# variogram: Empirical variogram for longitudinal data In graemeleehickey/joineR: Joint Modelling of Repeated Measurements and Time-to-Event Data

## Description

Calculates the variogram for observed measurements, with two components, the total variability in the data, and the variogram for all time lags in all individuals.

## Usage

 `1` ```variogram(indv, time, Y) ```

## Arguments

 `indv` vector of individual identification, as in the longitudinal data, repeated for each time point. `time` vector of observation time, as in the longitudinal data. `Y` vector of observed measurements. This can be a vector of longitudinal data, or residuals after fitting a model for the mean response.

## Details

The empirical variogram in this function is calculated from observed half-squared-differences between pairs of measurements, v_ijk = 0.5 * (r_ij-r_ik)^2 and the corresponding time differences u_ijk=t_ij-t_ik. The variogram is plotted for averages of each time lag for the v_ijk for all i.

## Value

An object of class `vargm` and `list` with two elements. The first `svar` is a matrix with columns for all values (u_ijk,v_ijk), and the second `sigma2` is the total variability in the data.

## Note

There is a function `plot.vargm` which should be used to plot the empirical variogram.

## Author(s)

Ines Sousa ([email protected])

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```data(mental) mental.unbalanced <- to.unbalanced(mental, id.col = 1, times = c(0, 1, 2, 4, 6, 8), Y.col = 2:7, other.col = c(8, 10, 11)) names(mental.unbalanced)[3] <- "Y" vgm <- variogram(indv = tail(mental.unbalanced[, 1], 30), time = tail(mental.unbalanced[, 2], 30), Y = tail(mental.unbalanced[, 3], 30)) ```

graemeleehickey/joineR documentation built on May 31, 2018, 7:03 a.m.