fit.mstil.r.batch: This function finds the maximum penalised quasi likelihood...

In henrylobster/mstil: 'Multivariate Skew t Distribution with Independent Logistic Skewing Function (MSTIL)'

Description

This function finds the maximum penalised quasi likelihood estiamtes for mstil.r using batch of subsamples.

Usage

fit.mstil.r.batch(x, param = NULL, show.progress = TRUE, control = list())

Arguments

x	matrix of quantiles of size n x k. Each row is taken as a quantile.
param	list of inital parameters, contains lambda, delta, Ainv, and nu.
show.progress	logical value. If TRUE, progress of the algorithm will be printed in console. By default TRUE.
control	list of control variables, see 'details'.

Details

The control argument is a list that accepts the following components.

cvgNR

a positive integer. The algorithm stops when the estimated log-likelihood is not improved by at least cvgTolR on average in cvgNR iterations. By default 5.

cvgTolR

a positive value. The algorithm stops when the estimated log-likelihood is not improved by at least cvgTolR on average in cvgNR iterations. By default 1e-2.

lambdaPenalty

a positive value, represents the L2 penalty coefficient for lambda. By default 0.

ainvPenalty

a positive value, represents the L2 penalty coefficient for Ainv. By default 0.

maxitR

a positive integer, represents the maximum number iterations allowed. By default 1e3.

maxitOptimR

a positive integer, represents the maximum number of iterations allowed in optim. By default 10.

batchSizeR

a positive integer, represents the batch sample size. By default n.

Value

a list with components:

logLik	a vector of values of the log-likelihood function after each itereation.
par	a list of lists of fitted parameters after each iteration.
time	a vector recorded the time elapsed after each iteration.

Examples

# Not run:
# data(RiverFlow)
# fit.mstil.r.batch(as.matrix(log(RiverFlow)), control = list(batchSizeR = 100))

henrylobster/mstil documentation built on Sept. 25, 2020, 3:48 p.m.