Mort2Dsmooth_estimate: Estimate 2D P-splines for two given lambdas

Description Usage Arguments Details Value Author(s) See Also

View source: R/Mort2Dsmooth_estimate.R

Description

This is an internal function of package MortalitySmooth which estimates coefficients and computes diagnostics for two-dimensional penalized B-splines for two given smoothing parameters within the function Mort2Dsmooth.

Usage

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Mort2Dsmooth_estimate(x, y, Z, offset, psi2, wei,
                      Bx, By, nbx, nby, RTBx, RTBy,
                      lambdas, Px, Py, a.init, 
                      MON, TOL1, MAX.IT)

Arguments

x

vector for the abscissa of data.

y

vector for the ordinate of data.

Z

matrix of counts response.

offset

matrix with an a priori known component (optional).

psi2

overdispersion parameter.

wei

an optional matrix of weights to be used in the fitting process.

Bx

B-splines basis for the x-axis.

By

B-splines basis for the y-axis.

nbx

number of B-splines for the x-axis.

nby

number of B-splines for the y-axis.

RTBx

tensors product of B-splines basis for the x-axis.

RTBy

tensors product of B-splines basis for the y-axis.

lambdas

vector with the two smoothing parameters.

Px

penalty factor for the x-axis.

Py

penalty factor for the y-axis.

a.init

matrix with the initial coefficients.

MON

logical switch indicating if monitoring is required.

TOL1

the tolerance level in the IWLS algorithm.

MAX.IT

the maximum number of iterations.

Details

Internal function used in Mort2Dsmooth for estimating coefficients and computing diagnostics.

Value

A list with components:

a

fitted coefficients (in a matrix).

h

diagonal of the hat-matrix.

df

effective dimension of used degree of freedom.

aic

Akaike's Information Criterion.

bic

Bayesian Information Criterion.

dev

Poisson deviance.

tol

tolerance level.

BWB

inner product of basis and weights.

P

penalty matrix.

Author(s)

Carlo G Camarda

See Also

Mort2Dsmooth_update, Mort2Dsmooth.


MortalitySmooth documentation built on May 29, 2017, 7:11 p.m.