asynchLV | R Documentation |

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

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

`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. |

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().

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. |

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

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.

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

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