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.
ACF(object, maxLag, resType, form, na.action)
gls
, representing
a generalized least squares fitted model.
"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"
.
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
s. The default action (
na.fail
) causes
ACF.gls
to print an error message and terminate if there are any
incomplete observations.
lag
and
ACF
representing,
respectively, the lag between residuals within a pair and the corresponding
empirical autocorrelation. The returned value inherits from class
ACF
.
Box, G.E.P., Jenkins, G.M., and Reinsel G.C. (1994) "Time Series Analysis: Forecasting and Control", 3rd Edition, Holden-Day.
fm1 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary) ACF(fm1, form = ~ 1 | Mare)