| corCAR1 {nlme} | R Documentation |
This function is a constructor for the corCAR1 class,
representing an autocorrelation structure of order 1, with a
continuous time covariate. Objects created using this constructor must
be later initialized using the appropriate Initialize
method.
corCAR1(value, form, fixed)
value |
the correlation between two observations one unit of time apart. Must be between 0 and 1. Defaults to 0.2. |
form |
a one sided formula of the form ~ t, or ~ t |
g, specifying a time covariate t and, optionally, a
grouping factor g. Covariates for this correlation structure
need not be integer valued. When a grouping factor is present in
form, the correlation structure is assumed to apply only
to observations within the same grouping level; observations with
different grouping levels are assumed to be uncorrelated. Defaults to
~ 1, which corresponds to using the order of the observations
in the data as a covariate, and no groups. |
fixed |
an optional logical value indicating whether the
coefficients should be allowed to vary in the optimization, or kept
fixed at their initial value. Defaults to FALSE, in which case
the coefficients are allowed to vary. |
an object of class corCAR1, representing an autocorrelation
structure of order 1, with a continuous time covariate.
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.
Jones, R.H. (1993) "Longitudinal Data with Serial Correlation: A State-space Approach", Chapman and Hall.
Pinheiro, J.C., and Bates, D.M. (2000) "Mixed-Effects Models in S and S-PLUS", Springer, esp. pp. 236, 243.
corClasses,
Initialize.corStruct,
summary.corStruct
## covariate is Time and grouping factor is Mare
cs1 <- corCAR1(0.2, form = ~ Time | Mare)
# Pinheiro and Bates, pp. 240, 243
fm1Ovar.lme <- lme(follicles ~
sin(2*pi*Time) + cos(2*pi*Time),
data = Ovary, random = pdDiag(~sin(2*pi*Time)))
fm4Ovar.lme <- update(fm1Ovar.lme,
correlation = corCAR1(form = ~Time))