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Last data update: 2014.03.03

R: A function for calculating the empirical variogram for data...

variogram

R Documentation

A function for calculating the empirical variogram for data sets with regularly or irregularly spaced follow-up time points

Description

Calculates empirical variogram for data sets with regularly or irregularly spaced time points, and plots the result

Usage

variogram(resid, timeVar, id, binwidth, numElems = 0, inc.var = TRUE, 
irregular = TRUE)

Arguments

`resid`	a vector of empirical residuals
`timeVar`	a vector for the time variable
`id`	a vector for subject identification
`binwidth`	a numerical value for the bin length, to be used for irregularly spaced data
`numElems`	a numerical value for the elimination of the bins with less than that number of elements
`inc.var`	a character string, if set `FALSE` process variance is not included in the plot
`irregular`	a character string, `FALSE` indicates the data are collected at regular time points

Value

Returns mid values and means of the bins, and numbers of elements falling into the bins for irregular = TRUE, and lags and means of the lags, and number of the elements for the lags for irregular = FALSE. Process variance is also returned.

Author(s)

Ozgur Asar, Peter J. Diggle

References

Asar O, Ritchie J, Kalra P, Diggle PJ (2015) Acute kidney injury amongst chronic kidney disease patients: a case-study in statistical modelling. To be submitted.

Diggle PJ (1988) An approach to the analysis of repeated measurements. Biometrics, 44, 959-971.

Diggle PJ, Heagerty PJ, Liang K-Y, Zeger SL. (2002) Analysis of Longitudinal Data, 2nd edition. Oxford University Press: Oxford.

Diggle PJ, Ribeiro PJ Jr. (2007) Model-based Geostatistics. Springer-Verlag: New York.

Diggle PJ, Sousa I, Asar O (2015) Real time monitoring of progression towards renal failure in primary care patients. Biostatistics, 16(3), 522-536.

Examples

# loading the data set and subsetting it for the first 20 patients 
# for the sake illustration of the usage of the functions
data(data.sim.ibm)
data.sim.ibm.short <- data.sim.ibm[data.sim.ibm$id <= 20, ]

# obtaining empirical residuals by a linear model
# and calculating the empirical variogram
lm.fit <- lm(log.egfr ~ sex + bage + fu + pwl, data = data.sim.ibm.short)
variogram(resid = resid(lm.fit), timeVar = data.sim.ibm.short$fu, id = data.sim.ibm.short$id,
  binwidth = 0.1, numElems = 20, inc.var = FALSE)