Description Usage Arguments Value Author(s) References Examples
Calculates empirical variogram for data sets with regularly or irregularly spaced time points, and plots the result
1 2 |
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 |
irregular |
a character string, |
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.
Ozgur Asar, Peter J. Diggle
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.
1 2 3 4 5 6 7 8 9 10 | # 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)
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