| ACF.gls {nlme} | R Documentation |
This method function calculates the empirical autocorrelation function
for the residuals from a gls fit. If a grouping variable is
specified in form, the autocorrelation values
are calculated using pairs of residuals within the same group;
otherwise all possible residual pairs are used. The autocorrelation
function is useful for investigating serial correlation models for
equally spaced data.
## S3 method for class 'gls': ACF(object, maxLag, resType, form, na.action, ...)
object |
an object inheriting from class gls, representing
a generalized least squares fitted model. |
maxLag |
an optional integer giving the maximum lag for which the autocorrelation should be calculated. Defaults to maximum lag in the 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". |
form |
an optional one sided formula of the form ~ t, or
~ t | g, specifying a time covariate t and, optionally, a
grouping factor g. The time covariate must be integer
valued. When a grouping factor is present in
form, the autocorrelations are calculated using residual pairs
within the same group. Defaults to ~ 1, which corresponds to
using the order of the observations in the data as a covariate, and
no groups. |
na.action |
a function that indicates what should happen when the
data contain NAs. The default action (na.fail) causes
ACF.gls to print an error message and terminate if there are any
incomplete observations. |
... |
some methods for this generic require additional arguments. |
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 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary)
ACF(fm1, form = ~ 1 | Mare)
# Pinheiro and Bates, p. 255-257
fm1Dial.gls <- gls(rate ~
(pressure+I(pressure^2)+I(pressure^3)+I(pressure^4))*QB,
Dialyzer)
fm2Dial.gls <- update(fm1Dial.gls,
weights = varPower(form = ~ pressure))
ACF(fm2Dial.gls, form = ~ 1 | Subject)