Mort2Dsmooth_estimate: Estimate 2D P-splines for two given lambdas
In MortalitySmooth: Smoothing and Forecasting Poisson Counts with P-Splines

Description Usage Arguments Details Value Author(s) See Also

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.

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

`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.

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

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.