variogram: Empirical variogram for longitudinal data

Description Usage Arguments Details Value Note Author(s) Examples

View source: R/variogram.R

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

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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

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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.