Description Usage Arguments Details Value Author(s) References See Also Examples
Main procedure to calculate PCLM with automated step.
1 2  pclm.general(x, y, count.type = c("DX", "LX"), out.step = "auto",
exposures = NULL, control = list())

x 
Vector with start of the interval for age/time classes. 
y 
Vector with counts, e.g. 
count.type 
Type of the data, deaths( 
out.step 
Age interval length in output aggregated lifetable. If set to 
exposures 
Optional exposures to calculate smooth mortality rates.
A vector of the same length as 
control 
List with additional parameters. See 
The function has four major steps:
Calculate interval multiple (pclm.interval.multiple
to remove fractional parts from x
vector.
The removal of fractional parts is necessary to build composition matrix.
Calculate composition matrix using pclm.compmat
.
Fit PCLM model using pclm.opt
.
Calculate aggregated (grouped) lifetable using pclm.aggregate
.
More details for PCLM algorithm can be found in reference [1], but see also pclm.fit
and pclm.compmat
.
The output is of "pclm"
class with the components:

Lifetable based on aggregated PCLM fit and defined by 

Lifetable based on original (raw) PCLM fit. 

PCLM fit used to construct lifetables. 

Interval multiple, see 

Value of 

Interval length of aggregated lifetable, see 

Used control parameters, see 

List with warnings. 
Maciej J. Danko <[email protected]> <[email protected]>
Rizzi S, Gampe J, Eilers PHC. Efficient estimation of smooth distributions from coarsely grouped data. Am J Epidemiol. 2015;182:138?47.
Rizzi S, Thinggaard M, Engholm G, et al. Comparison of nonparametric methods for ungrouping coarsely aggregated data. BMC Medical Research Methodology. 2016;16:59. doi:10.1186/s1287401601578.
pclm.fit
, pclm.compmat
, pclm.interval.multiple
, and pclm.nclasses
.
1 2  # The examples with use of the \code{pash} object are presented in \link{pclm.fit}.
# Explicit examples of use \code{pclm.general} (especially how to use exposures) are to be written in a next package release.

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