anova.rms,
fastbw, etc., to retrieve various attributes of a design. These
functions allow some fitting functions not in the rms series
(e.g,, lm, glm) to be used with rms.Design,
fastbw, and similar functions.
For vcov, there are these functions: vcov.default,
vcov.lm, vcov.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
rms 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.
rmsFit is used to convert a fit from non-rms functions (e.g.,
glm) that were invoked with rms in effect to rms functions so
that anova.rms 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.rms and residuals.rms to
dispatch them.
## S3 method for class 'cph':
vcov(object, regcoef.only=TRUE, \dots)
## S3 method for class 'Glm':
vcov(object, \dots)
## S3 method for class 'Gls':
vcov(object, \dots)
## S3 method for class 'lrm':
vcov(object, regcoef.only=TRUE, \dots)
## S3 method for class 'ols':
vcov(object, regcoef.only=TRUE, \dots)
## S3 method for class 'psm':
vcov(object, regcoef.only=TRUE, \dots)oos.loglik(fit, ...)
## S3 method for class 'ols':
oos.loglik(fit, lp, y, \dots)
## S3 method for class 'lrm':
oos.loglik(fit, lp, y, \dots)
## S3 method for class 'cph':
oos.loglik(fit, lp, y, \dots)
## S3 method for class 'psm':
oos.loglik(fit, lp, y, \dots)
## S3 method for class 'Glm':
oos.loglik(fit, lp, y, \dots)
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, \dots)
univarLR(fit)
Newlabels(fit, ...)
Newlevels(fit, ...)
## S3 method for class 'rms':
Newlabels(fit, labels, \dots)
## S3 method for class 'rms':
Newlevels(fit, levels, \dots)
rmsFit(fit) # fit from glm, lm, etc.,then use anova etc. on result
regcoef.only=TRUE causes only the first
p rowsDesign element of a fitlrm,ols,psm,cph etc. It doesn't matter which
fit object is the sub-model.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 returneoos.loglik.GetlimGetlim 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)lrtestlabels of the
form labels=c("Age in Years","Cholesterol"), where the list of new labels is
assumed to be the lparms as well as datadist information
(if available) that were stored with the fit.vcov 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.
rmsFit returns the original object but with oldClass of
"rms" and with a new attribute "fitFunction" containing the
original vector of classes.rms, fastbw, anova.rms, summary.lm, summary.glm, datadist, vif, bootcovf <- 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)Run the code above in your browser using DataCamp Workspace