LaplaceFit: Estimation of location and scatter using the multivariate Laplace distribution
Description
Estimates the location vector and scatter matrix assuming the data came from a multivariate
Laplace distribution.
Usage
LaplaceFit(x, data, subset, na.action, tol = 1e-6, maxiter = 200)
Value
A list with class 'LaplaceFit' containing the following components:
- call
a list containing an image of the LaplaceFit call that produced the object.
- center
final estimate of the location vector.
- Scatter
final estimate of the scale matrix.
- logLik
the log-likelihood at convergence.
- numIter
the number of iterations used in the iterative algorithm.
- weights
estimated weights corresponding to the Laplace distribution.
- distances
estimated squared Mahalanobis distances.
Generic function print show the results of the fit.
Arguments
- x
a formula or a numeric matrix or an object that can be coerced to a numeric matrix.
- data
an optional data frame (or similar: see model.frame), used only if
x is a formula. By default the variables are taken from environment(formula).
- subset
an optional expression indicating the subset of the rows of
data that should be used in the fitting process.
- na.action
a function that indicates what should happen when the data contain NAs.
- tol
the relative tolerance in the iterative algorithm.
- maxiter
maximum number of iterations. The default is 200.
References
Yavuz, F.G., Arslan, O. (2018).
Linear mixed model with Laplace distribution (LLMM).
Statistical Papers 59, 271-289.
Examples
Run this codefit <- LaplaceFit(stack.x)
fit
# covariance matrix
p <- fit$dims[2]
Sigma <- (4 * (p + 1)) * fit$Scatter
Sigma
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