tpr: Temporal Process Regression

In tpr: Temporal Process Regression

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.

tpr documentation built on Oct. 17, 2022, 9:07 a.m.