tpr {tpr}

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 <[e-mail]>

References

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

Yan and Huang (2009). Partly Functional Temporal Process Regression with Semiparametric Profile Estimating Functions. Biometrics.