These functions are used internally to 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.
logLik.Gls is also defined.
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,
html, and LaTeX printing, the latter two resulting in html or LaTeX code
written to the console, automatically ready for knitr. The work
of printing
summary statistics is done by prStats, which uses the Hmisc
print.char.matrix function to print overall model statistics if
options(prType=) was not set to "latex" or "html".
Otherwise it generates customized LaTeX or html
code. The LaTeX longtable and epic packages must be in effect to use LaTeX.
reListclean allows one to rename a subset of a named list,
ignoring the previous names and not concatenating them as R does. It
also removes NULL elements and (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 lang="latex" it will translate any scientific
notation to LaTeX math form. If lang="html" will convert to html.
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.
html.naprint.delete does the same thing in the RStudio R markdown
context, using Hmisc::dotchartp (which uses plotly) for
drawing any needed dot chart.
removeFormulaTerms removes one or more terms from a model
formula, using strictly character manipulation. This handles problems
such as [.terms removing offset() if you subset on
anything. The function can also be used to remove the dependent
variable(s) from the formula.
getParamCoef extracts Bayesian model point estimates, with an
error message if the mode is requested and it was not computed for the fit
# S3 method for rms
vcov(object, regcoef.only=TRUE, intercepts='all', …)
# S3 method for cph
vcov(object, regcoef.only=TRUE, …)
# S3 method for Glm
vcov(object, regcoef.only=TRUE, intercepts='all', …)
# S3 method for Gls
vcov(object, intercepts='all', …)
# S3 method for lrm
vcov(object, regcoef.only=TRUE, intercepts='all', …)
# S3 method for ols
vcov(object, regcoef.only=TRUE, …)
# S3 method for orm
vcov(object, regcoef.only=TRUE, intercepts='mid', …)
# S3 method for psm
vcov(object, regcoef.only=TRUE, …)oos.loglik(fit, …)
# S3 method for ols
oos.loglik(fit, lp, y, …)
# S3 method for lrm
oos.loglik(fit, lp, y, …)
# S3 method for cph
oos.loglik(fit, lp, y, …)
# S3 method for psm
oos.loglik(fit, lp, y, …)
# S3 method for Glm
oos.loglik(fit, lp, y, …)
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 Gls
logLik(object, …)
# S3 method for ols
logLik(object, …)
# S3 method for rms
logLik(object, …)
# S3 method for rms
AIC(object, …, k=2, type=c('loglik', 'chisq'))
# S3 method for rms
nobs(object, …)
lrtest(fit1, fit2)
# S3 method for lrtest
print(x, …)
univarLR(fit)
Newlabels(fit, …)
Newlevels(fit, …)
# S3 method for rms
Newlabels(fit, labels, …)
# S3 method for rms
Newlevels(fit, levels, …)
prModFit(x, title, w, digits=4, coefs=TRUE, footer=NULL,
lines.page=40, long=TRUE, needspace, …)
prStats(labels, w, lang=c("plain", "latex", "html"))
reListclean(…, na.rm=TRUE)
formatNP(x, digits=NULL, pvalue=FALSE,
lang=c("plain", "latex", "html"))
# S3 method for naprint.delete
latex(object, file="", append=TRUE, …)
# S3 method for naprint.delete
html(object, …)
removeFormulaTerms(form, which=NULL, delete.response=FALSE)
getParamCoef(fit, posterior.summary=c('mean', 'median', 'mode'))
result of a fitting function
result of a fitting function
For fits such as parametric survival models
which have a final row and column of the covariance matrix for a
non-regression parameter such as a log(scale) parameter, setting
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.
set to "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" to use the default for orm. The default is to use the
first for lrm and the median intercept for orm.
Design element of a fit
index of a predictor variable (main effect)
fit objects from lrm,ols,psm,cph etc. It doesn't matter which
fit object is the sub-model.
linear predictor vector for 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.
values of a new vector of responses passed to oos.loglik.
the name of a variable in the model
an object returned by Getlim
prevents Getlim from issuing an error message if no limits are found
in the fit or in the object pointed to by options(datadist=)
set to FALSE to prevent Getlim or Getlimi from issuing an error message
if data for a variable are not found
For related.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 AIC. The default is minus twice the maximized log
likelihood plus k times the degrees of freedom counting
intercept(s). Specify type='chisq' to get a penalized model
likelihood ratio chi-square instead.
1 for all parameters, 2 for all parameters associated with either nonlinear or interaction effects, 3 for nonlinear effects (main or interaction), 4 for interaction effects, 5 for nonlinear interaction effects.
a design matrix, not including columns for intercepts
a vector or list specifying penalty multipliers for types of model terms
the multiplier of the degrees of freedom to be used in computing AIC. The default is 2.
a result of lrtest, or the result of a high-level model
fitting function (for prModFit
a character vector specifying new labels for variables in a fit.
To give new labels for all variables, you can specify 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").
For prStats, is a list with major column headings, which can
themselves be vectors that are then stacked vertically.
a list of named vectors specifying new level labels for categorical
predictors. This will override parms as well as datadist information
(if available) that were stored with the fit.
a single character string used to specify an overall title
for the regression fit, which is printed first by prModFit.
Set to "" to suppress the title
For prModFit, 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 statistics to print, elements of which can be vectors
that are stacked vertically. Unnamed elements specify number of
digits to the right of the decimal place to which to round (NA
means use format without rounding, as with integers and
floating point values). Negative values of digits indicate
that the value is a P-value to be formatted with formatNP.
Digits are recycled as needed.
number of digits to the right of the decimal point, for formatting numeric values in printed output
specify coefs=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.
a character string to appear at the bottom of the regression model output
name of file to which to write model output
specify append=FALSE when using file and you
want to start over instead of adding to an existing file.
specifies the typesetting language: plain text, LaTeX, or html
see latex
set to FALSE to suppress printing of formula and
certain other model output
optional character string to insert inside a LaTeX
needspace macro call before the statistics table and before the
coefficient matrix, to avoid bad page splits. This assumes the LaTeX
needspace style is available. Example:
needspace='6\baselineskip' or needspace='1.5in'.
set to FALSE to keep NAs in the vector
created by reListclean
set to TRUE if you want values below 10 to the
minus digits to be formatted to be less than that value
a formula object
a vector of one or more character strings specifying the
names of functions that are called from a formula, e.g.,
"cluster". By default no right-hand-side terms are removed.
set to TRUE to remove the dependent
variable(s) from the formula
type of Bayesian point estimate to use from coefficients
other arguments. For reListclean 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 LaTeX.
vcov 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.
reListclean returns a vector of named (by its arguments) elements.
formatNP returns a character vector.
removeFormulaTerms returns a formula object.
rms, fastbw, anova.rms,
summary.lm, summary.glm,
datadist, vif, bootcov,
latex, latexTabular,
latexSN, print.char.matrix
# 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)
# }
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