) matrix with one sample in each row.
Details
The matrix decomposition is done via eigen; although a Choleski decomposition might be faster, the eigendecomposition is stabler.* Causes creation of the dataset .Random.seed if it does not already exist, otherwise its value is updated.
See also
Other Auxiliary:
IQRloadCSVdata(),
IQRsaveCSVdata(),
and(),
aux_explode(),
aux_explodePC(),
aux_fileparts(),
aux_fileread(),
aux_filewrite(),
aux_getRelPath(),
aux_mkdir(),
aux_na_locf(),
aux_postFillChar(),
aux_preFillChar(),
aux_quantilenumber(),
aux_rmdir(),
aux_simplifypath(),
aux_splitVectorEqualPieces(),
aux_strFindAll(),
aux_strrep(),
aux_strtrim(),
aux_unlevel(),
aux_version(),
calcAICBIC(),
clusterX(),
compare_IQRmodel_IQRsysModel_simulation(),
fit_EmaxModel(),
format_GUM(),
ge(),
gen_aux_version(),
geocv(),
geomean(),
geosd(),
ginv(),
gt(),
interp0(),
interp1(),
interpcs(),
inv_logit(),
le(),
logit(),
lt(),
mod(),
norm_M3(),
or(),
piecewise(),
progressBar(),
remove_duplicates(),
run_silent_IQR(),
stopIQR(),
tempdirIQR(),
tempfileIQR(),
warningIQR()