variogram | R Documentation |

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.

variogram(indv, time, Y)

`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. |

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*.

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.

There is a function `plot.vargm`

which should be used to
plot the empirical variogram.

Ines Sousa

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))

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