R: Regression Analysis for Time-Invariant Coefficients Using...
asynchHK
R Documentation
Regression Analysis for Time-Invariant Coefficients Using Half-Kernel Estimation
Description
Estimation of regression models for sparse asynchronous longitudinal
observations using a half-kernel estimation approach with time-invariant coefficients.
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 or a
numeric vector containing the bandwidths for which parameter
estimates are to be obtained.
If NULL, an optimal bandwidth will be determined
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.
See original reference for details of the
selection procedure.
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().
Value
A list is returned. If bandwidths are provided, each element
of the list is a matrix,
where the ith row corresponds to the ith bandwidth of argument “bw" and the
columns correspond to the model parameters. If the bandwidth is
determined automatically, each element is a named vector calculated
at the optimal bandwidth.
betaHat
The estimated model coefficients.
stdErr
The standard error for each coefficient.
zValue
The estimated z-value for each coefficient.
pValue
The p-value 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, Jialiang Li, Jason P. Fine, and Shannon T. Holloway
References
Cao, H., Li, Jialiang, and Fine, J. P. (2015).
On last observation carried forward and asynchronous longitudinal regression analysis.
Electronic Journal of Statistics, submitted.
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
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> 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/asynchHK.Rd_%03d_medium.png", width=480, height=480)
> ### Name: asynchHK
> ### Title: Regression Analysis for Time-Invariant Coefficients Using
> ### Half-Kernel Estimation
> ### Aliases: asynchHK
>
> ### ** Examples
>
>
> data(asynchDataTI)
>
> res <- asynchHK(data.x = TI.x,
+ data.y = TI.y,
+ bw = c(0.05, 0.03),
+ kType = "epan",
+ lType = "identity")
Bandwidth: 0.05
estimate stdErr z-value p-value
beta0 0.5188446 0.07409504 7.00242 2.515789e-12
beta1 1.4067066 0.07634784 18.42497 8.283847e-76
Bandwidth: 0.03
estimate stdErr z-value p-value
beta0 0.5030785 0.08175940 6.153158 7.595515e-10
beta1 1.4283454 0.08645142 16.521942 2.550498e-61
>
>
>
>
>
>
> dev.off()
null device
1
>