Estimation of location and scatter using the multivariate Laplace distribution
Estimates the location vector and scatter matrix assuming the data came from a multivariate Laplace distribution.
LaplaceFit(x, data, subset, na.action, tol = 1e-6, maxiter = 200)
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.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.
Yavuz, F.G., Arslan, O. (2018). Linear mixed model with Laplace distribution (LLMM). Statistical Papers 59 , 271-289.
cov
fit <- LaplaceFit(stack.x) fit # covariance matrix p <- fit$dims[2] Sigma <- (4 * (p + 1)) * fit$Scatter Sigma