pclm.fit: Fit PCLM Models

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

Fit PCLM Models

Description

This is an internal function used to estimate PCLM model. It is used by pclm and pclm2D functions

Usage

pclm.fit(
  x,
  y,
  nlast,
  offset,
  out.step,
  verbose,
  lambda,
  kr,
  deg,
  diff,
  max.iter,
  tol,
  type
)

Arguments

x	Vector containing the starting values of the input intervals/bins. For example: if we have 3 bins [0,5), [5,10) and [10, 15), x will be defined by the vector: c(0, 5, 10).
y	Vector with counts to be ungrouped. It must have the same dimension as x.
nlast	Length of the last interval. In the example above nlast would be 5.
offset	Optional offset term to calculate smooth mortality rates. A vector of the same length as x and y. See \insertCiterizzi2015;textualungroup for further details.
out.step	Length of estimated intervals in output. Values between 0.1 and 1 are accepted. Default: 1.
verbose	Logical value. Indicates whether a progress bar should be shown or not. Default: FALSE.
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.
diff	An integer indicating the order of differences of the components of PCLM coefficients. Default value: 2.
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.
type	Type of PCLM model. Options: "1D", "2D" for univariate and two-dimensional model respectively.

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