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. It has an
argument `dec`

whose elements are correspondingly removed, then
`dec`

is appended to the result vector.

`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(..., dec=NULL, 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)

`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.

- 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- dec
vector of decimal places used for rounding

- 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.

`rms`

, `fastbw`

, `anova.rms`

,
`summary.lm`

, `summary.glm`

,
`datadist`

, `vif`

, `bootcov`

,
`latex`

, `latexTabular`

,
`latexSN`

,
`print.char.matrix`

,

```
if (FALSE) {
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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