| glsD {Design} | R Documentation |
This function fits a linear model using generalized least
squares. The errors are allowed to be correlated and/or have unequal
variances. glsD is a slightly enhanced version of the
Pinheiro and Bates glsD function in the nlme package to
make it easy to use with the Design library and to implement cluster
bootstrapping (primarily for nonparametric estimates of the
variance-covariance matrix of the parameter estimates and for
nonparametric confidence limits of correlation parameters).
glsD(model, data, correlation, weights, subset, method, na.action,
control, verbose, B=0, dupCluster=FALSE, pr=FALSE,
opmeth=c('optimize','optim'))
## S3 method for class 'glsD':
print(x, digits=4, ...)
model |
a two-sided linear formula object describing the
model, with the response on the left of a ~ operator and the
terms, separated by + operators, on the right. |
data |
an optional data frame containing the variables named in
model, correlation, weights, and
subset. By default the variables are taken from the
environment from which gls is called. |
correlation |
an optional corStruct object describing the
within-group correlation structure. See the documentation of
corClasses for a description of the available corStruct
classes. If a grouping variable is to be used, it must be specified in
the form argument to the corStruct
constructor. Defaults to NULL, corresponding to uncorrelated
errors. |
weights |
an optional varFunc object or one-sided formula
describing the within-group heteroscedasticity structure. If given as
a formula, it is used as the argument to varFixed,
corresponding to fixed variance weights. See the documentation on
varClasses for a description of the available varFunc
classes. Defaults to NULL, corresponding to homoscesdatic
errors. |
subset |
an optional expression indicating which subset of the rows of
data should be used in the fit. This can be a logical
vector, or a numeric vector indicating which observation numbers are
to be included, or a character vector of the row names to be
included. All observations are included by default. |
method |
a character string. If "REML" the model is fit by
maximizing the restricted log-likelihood. If "ML" the
log-likelihood is maximized. Defaults to "REML". |
na.action |
a function that indicates what should happen when the
data contain NAs. The default action (na.fail) causes
gls to print an error message and terminate if there are any
incomplete observations. |
control |
a list of control values for the estimation algorithm to
replace the default values returned by the function glsControl.
Defaults to an empty list. |
verbose |
an optional logical value. If TRUE information on
the evolution of the iterative algorithm is printed. Default is
FALSE. |
B |
number of bootstrap resamples to fit and store, default is none |
dupCluster |
set to TRUE to have glsD when
bootstrapping to consider multiply-sampled clusters as if they were
one large cluster when fitting using the gls algorithm |
pr |
set to TRUE to show progress of bootstrap resampling |
opmeth |
specifies whether the optimize or the optim
function is to be used for optimization |
x |
the result of glsD |
digits |
number of significant digits to print |
... |
ignored |
an object of classes glsD, Design, and gls
representing the linear model
fit. Generic functions such as print, plot, and
summary have methods to show the results of the fit. See
glsObject for the components of the fit. The functions
resid, coef, and fitted can be used to extract
some of its components. glsD returns the following components
not returned by gls: Design, assign,
formula, opmeth (see arguments), B (see
arguments), bootCoef (matrix of B bootstrapped
coefficients), boot.Corr (vector of bootstrapped correlation
parameters), Nboot (vector of total sample size used in each
bootstrap (may vary if have unbalanced clusters), and var
(sample variance-covariance matrix of bootstrapped coefficients).
Jose Pinheiro jcp@research.bell-labs.com, Douglas Bates bates@stat.wisc.edu, Frank Harrell f.harrell@vanderbilt.edu, Patrick Aboyoun aboyoun@insightful.com
Pinheiro J, Bates D (2000): Mixed effects models in S and S-Plus. New York: Springer-Verlag.
gls
glsControl, glsObject,
varFunc, corClasses, varClasses
## Not run:
ns <- 20 # no. subjects
nt <- 10 # no. time points/subject
B <- 10 # no. bootstrap resamples
# usually do 100 for variances, 1000 for nonparametric CLs
rho <- .5 # AR(1) correlation parameter
V <- matrix(0, nrow=nt, ncol=nt)
V <- rho^abs(row(V)-col(V)) # per-subject correlation/covariance matrix
d <- expand.grid(tim=1:nt, id=1:ns)
d$trt <- factor(ifelse(d$id <= ns/2, 'a', 'b'))
true.beta <- c(Intercept=0,tim=.1,'tim^2'=0,'trt=b'=1)
d$ey <- true.beta['Intercept'] + true.beta['tim']*d$tim +
true.beta['tim^2']*(d$tim^2) + true.beta['trt=b']*(d$trt=='b')
set.seed(13)
library(MASS) # needed for mvrnorm
d$y <- d$ey + as.vector(t(mvrnorm(n=ns, mu=rep(0,nt), Sigma=V)))
dd <- datadist(d); options(datadist='dd')
# library(nlme) # S-Plus: library(nlme3) or later
f <- glsD(y ~ pol(tim,2) + trt, correlation=corCAR1(form= ~tim | id),
data=d, B=B)
f
f$var # bootstrap variances
f$varBeta # original variances
summary(f)
anova(f)
plot(f, tim=NA, trt=NA)
# v <- Variogram(f, form=~tim|id, data=d)
## End(Not run)