backfit {KernGPLM}R Documentation

Backfitting for an additive model

Description

Implements kernel-based backfitting in an additive model, optional with a partial linear term.

Usage

backfit(t, y, h, x = NULL, grid = NULL, weights.conv = 1,
           offset = 0, method = "generic",
           max.iter = 50, eps.conv = 1e-04, m.start = NULL,
           kernel.p = 2, kernel.q = 2)

Arguments

y n x 1 vector, responses
t n x q matrix, data for nonparametric part
h scalar or 1 x q, bandwidth(s)
x optional, n x p matrix, data for linear part
grid m x q matrix, where to calculate the nonparametric function (default = t)
weights.conv weights for convergence criterion
offset offset
method one of "generic", "linit" or "modified"
max.iter maximal number of iterations
eps.conv convergence criterion
m.start n x q matrix, start values for m
kernel.p integer or text, see kernel.function
kernel.q integer, see kernel.function

Value

List with components:

c constant
b p x 1 vector, linear coefficients
m n x q matrix, nonparametric marginal function estimates
m.grid m x q matrix, nonparametric marginal function estimates on grid
rss residual sum of squares

Author(s)

Marlene Mueller

See Also

kernel.function, kreg

[Package KernGPLM version 0.65 Index]