AsynchLong (version 2.3)

asynchLV: 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", verbose = TRUE, ...)

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.

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"}.

verbose

An object of class logical. TRUE results in screen prints.

...

Ignored.

Author

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

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().

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")