Description Usage Arguments Details Value Note Author(s) Examples

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.

1 |

`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 ([email protected])

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.

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