tpr | R Documentation |
Regression for temporal process responses and time-independent covariate. Some covariates have time-varying coefficients while others have time-independent coefficients.
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))
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 |
A vector of time points at which the model is to be fitted. |
family |
Specification of the response distribution; see
|
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. |
This rapper function can be made more user-friendly in the future. For
example, evstr
can be determined from the family
argument.
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 |
Jun Yan <jun.yan@uconn.edu>
Fine, Yan, and Kosorok (2004). Temporal Process Regression. Biometrika.
Yan and Huang (2009). Partly Functional Temporal Process Regression with Semiparametric Profile Estimating Functions. Biometrics.
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