Univariate or Multivariate Normal Fit
Computes the mean, covariance, and log-likelihood from fitting a single Gaussian (univariate or multivariate normal).
mvnX(data, prior = NULL, warn = NULL, ...) mvnXII(data, prior = NULL, warn = NULL, ...) mvnXXI(data, prior = NULL, warn = NULL, ...) mvnXXX(data, prior = NULL, warn = NULL, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
prior |
Specification of a conjugate prior on the means and variances. The default assumes no prior. |
warn |
A logical value indicating whether or not a warning should be issued
whenever a singularity is encountered.
The default is given by |
... |
Catches unused arguments in indirect or list calls via |
mvnXIIcomputes the best fitting Gaussian with the covariance restricted to be a multiple of the identity.
mvnXXIcomputes the best fitting Gaussian with the covariance restricted to be diagonal.
mvnXXXcomputes the best fitting Gaussian with ellipsoidal (unrestricted) covariance.
A list including the following components:
modelName |
A character string identifying the model (same as the input argument). |
parameters |
|
loglik |
The log likelihood for the data in the mixture model. |
Attributes: |
|
n <- 1000
set.seed(0)
x <- rnorm(n, mean = -1, sd = 2)
mvnX(x)
mu <- c(-1, 0, 1)
set.seed(0)
x <- sweep(matrix(rnorm(n*3), n, 3) %*% (2*diag(3)),
MARGIN = 2, STATS = mu, FUN = "+")
mvnXII(x)
set.seed(0)
x <- sweep(matrix(rnorm(n*3), n, 3) %*% diag(1:3),
MARGIN = 2, STATS = mu, FUN = "+")
mvnXXI(x)
Sigma <- matrix(c(9,-4,1,-4,9,4,1,4,9), 3, 3)
set.seed(0)
x <- sweep(matrix(rnorm(n*3), n, 3) %*% chol(Sigma),
MARGIN = 2, STATS = mu, FUN = "+")
mvnXXX(x)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.