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 several functions. The method for orm
fits is a bit different because the covariance matrix stored in the fit
object only deals with the middle intercept. See the intercepts
argument for more options. There is a method for lrm that also
allows non-default intercept(s) to be selected (default is first).
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).
combineRelatedPredictors creates of list of inter-connected main
effects and interations for use with predictrms with
type='ccterms' (useful for gIndex).
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.
logLik.rms returns the maximized log likelihood for the model,
whereas AIC.rms returns the AIC. The latter function has an
optional argument for computing AIC on a "chi-square" scale (model
likelihood ratio chi-square minus twice the regression degrees of
freedom. logLik.ols handles the case for ols, just by
invoking logLik.lm in the stats package.
nobs.rms returns the number of observations used in the fit.
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.
rmsArgs handles ...arguments to functions such as
Predict, summary.rms, nomogram so that variables to
vary may be specified without values (after an equals sign).
prModFit is the workhorse for the print methods for
highest-level rms model fitting functions, handling both regular
and LaTeX printing, the latter resulting in LaTeX code written to the
terminal, automatically ready for Sweave. The work of printing
summary statistics is done by prStats, which uses the Hmisc
print.char.matrix function to print overall model statistics if
latex=FALSE, otherwise it generates customized LaTeX code. The
LaTeX longtable and epic packages must be in effect to use these
LaTeX functions.
reVector allows one to rename a subset of a named vector,
ignoring the previous names and not concatenating them as Rdoes. It
also removes (by default) elements that are NA, as when an
optional named element is fetched that doesn't exist.
formatNP is a function to format a vector of numerics. If
digits is specified, formatNP will make sure that the
formatted representation has digits positions to the right of the
decimal place. If latex=TRUE it will translate any scientific
notation to LaTeX math form. If pvalue=TRUE, it will replace
formatted values with "< 0.0001" (if digits=4).
latex.naprint.delete will, if appropriate, use LaTeX to draw a
dot chart of frequency of variable NAs related to model fits.
## S3 method for class 'rms':
vcov(object, regcoef.only=TRUE, intercepts='all', \dots)
## S3 method for class 'cph':
vcov(object, regcoef.only=TRUE, \dots)
## S3 method for class 'Glm':
vcov(object, regcoef.only=TRUE, intercepts='all', \dots)
## S3 method for class 'Gls':
vcov(object, intercepts='all', \dots)
## S3 method for class 'lrm':
vcov(object, regcoef.only=TRUE, intercepts='all', \dots)
## S3 method for class 'ols':
vcov(object, regcoef.only=TRUE, \dots)
## S3 method for class 'orm':
vcov(object, regcoef.only=TRUE, intercepts='mid', \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)
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)
combineRelatedPredictors(at)
param.order(at, term.order)
Penalty.matrix(at, X)
Penalty.setup(at, penalty)
## S3 method for class 'ols':
logLik(object, \dots)
## S3 method for class 'rms':
logLik(object, \dots)
## S3 method for class 'rms':
AIC(object, \dots, k=2, type=c('loglik', 'chisq'))
## S3 method for class 'rms':
nobs(object, \dots)
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)
prModFit(x, title, w, digits=4, coefs=TRUE, latex=FALSE, lines.page=40,
long=TRUE, needspace, ...)
prStats(labels, w, latex=FALSE)
reVector(..., na.rm=TRUE)
formatNP(x, digits=NULL, pvalue=FALSE, latex=FALSE)
## S3 method for class 'naprint.delete':
latex(object, \dots)
regcoef.only=TRUE causes only the first
p rows"none" to omit any rows and columns
related to intercepts. Set to an integer scalar
or vector to include particular intercept elements. Set to
'all' to include all intercepts, or for orm to
"mid"Design 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 foundrelated.predictors, set to "direct" to return lists of
indexes of directly related factors only (those in interactions with the
predictor). For AIC.rms, type specifies the basis on
which to return Alrtest, or the result of a high-level model
fitting function (for prModFitlabels 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.prModFit.
Set to "" to suppress the titleprModFit, a special list of lists, which each list
element specifying information about a block of information to include
in the print. output for a fit. For prStats, w
is a list of statisticscoefs=FALSE to suppress printing the table
of model coefficients, standard errors, etc. Specify coefs=n
to print only the first n regression coefficients in the
model.latexFALSE to suppress printing of formula and
certain other model outputneedspace='6\basFALSE to keep NAs in the vector
created by reVectorTRUE if you want values below 10 to the
minus digits to be formatted to be less than that valuereVector this contains the
elements being extracted. For prModFit this information is
passed to the Hmisc latexTabular function when a block of
output is a vector to be formatted in LaTeXvcov returns a variance-covariance matrix
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 and combineRelatedPredictors return 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.reVector returns a vector of named (by its arguments) elements.
formatNP returns a character vector.
rms, fastbw, anova.rms,
summary.lm, summary.glm,
datadist, vif, bootcov,
latex, latexTabular,
latexSN, print.char.matrixf <- 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 DataLab