anova.Design
,
fastbw
, etc., to retrieve various attributes of a design. These
functions allow some fitting functions not in the
Design
series
(e.g,,
lm
,
glm
) to be used with
anova.Design
,
fastbw
, and similar functions.
For
Varcov
, there are these functions:
Varcov.default
,
Varcov.lm
,
Varcov.glm
. The
oos.loglik
function for
each type of model implemented computes the -2 log likelihood for
out-of-sample data (i.e., data not necessarily used to fit the model)
evaluated at the parameter estimates from a model fit. Vectors for the
model's linear predictors and response variable must be given.
oos.loglik
is used primarily by
bootcov
.
The
Getlim
function retrieves distribution summaries
from the fit or from a
datadist
object. It handles getting summaries
from both sources to fill in characteristics for variables that were not
defined during the model fit.
Getlimi
returns the summary
for an individual model variable.
The
related.predictors
function
returns a list containing variable numbers that are directly or
indirectly related to each predictor. The
interactions.containing
function returns indexes of interaction effects containing a given
predictor. The
param.order
function returns a vector of logical
indicators for whether parameters are associated with certain types of
effects (nonlinear, interaction, nonlinear interaction).
The
Penalty.matrix
function builds a default penalty matrix for
non-intercept term(s) for use in penalized maximum likelihood
estimation. The
Penalty.setup
function takes a constant or list
describing penalty factors for each type of term in the model and
generates the proper vector of penalty multipliers for the current model.
The
lrtest
function does likelihood ratio tests for
two nested models, from fits that have
stats
components with
"Model L.R."
values. For models such as
psm, survreg, ols, lm
which have
scale parameters, it is assumed that scale parameter for the smaller model
is fixed at the estimate from the larger model (see the example).
univarLR
takes a multivariable model fit object from
Design
and re-fits a sequence of models containing one predictor
at a time. It prints a table of likelihood ratio chi^2 statistics
from these fits.
The
Newlabels
function is used to override the variable labels in a
fit object. Likewise,
Newlevels
can be used to create a new fit object
with levels of categorical predictors changed. These two functions are
especially useful when constructing nomograms.
DesignFit
is used to convert a fit from non-Design functions (e.g.,
glm
) that were invoked with Design in effect to Design functions so
that
anova.Design
will be called by
anova()
, etc. So that the
original fit's
residuals
and
print
methods, if they exist, will be
called, there are functions
print.Design
and
residuals.Design
to
dispatch them. These two functions are not needed in versions of
S-PLUS prior to 5.x (i.e., non-SV4).
## S3 method for class 'cph': Varcov(object, regcoef.only=FALSE, ...) ## S3 method for class 'glmD': Varcov(object, regcoef.only=FALSE, ...) ## S3 method for class 'glsD': Varcov(object, regcoef.only=FALSE, ...) ## S3 method for class 'lrm': Varcov(object, regcoef.only=FALSE, ...) ## S3 method for class 'ols': Varcov(object, regcoef.only=FALSE, ...) ## S3 method for class 'psm': Varcov(object, regcoef.only=FALSE, ...) oos.loglik(fit, ...) ## S3 method for class 'ols': oos.loglik(fit, lp, y, ...) ## S3 method for class 'lrm': oos.loglik(fit, lp, y, ...) ## S3 method for class 'cph': oos.loglik(fit, lp, y, ...) ## S3 method for class 'psm': oos.loglik(fit, lp, y, ...) ## S3 method for class 'glmD': oos.loglik(fit, lp, y, ...) num.intercepts(fit) Getlim(at, allow.null=FALSE, need.all=TRUE) Getlimi(name, Limval, need.all=TRUE) related.predictors(at, type=c("all","direct")) interactions.containing(at, pred) param.order(at, term.order) Penalty.matrix(at, X) Penalty.setup(at, penalty) lrtest(fit1, fit2) ## S3 method for class 'lrtest': print(x, ...) univarLR(fit) Newlabels(fit, ...) Newlevels(fit, ...) ## S3 method for class 'Design': Newlabels(fit, labels, ...) ## S3 method for class 'Design': Newlevels(fit, levels, ...) DesignFit(fit) # fit from glm, lm, etc.,then use anova etc. on result
Design
element of a fit
lrm,ols,psm,cph
etc. It doesn't matter which
fit object is the sub-model.
regcoef.only=TRUE
causes only the first
p
rows and columns of the covariance matrix to be returned, where
p
is the length of
object$coef
.
oos.loglik
. For proportional odds
ordinal logistic models, this should have used the first intercept
only. If
lp
and
y
are omitted, the -2 log likelihood for the
original fit are returned.
oos.loglik
.
Getlim
Getlim
from issuing an error message if no limits are found
in the fit or in the object pointed to by
options(datadist=)
FALSE
to prevent
Getlim
or
Getlimi
from issuing an error message
if data for a variable are not found
"direct"
to return lists of indexes of directly related
factors only (those in interactions with the predictor)
lrtest
labels
of the
form
labels=c("Age in Years","Cholesterol")
, where the list of new labels is
assumed to be the length of all main effect-type variables in the fit and
in their original order in the model formula. You may specify a named
vector to give new labels in random order or for a subset of the
variables, e.g.,
labels=c(age="Age in Years",chol="Cholesterol")
.
parms
as well as
datadist
information
(if available) that were stored with the fit.
Varcov
the first argument is
the fit object
Varcov
returns a variance-covariance matrix, and
num.intercepts
returns an integer with the number of intercepts in the model.
oos.loglik
returns a scalar -2 log likelihood value.
Getlim
returns a list with components
limits
and
values
, either
stored in
fit
or retrieved from the object created by
datadist
and
pointed to in
options(datadist=)
.
related.predictors
returns a list of vectors, and
interactions.containing
returns a vector.
param.order
returns a logical vector corresponding
to non-strata terms in the model.
Penalty.matrix
returns a symmetric matrix with dimension equal to the
number of slopes in the model. For all but categorical predictor main
effect elements, the matrix is diagonal with values equal to the variances
of the columns of
X
. For segments corresponding to
c-1
dummy variables
for
c
-category predictors, puts a
c-1
x
c-1
sub-matrix in
Penalty.matrix
that is constructed so that a quadratic form with
Penalty.matrix
in the middle computes the sum of squared differences
in parameter values about the mean, including a portion for the reference
cell in which the parameter is by definition zero.
Newlabels
returns a new fit object with the labels adjusted.
DesignFit
returns the original object but with
oldClass
of
"Design"
and with a new attribute
"fitFunction"
containing the
original vector of classes.
## Not run: f <- psm(S ~ x1 + x2 + sex + race, dist='gau') g <- psm(S ~ x1 + sex + race, dist='gau', fixed=list(scale=exp(f$parms))) lrtest(f, g) g <- Newlabels(f, c(x2='Label for x2')) g <- Newlevels(g, list(sex=c('Male','Female'),race=c('B','W'))) nomogram(g) ## End(Not run)