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Last data update: 2014.03.03

R: Regression Analysis Using Last Value Carried Forward

asynchLV

R Documentation

Regression Analysis Using Last Value Carried Forward

Description

Estimation of regression models for sparse asynchronous longitudinal observations using the last value carried forward approach.

Usage

       asynchLV(data.x, data.y, lType = "identity", ...)

Arguments

`data.x`	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 integer without loss of information. Missing values must be indicated as NA. All times will automatically be rescaled to [0,1].
`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"}.
`...`	Ignored.

Details

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

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

Value

A list is returned, the elements of which are named vectors:

`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.

Author(s)

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

References

Cao, H., Zeng, D., and Fine, J. P. (2015) Regression Analysis of sparse asynchronous longitudinal data. Journal of the Royal Statistical Society: Series B, 77, 755-776.

Examples


  data(asynchDataTI)

  res <- asynchLV(data.x = TI.x, 
                  data.y = TI.y,
                  lType = "identity")

Results


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/asynchLV.Rd_%03d_medium.png", width=480, height=480)
> ### Name: asynchLV
> ### Title: Regression Analysis Using Last Value Carried Forward
> ### Aliases: asynchLV
> 
> ### ** Examples
> 
> 
>   data(asynchDataTI)
> 
>   res <- asynchLV(data.x = TI.x, 
+                   data.y = TI.y,
+                   lType = "identity")
       estimate     stdErr   z-value       p-value
beta0 0.5489211 0.05926819  9.261647  2.013024e-20
beta1 1.2766146 0.05475263 23.316041 3.048218e-120

>   
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>