Description Usage Arguments Value Author(s) References See Also
Auxiliary function for controlling PCLM fitting. Use this function to set control
parameters of the pclm.fit
and other related functions.
1 2 3 4 5 6 7 8 | pclm.control(x.div = 1L, x.auto.trans = TRUE, x.max.ext = 25L,
zero.class.add = TRUE, zero.class.end = NULL, zero.class.frac = 0.2,
bs.use = "auto", bs.method = c("MortalitySmooth", "bs"),
bs.df = c("maxprec", "thumb"), bs.df.max = 200L, bs.deg = 3L,
opt.method = c("BIC", "AIC"), opt.tol = .Machine$double.eps^0.5,
pclm.deg = 2L, pclm.max.iter = 100L,
pclm.lsfit.tol = .Machine$double.eps^0.5,
pclm.tol = .Machine$double.eps^0.5)
|
x.div |
Number of sub-classes within PCLM tim/age class (default is 1).
Low value of the parameter makes the PCLM computation faster. It is however recommended to set
it to higher value (e.g. 10) for better |
x.auto.trans |
Logical indicating if automatically multiple age intervals to remove fractions.
|
x.max.ext |
Integer defining maximal multiple of an age interval. See also |
zero.class.add |
Logical indicating if additional zero count class (open interval)
should be added after last age class. |
zero.class.end |
Positive indicating the end of zero count class = anticipated end of
last (open) interval. If set to |
zero.class.frac |
Fraction of total range of |
bs.use |
Logical indicating if use B- or P-spline basis to speed-up computations.
Possible values: |
bs.method |
Basis for B- or P-spline used by
|
bs.df |
B- or P- spline degree of freedom (df, number of inner knots)
or a way to its calculation used in
|
bs.df.max |
Maximal number of knots (df) for B- or P-spline basis.
Defaut value is 200, but can be decreased if computations are slow.
Used in |
bs.deg |
Degree of the piecewise polynomial for B- or P-spline basis.
Default and recommended value is 3. Used in |
opt.method |
Selection criterion for |
opt.tol |
Tolerance for |
pclm.deg |
Order of differences of the components of |
pclm.max.iter |
Maximal number of iterations in |
pclm.lsfit.tol |
Tolerance for |
pclm.tol |
Tolerance for |
List with control parameters.
Maciej J. Danko <danko@demogr.mpg.de> <maciej.danko@gmail.com>
Rizzi S, Gampe J, Eilers PHC. Efficient estimation of smooth distributions from coarsely grouped data. Am J Epidemiol. 2015;182:138?47.
pclm.fit
, pclm.general
, pclm.core
,
pclm.opt
, pclm.aggregate
, pclm.compmat
,
pclm.interval.multiple
, pclm.nclasses
,
plot.pclm
, and summary.pclm
.
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