control.pclm: Auxiliary for Controlling 'pclm' Fitting

In ungroup: Penalized Composite Link Model for Efficient Estimation of Smooth Distributions from Coarsely Binned Data

Auxiliary for Controlling pclm Fitting

Description

Auxiliary for Controlling pclm Fitting

Usage

control.pclm(lambda     = NA,
             kr         = 2,
             deg        = 3,
             int.lambda = c(0.1, 1e+5),
             diff       = 2,
             opt.method = c("BIC", "AIC"),
             max.iter   = 1e+3,
             tol        = 1e-3)

Arguments

lambda	Smoothing parameter to be used in pclm estimation. If lambda = NA an algorithm will find the optimal values.
kr	Knot ratio. Number of internal intervals used for defining 1 knot in B-spline basis construction. See MortSmooth_bbase.
deg	Degree of the splines needed to create equally-spaced B-splines basis over an abscissa of data.
int.lambda	If lambda is optimized an interval to be searched needs to be specified. Format: vector containing the end-points.
diff	An integer indicating the order of differences of the components of PCLM coefficients. Default value: 2.
opt.method	Selection criterion of the model. Possible values are "AIC" and "BIC". Default: "BIC".
max.iter	Maximal number of iterations used in fitting procedure.
tol	Relative tolerance in PCLM fitting procedure. Default: 0.1% i.e. the estimated aggregate bins should be in the 0.1% error margin.

Value

A list with exactly eight control parameters.

See Also

pclm

Examples

control.pclm()

ungroup documentation built on May 29, 2024, 11:55 a.m.