tpr {tpr}R Documentation

Temporal Process Regression

Description

Regression for temporal process responses and time-independent covariate. Some covariates have time-varying coefficients while others have time-independent coefficients.

Usage

tpr(y, delta, x, xtv=list(), z, ztv=list(), w, tis,
    family = poisson(),
    evstr = list(link = 5, v = 3),
    alpha = NULL, theta = NULL,
    tidx = 1:length(tis),
    kernstr = list(kern=1, poly=1, band=range(tis)/50),
    control = list(maxit=25, tol=0.0001, smooth=0, intsmooth=0))

Arguments

y Response, a list of "lgtdl" objects.
delta Data availability indicator, a list of "lgtdl" objects.
x Covariate matrix for time-varying coefficients.
xtv A list of list of "lgtdl" for time-varying covariates with time-varying coefficients.
z NOT READY YET; Covariate matrix for time-independent coefficients.
ztv NOT READY YET; A list of list of "lgtdl" for time-varying covariates with time-independent coefficients.
w Weight vector with the same length of tis.
tis A vector of time points at which the model is to be fitted.
family Specification of the response distribution; see family for glm; this argument is used in getting initial estimates.
evstr A list of two named components, link function and variance function. link: 1 = identity, 2 = logit, 3 = probit, 4 = cloglog, 5 = log; v: 1 = gaussian, 2 = binomial, 3 = poisson
alpha A matrix supplying initial values of alpha.
theta A numeric vector supplying initial values of theta.
tidx indices for time points used to get initial values.
kernstr A list of two names components: kern: 1 = Epanechnikov, 2 = triangular, 0 = uniform; band: bandwidth
control A list of named components: maxit: maximum number of iterations; tol: tolerance level of iterations. smooth: 1 = smoothing; 0 = no smoothing.

Details

This rapper function can be made more user-friendly in the future. For example, evstr can be determined from the family argument.

Value

An object of class "tpr":

tis same as the input argument
alpha estimate of time-varying coefficients
beta estimate of time-independent coefficients
valpha a matrix of variance of alpha at tis
vbeta a matrix of variance of beta at tis
niter the number of iterations used
infAlpha a list of influence functions for alpha
infBeta a matrix of influence functions for beta

Author(s)

Jun Yan <jyan@stat.uiowa.edu>

References

Fine, Yan, and Kosorok (2004). Temporal Process Regression. Biometrika.

Yan and Huang (2006). Partly Functional Temporal Process Regression with Semiparametric Profile Estimating Functions.


[Package tpr version 0.2-4 Index]