rmsMisc
Miscellaneous Design Attributes and Utility Functions
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 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.
Usage
# 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,
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)
Arguments
- 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 firstp
rows and columns of the covariance matrix to be returned, wherep
is the length ofobject$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 fororm
to"mid"
to use the default fororm
. The default is to use the first forlrm
and the median intercept fororm
.- 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. Iflp
andy
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 byoptions(datadist=)
- need.all
set to
FALSE
to preventGetlim
orGetlimi
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). ForAIC.rms
,type
specifies the basis on which to return AIC. The default is minus twice the maximized log likelihood plusk
times the degrees of freedom counting intercept(s). Specifytype='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 (forprModFit
- 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 formlabels=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")
. ForprStats
, 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 asdatadist
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 theprint.
output for a fit. ForprStats
,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 useformat
without rounding, as with integers and floating point values). Negative values ofdigits
indicate that the value is a P-value to be formatted withformatNP
. 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. Specifycoefs=n
to print only the firstn
regression coefficients in the model.- file
name of file to which to write model output
- append
specify
append=FALSE
when usingfile
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'
orneedspace='1.5in'
.- na.rm
set to
FALSE
to keepNA
s in the vector created byreListclean
- pvalue
set to
TRUE
if you want values below 10 to the minusdigits
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- …
other arguments. For
reListclean
this contains the elements being extracted. ForprModFit
this information is passed to theHmisc latexTabular
function when a block of output is a vector to be formatted in LaTeX.
Value
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.
See Also
rms
, fastbw
, anova.rms
,
summary.lm
, summary.glm
,
datadist
, vif
, bootcov
,
latex
, latexTabular
,
latexSN
, print.char.matrix
Examples
# 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)
# }