A data.frame of covariates.
The structure of the data.frame must be
{patient ID, time of measurement, measurement(s)}.
Patient IDs must be of class integer or be able to be coerced to
class integer without loss of information.
Missing values must be indicated as NA.
All times will automatically be rescaled to [0,1].

data.y

A data.frame of response measurements.
The structure of the data.frame must be
{patient ID, time of measurement, measurement}.
Patient IDs must be of class integer or be able to be coerced to
class integer without loss of information.
Missing values must be indicated as NA.
All times will automatically be rescaled to [0,1].

kType

An object of class character indicating the type of
smoothing kernel to use in the estimating equation.
Must be one of {"epan", "uniform", "gauss"}, where
"epan" is the Epanechnikov kernel and "gauss" is the
Gaussian kernel.

lType

An object of class character indicating the type of link
function to use for the regression model.
Must be one of {"identity","log","logistic"}.

bw

If provided, bw is an object of class numeric
containing a single bandwidth at which parameter
estimates are to be obtained.
If NULL, an “optimal" bandwidth will be determined
for each time point using an adaptive selection procedure.
The range of the bandwidth search space is taken
to be 2*(Q3 - Q1)*n^-0.7 to 2*(Q3 - Q1)*n^-0.3,
where Q3 is the 0.75 quantile and Q1 is the 0.25 quantile
of the pooled sample of measurement times for the covariate
and response, and n is the number of patients.
For each time point, the optimal bandwidth(s) is taken to be
that which minimizes the mean squared error.
See original reference for details of the
selection procedure.

times

A vector object of class numeric. The time points at which
the coefficients are to be estimated.

nCores

A numeric object. For auto-tune method, the number of cores
to employ for calculation. If nCores > 1, the bandwidth
search space will be distributed across the cores
using parallel's parLapply.

...

Ignored.

Details

For lType = "log" and lType = "logistic", parameter estimates are
obtained by minimizing the estimating equation
using optim() with method="Nelder-Mead";
all other arguments take their default values.

For lType = "identity", parameter estimates are obtained using solve().

Upon completion, a single plot is generating showing the
time-dependence of each coefficient.

Value

A list is returned. Each element of the list is a matrix, where
the ith row corresponds to the ith time point of input argument “times"
and the columns correspond to the model parameters.

The returned values are estimated using either the
provided bandwidth or the “optimal" bandwidth as determined
using the adaptive selection procedure.

betaHat

The estimated model coefficients.

stdErr

The standard errors for each coefficient.

zValue

The estimated z-values for each coefficient.

pValue

The p-values for each coefficient.

If the bandwidth is determined automatically, two additional list
elements are returned:

optBW

The estimated optimal bandwidth for each coefficient.

minMSE

The mean squared error at the optimal bandwidth for
each coefficient.

Author(s)

Hongyuan Cao, Donglin Zeng, Jason P. Fine, and Shannon T. Holloway

References

Cao, H., Zeng, D., and Fine, J. P. (2014)
Regression Analysis of sparse asynchronous longitudinal data.
Journal of the Royal Statistical Society: Series B, 77, 755-776.

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(AsynchLong)
Loading required package: compiler
Loading required package: parallel
AsynchLong was developed in support of IMPACT, a comprehensive research
program that aims to improve the health and longevity of people by
improving the clinical trial process. To learn more about our
research and available software visit www.impact.unc.edu.
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/AsynchLong/asynchTD.Rd_%03d_medium.png", width=480, height=480)
> ### Name: asynchTD
> ### Title: Regression Analysis for Time-Dependent Coefficients
> ### Aliases: asynchTD
>
> ### ** Examples
>
>
> data(asynchDataTD)
>
> res <- asynchTD(data.x = TD.x,
+ data.y = TD.y,
+ times = c(0.25, 0.50, 0.75),
+ bw = 0.05,
+ kType = "epan",
+ lType = "identity")
Time point: 0.25
Bandwidth: 0.05
estimate stdErr z-value p-value
beta0 0.6545948 0.1047513 6.249040 4.129827e-10
beta1 0.5216886 0.1091160 4.781047 1.743847e-06
Time point: 0.5
Bandwidth: 0.05
estimate stdErr z-value p-value
beta0 0.4595937 0.09531513 4.821833 1.422449e-06
beta1 0.7081564 0.07747553 9.140388 6.222894e-20
Time point: 0.75
Bandwidth: 0.05
estimate stdErr z-value p-value
beta0 0.3876471 0.13696196 2.830326 4.650054e-03
beta1 0.9602452 0.08867424 10.828908 2.511314e-27
>
>
>
>
>
>
>
> dev.off()
null device
1
>