| ACF.lme {nlme} | R Documentation |
This method function calculates the empirical autocorrelation function
for the within-group residuals from an lme fit. The
autocorrelation values are calculated using pairs of residuals within
the innermost group level. The autocorrelation function is useful for
investigating serial correlation models for equally spaced data.
## S3 method for class 'lme': ACF(object, maxLag, resType, ...)
object |
an object inheriting from class lme, representing
a fitted linear mixed-effects model. |
maxLag |
an optional integer giving the maximum lag for which the autocorrelation should be calculated. Defaults to maximum lag in the within-group residuals. |
resType |
an optional character string specifying the type of
residuals to be used. If "response", the "raw" residuals
(observed - fitted) are used; else, if "pearson", the
standardized residuals (raw residuals divided by the corresponding
standard errors) are used; else, if "normalized", the
normalized residuals (standardized residuals pre-multiplied by the
inverse square-root factor of the estimated error correlation
matrix) are used. Partial matching of arguments is used, so only the
first character needs to be provided. Defaults to "pearson". |
... |
some methods for this generic require additional arguments – not used. |
a data frame with columns lag and ACF representing,
respectively, the lag between residuals within a pair and the corresponding
empirical autocorrelation. The returned value inherits from class
ACF.
Jose Pinheiro Jose.Pinheiro@pharma.novartis.com and Douglas Bates bates@stat.wisc.edu
Box, G.E.P., Jenkins, G.M., and Reinsel G.C. (1994) "Time Series Analysis: Forecasting and Control", 3rd Edition, Holden-Day.
Pinheiro, J.C., and Bates, D.M. (2000) "Mixed-Effects Models in S and S-PLUS", Springer.
fm1 <- lme(follicles ~ sin(2*pi*Time) + cos(2*pi*Time),
Ovary, random = ~ sin(2*pi*Time) | Mare)
ACF(fm1, maxLag = 11)
# Pinheiro and Bates, p240-241
fm1Over.lme <- lme(follicles ~ sin(2*pi*Time) +
cos(2*pi*Time), data=Ovary,
random=pdDiag(~sin(2*pi*Time)) )
(ACF.fm1Over <- ACF(fm1Over.lme, maxLag=10))
plot(ACF.fm1Over, alpha=0.01)