| rmvnorm {splus2R} | R Documentation |
Random generation for the multivariate normal (also called Gaussian) distribution.
rmvnorm(n, mean=rep(0,d), cov=diag(d), sd, rho, d=2)
n |
sample size – number of random vectors of length d to return (as rows in a matrix). |
cov |
covariance or correlation matrix with d rows and columns. |
d |
dimension of the multivariate normal. |
mean |
vector of length d, or matrix with n rows and d columns. |
rho |
scalar, vector, or bdVector of length n, containing correlations for bivariate data. This is ignored if cov is supplied. |
sd |
vector of length d, or matrix with n rows and d columns, containing standard deviations. If supplied, the rows and columns of cov are multiplied by sd. In particular, if cov is a correlation matrix and sd is a vector of standard deviations, the result is a covariance matrix. If sd is a matrix then one row is used for each observation. |
random sample ( rmvnorm) for the multivariate normal distribution.
anyMissing,
as.rectangular,
colIds,
colMaxs,
colMedians,
colMins,
colRanges,
colStdevs,
colVars,
deparseText,
ifelse1,
is.numeric.atomic.vector,
is.rectangular,
is.missing,
is.zero,
lowerCase,
oldUnclass,
numCols,
numRows,
peaks,
positions,
rowIds,
rowMaxs,
stdev,
subscript2d,
upperCase,
vecnorm,
which.na.
## 5 rows and 2 independent columns rmvnorm(5) ## 5 rows and 3 independent columns rmvnorm(5, mean=c(9,3,1)) ## 2 columns, std. dev. 1, correlation .9 rmvnorm(5, rho=.9) ## specify variable means and covariance matrix rmvnorm(5, mean=c(9,3), cov=matrix(c(4,1,1,2), 2))