Package 'tpr' reference manual

Title:	Temporal Process Regression
Description:	Regression models for temporal process responses with time-varying coefficient.
Authors:	Jun Yan <[email protected]>
Maintainer:	Jun Yan <[email protected]>
License:	GPL (>= 3)
Version:	0.3-3
Built:	2025-03-11 03:14:17 UTC
Source:	https://github.com/cran/tpr

Temporal Process Regression

Description

Fit regression models for temporal process responses with time-varying and time-independent coefficients.

Details

An overview of how to use the package, including the most important functions

Author(s)

Jun Yan <[email protected]>

References

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

rhDNase Data

Description

Randomized trial of rhDNase for treatment of cystic fibrosis

Usage

data(dnase)data(dnase)

Format

A data frame with 767 observations on the following 6 variables.

id: subject id
rx: treatment arm: 0 = placebo, 1 = rhDNase
fev: forced expirotary volume, a measure of lung capacity
futime: follow time
iv1: IV start time
iv2: IV stop time

Details

During an exacerbation, patients received intravenous (IV) antibiotics and were considered unsusceptible until seven exacerbation-free days beyond the end of IV therapy.

A few subjects were infected at the time of enrollment, for instance a subject has a first infection interval of -21 to 7. We do not count this first infection as an "event", and the subject first enters the risk set at day 7.

Source

Therneau and Grambsch (2000). Modeling Survival Data: Extending the Cox model. Springer. http://www.mayo.edu/hsr/people/therneau/book/data/dnase.html

References

Yan and Fine (2008). Analysis of Episodic Data with Application to Recurrent Pulmonary Exacerbations in Cystic Fibrosis Patients. JASA.

Examples

## This example steps through how to set up for the tpr function.
## Three objects are needed:
##     1) response process (an object of "lgtdl")
##     2) data availability process (an object of "lgtdl")
##     3) a time-independent covariate matrix

data(dnase)

## extracting the unique id and subject level information
dat <- unique(dnase[,c("id", "futime", "fev", "rx")])

## construct temporal process response for recurrent enent
rec <- lapply(split(dnase[,c("id", "iv1", "futime")], dnase$id),
              function(x) {
                v <- x$iv1
                maxfu <- max(x$futime)
                ## iv1 may be negative!!!
                if (is.na(v[1])) c(0, maxfu + 1.0)
                else if (v[1] < 0) c(v[1] - 1, v[!is.na(v)], maxfu + 1.0)
                else c(0, v[!is.na(v)], maxfu + 1.0)
              })

yrec <- lapply(rec,
               function(x) {
                 dat <- data.frame(time=x, cov=1:length(x)-1)
                 len <- length(x)
                 dat$cov[len] <- dat$cov[len - 1]
                 as.lgtdl(dat)
               })

## construct temporal process response for accumulative days exacerbation
do1.acc <- function(x) {
  gap <- x$iv2 - x$iv1 + 1
  if (all(is.na(gap))) yy <- tt <- NULL
  else {
    gap <- na.omit(gap)
    yy <- cumsum(rep(1, sum(gap)))
    tt <- unlist(sapply(1:length(gap), function(i)
                        seq(x$iv1[i], x$iv2[i], by=1.0)))
  }
  yy <- c(0, yy, rev(yy)[1])
  if (!is.null(tt[1]) && tt[1] < 0)
    tt <- c(tt[1] - 1, tt, max(x$futime) + 1.0)
  else tt <- c(0, tt, max(x$futime) + 1.0)
  as.lgtdl(data.frame(time=tt, cov=yy))
}

yacc <- lapply(split(dnase[,c("id", "iv1", "iv2", "futime")], dnase$id),
              do1.acc)

## construct data availability (or at risk) indicator process
tu <- max(dat$futime) + 0.001
rt <- lapply(1:nrow(dat),
             function(i) {
               x <- dat[i, "futime"]
               time <- c(0, x, tu)
               cov <- c(1, 0, 0)
               as.lgtdl(data.frame(time=time, cov=cov))
             })

## time-independent covariate matrix
xmat <- model.matrix(~ rx + fev, data=dat)
## time-window in days
tlim <- c(10, 168)
good <- unlist(lapply(yrec, function(x) x$time[1] == 0))


## fully functional temporal process regression

## for recurrent event
m.rec <- tpr(yrec, rt, xmat[,1:3], list(), xmat[,-(1:3),drop=FALSE], list(),
             tis=10:160, w = rep(1, 151), family = poisson(),
             evstr = list(link = 5, v = 3))
par(mfrow=c(1,3), mgp=c(2,1,0), mar=c(4,2,1,0), oma=c(0,2,0,0))
for(i in 1:3) ci.plot(m.rec$tis, m.rec$alpha[,i], sqrt(m.rec$valpha[,i]))
## hypothesis test of significance
## integral test, covariate index 2 and 3
sig.test.int.ff(m.rec, idx=2:3, ncut=2)
sig.test.boots.ff(m.rec, idx=2:3, nsim=1000)
## constant fit
cfit <- cst.fit.ff(m.rec, idx=2:3)

## goodness-of-fit test for constant fit
gof.test.int.ff(m.rec, idx=2:3, ncut=2)
gof.test.boots.ff(m.rec, idx=2:3, nsim=1000)




## for cumulative days in exacerbation
m.acc <- tpr(yacc, rt, xmat[,1:3], list(), xmat[,-(1:3),drop=FALSE], list(),
             tis=10:160, w = rep(1, 151), family = gaussian(),
             evstr = list(link = 1, v = 1))
par(mfrow=c(1,3), mgp=c(2,1,0), mar=c(4,2,1,0), oma=c(0,2,0,0))
for(i in 1:3) ci.plot(m.acc$tis, m.acc$alpha[,i], sqrt(m.acc$valpha[,i]))
## This example steps through how to set up for the tpr function.
## Three objects are needed:
##     1) response process (an object of "lgtdl")
##     2) data availability process (an object of "lgtdl")
##     3) a time-independent covariate matrix

data(dnase)

## extracting the unique id and subject level information
dat <- unique(dnase[,c("id", "futime", "fev", "rx")])

## construct temporal process response for recurrent enent
rec <- lapply(split(dnase[,c("id", "iv1", "futime")], dnase$id),
              function(x) {
                v <- x$iv1
                maxfu <- max(x$futime)
                ## iv1 may be negative!!!
                if (is.na(v[1])) c(0, maxfu + 1.0)
                else if (v[1] < 0) c(v[1] - 1, v[!is.na(v)], maxfu + 1.0)
                else c(0, v[!is.na(v)], maxfu + 1.0)
              })

yrec <- lapply(rec,
               function(x) {
                 dat <- data.frame(time=x, cov=1:length(x)-1)
                 len <- length(x)
                 dat$cov[len] <- dat$cov[len - 1]
                 as.lgtdl(dat)
               })

## construct temporal process response for accumulative days exacerbation
do1.acc <- function(x) {
  gap <- x$iv2 - x$iv1 + 1
  if (all(is.na(gap))) yy <- tt <- NULL
  else {
    gap <- na.omit(gap)
    yy <- cumsum(rep(1, sum(gap)))
    tt <- unlist(sapply(1:length(gap), function(i)
                        seq(x$iv1[i], x$iv2[i], by=1.0)))
  }
  yy <- c(0, yy, rev(yy)[1])
  if (!is.null(tt[1]) && tt[1] < 0)
    tt <- c(tt[1] - 1, tt, max(x$futime) + 1.0)
  else tt <- c(0, tt, max(x$futime) + 1.0)
  as.lgtdl(data.frame(time=tt, cov=yy))
}

yacc <- lapply(split(dnase[,c("id", "iv1", "iv2", "futime")], dnase$id),
              do1.acc)

## construct data availability (or at risk) indicator process
tu <- max(dat$futime) + 0.001
rt <- lapply(1:nrow(dat),
             function(i) {
               x <- dat[i, "futime"]
               time <- c(0, x, tu)
               cov <- c(1, 0, 0)
               as.lgtdl(data.frame(time=time, cov=cov))
             })

## time-independent covariate matrix
xmat <- model.matrix(~ rx + fev, data=dat)
## time-window in days
tlim <- c(10, 168)
good <- unlist(lapply(yrec, function(x) x$time[1] == 0))


## fully functional temporal process regression

## for recurrent event
m.rec <- tpr(yrec, rt, xmat[,1:3], list(), xmat[,-(1:3),drop=FALSE], list(),
             tis=10:160, w = rep(1, 151), family = poisson(),
             evstr = list(link = 5, v = 3))
par(mfrow=c(1,3), mgp=c(2,1,0), mar=c(4,2,1,0), oma=c(0,2,0,0))
for(i in 1:3) ci.plot(m.rec$tis, m.rec$alpha[,i], sqrt(m.rec$valpha[,i]))
## hypothesis test of significance
## integral test, covariate index 2 and 3
sig.test.int.ff(m.rec, idx=2:3, ncut=2)
sig.test.boots.ff(m.rec, idx=2:3, nsim=1000)
## constant fit
cfit <- cst.fit.ff(m.rec, idx=2:3)

## goodness-of-fit test for constant fit
gof.test.int.ff(m.rec, idx=2:3, ncut=2)
gof.test.boots.ff(m.rec, idx=2:3, nsim=1000)




## for cumulative days in exacerbation
m.acc <- tpr(yacc, rt, xmat[,1:3], list(), xmat[,-(1:3),drop=FALSE], list(),
             tis=10:160, w = rep(1, 151), family = gaussian(),
             evstr = list(link = 1, v = 1))
par(mfrow=c(1,3), mgp=c(2,1,0), mar=c(4,2,1,0), oma=c(0,2,0,0))
for(i in 1:3) ci.plot(m.acc$tis, m.acc$alpha[,i], sqrt(m.acc$valpha[,i]))

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))
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 <[email protected]>

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.

Constant fit of coefficients in a TPR model

Description

Weighted least square estimate of a constant model for time-varying coefficients in a TPR model.

Usage

cst.fit.ff(fit, idx)
cst.fit.ff(fit, idx)

Arguments

`fit`	a fitted object from `tpr`
`idx`	the index of the

Value

The estimated constant fit, standard error, z-value and p-value.

Author(s)

Jun Yan [email protected]

References

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

Significance and Goodness-of-fit Test of TPR

Description

Two kinds of tests are provided for inference on the coefficients in a fully functional TRP model: integral test and bootstrap test.

Usage

sig.test.int.ff(fit, chypo = 0, idx, weight = TRUE, ncut = 2)
sig.test.boots.ff(fit, chypo = 0, idx, nsim = 1000, plot = FALSE)
gof.test.int.ff(fit, cfitList = NULL, idx, weight = TRUE, ncut = 2)
gof.test.boots.ff(fit, cfitList = NULL, idx, nsim = 1000, plot = FALSE)
gof.test.boots.pf(fit1, fit2, nsim, p = NULL, q = 1)
sig.test.int.ff(fit, chypo = 0, idx, weight = TRUE, ncut = 2)
sig.test.boots.ff(fit, chypo = 0, idx, nsim = 1000, plot = FALSE)
gof.test.int.ff(fit, cfitList = NULL, idx, weight = TRUE, ncut = 2)
gof.test.boots.ff(fit, cfitList = NULL, idx, nsim = 1000, plot = FALSE)
gof.test.boots.pf(fit1, fit2, nsim, p = NULL, q = 1)

Arguments

`fit`	a fitted object from `tpr`
`chypo`	hypothesized value of coefficients
`idx`	the index of the coefficients to be tested
`weight`	whether or not use inverse variation weight
`ncut`	the number of cuts of the interval of interest in integral test
`cfitList`	a list of fitted object from `cst.fit.ff`
`nsim`	the number of bootstrap samples in bootstrap test
`plot`	whether or not plot
`fit1`	fit of H0 model (reduced)
`fit2`	fit of H1 model (full)
`p`	the index of the time-varying estimation in fit2
`q`	the index of the time-independent estimation in fit1

Value

Test statistics and their p-values.

Author(s)

Jun Yan [email protected]

References

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

Examples

## see ?tpr
## see ?tpr

`x`	the x coordinate
`y`	the y coordinate
`se`	the standard error of y
`level`	confidence level
`ylim`	the range of y axis
`newplot`	if TRUE, draw a new plot
`fun`	a transform function
`dfun`	the derivative of the tranform function
`...`	arguments to be passed to plot

Package 'tpr'

Help Index

Temporal Process Regression

Description

Details

Author(s)

References

Confidence Interval Plot

Description

Usage

Arguments

Author(s)

rhDNase Data

Description

Usage

Format

Details

Source

References

Examples

Temporal Process Regression

Description

Usage

Arguments

Details

Value

Author(s)

References

Constant fit of coefficients in a TPR model

Description

Usage

Arguments

Value

Author(s)

References

See Also

Significance and Goodness-of-fit Test of TPR

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples