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.

`Mean`

is a generic function that creates an R function that
calculates the expected value of the response variable given a fit from
`rms`

or `rmsb`

.

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 `NA`

s 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.

```
# 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, …)
```# Given Design attributes and number of intercepts creates R
# format assign list. atr non.slopes Terms
DesignAssign(atr, non.slopes, Terms)

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, subtitle=NULL, …)

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)

fit

result of a fitting function

object

result of a fitting function

regcoef.only

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`

.

intercepts

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`

.

at

`Design`

element of a fit

pred

index of a predictor variable (main effect)

fit1

fit2

fit objects from `lrm,ols,psm,cph`

etc. It doesn't matter which
fit object is the sub-model.

lp

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.

y

values of a new vector of responses passed to `oos.loglik`

.

name

the name of a variable in the model

Limval

an object returned by `Getlim`

allow.null

prevents `Getlim`

from issuing an error message if no limits are found
in the fit or in the object pointed to by `options(datadist=)`

need.all

set to `FALSE`

to prevent `Getlim`

or `Getlimi`

from issuing an error message
if data for a variable are not found

type

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.

term.order

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.

X

a design matrix, not including columns for intercepts

penalty

a vector or list specifying penalty multipliers for types of model terms

k

the multiplier of the degrees of freedom to be used in computing AIC. The default is 2.

x

a result of `lrtest`

, or the result of a high-level model
fitting function (for `prModFit`

)

labels

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.

levels

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.

title

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.

w

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.

digits

number of digits to the right of the decimal point, for formatting numeric values in printed output

coefs

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.

footer

a character string to appear at the bottom of the regression model output

file

name of file to which to write model output

append

specify `append=FALSE`

when using `file`

and you
want to start over instead of adding to an existing file.

lang

specifies the typesetting language: plain text, LaTeX, or html

lines.page

see `latex`

long

set to `FALSE`

to suppress printing of formula and
certain other model output

needspace

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'`

.

subtitle

optional vector of character strings containing
subtitles that will appear under `title`

but not bolded

na.rm

set to `FALSE`

to keep `NA`

s in the vector
created by `reListclean`

pvalue

set to `TRUE`

if you want values below 10 to the
minus `digits`

to be formatted to be less than that value

form

a formula object

which

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.

delete.response

set to `TRUE`

to remove the dependent
variable(s) from the formula

atr, non.slopes, Terms

`Design`

function attributes, number
of intercepts, and `terms`

object

…

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) # }