rms Special Transformation Functions
rms.trans.RdThis is a series of functions (asis, pol, lsp,
rcs, catg, scored, strat, matrx,
gTrans, and
%ia%) that set up special attributes (such as
knots and nonlinear term indicators) that are carried through to fits
(using for example lrm,cph, ols,
psm). anova.rms, summary.rms, Predict,
survplot, fastbw, validate, specs,
which.influence, nomogram and latex.rms use these
attributes to automate certain analyses (e.g., automatic tests of linearity
for each predictor are done by anova.rms). Many of the functions
are called implicitly. Some S functions such as ns derive data-dependent
transformations that are not always "remembered" when predicted values are
later computed, so the predictions may be incorrect. The functions listed
here solve that problem when used in the rms context.
asis is the identity transformation, pol is an ordinary
(non-orthogonal) polynomial, rcs is a linear tail-restricted
cubic spline function (natural spline, for which the
rcspline.eval function generates the design matrix, the
presence of system option rcspc causes rcspline.eval to be
invoked with pc=TRUE, and the presence of system option fractied
causes this value to be passed to rcspline.eval as the fractied
argument), catg is for a categorical variable,
scored is for an ordered categorical variable, strat is
for a stratification factor in a Cox model, matrx is for a matrix
predictor, and %ia% represents restricted interactions in which
products involving nonlinear effects on both variables are not included
in the model. asis, catg, scored, matrx are seldom invoked
explicitly by the user (only to specify label or name,
usually).
gTrans is a general multiple-parameter transformation function.
It can be used to specify new polynomial bases, smooth relationships
with a discontinuity at one or more values of x, grouped
categorical variables, e.g., a categorical variable with 5 levels where
you want to combine two of the levels to spend only 3 degrees of freedom in
all but see plots of predicted values where the two combined categories
are kept separate but will have equal effect estimates. The first
argument to gTrans is a regular numeric, character, or factor
variable. The next argument is a function that transforms a vector into
a matrix. If the basis functions are to include a linear term it is up
too the user to include the original x as one of the columns.
Column names are assigned automaticall, but any column names specified
by the user will override the default name. If you want to signal which
terms correspond to linear and which correspond to nonlinear effects for
the purpose of running anova.rms, add an integer vector attribute
nonlinear to the resulting matrix. This vector specifies the
column numbers corresponding to nonlinear effects. The default is to assume a column
is a linear effect. The parms attribute stored with a
gTrans result a character vector version of the function, so as
to not waste space carrying along any environment information. If you
will be using the latex method for typesetting the fitted model,
you must include a tex attribute also in the produced matrix.
This must be a function of a single character string argument (that will
ultimately contain the name of the predictor in LaTeX notation) and must
produce a vector of LaTeX character strings. See
https://hbiostat.org/R/examples/gTrans/gTrans.html for several examples of the
use of gTrans including the use of nonlinear and
tex.
A makepredictcall method is defined so that usage of the
transformation functions outside of rms fitting functions will
work for getting predicted values. Thanks to Therry Therneau for the code.
In the list below, functions asis through gTrans can have
arguments x, parms, label, name except that parms does not
apply to asis, matrx, strat.
Usage
asis(...)
matrx(...)
pol(...)
lsp(...)
rcs(...)
catg(...)
scored(...)
strat(...)
gTrans(...)
x1 %ia% x2
# S3 method for class 'rms'
makepredictcall(var, call)Arguments
- ...
The arguments ... above contain the following.
xa predictor variable (or a function of one). If you specify e.g.
pol(pmin(age,10),3), a cubic polynomial will be fitted inpmin(age,10)(pminis the S vector element–by–element function). The predictor will be labeledagein the output, and plots with haveagein its original units on the axes. If you use a function such aspmin, the predictor is taken as the first argument, and other arguments must be defined in the frame in effect when predicted values, etc., are computed.parmsparameters of transformation (e.g. number or location of knots). For
polthe argument is the order of the polynomial, e.g.2for quadratic (the usual default). Forlspit is a vector of knot locations (lspwill not estimate knot locations). Forrcsit is the number of knots (if scalar), or vector of knot locations (if>2elements). The default number is thenknotssystem option ifparmsis not given. If the number of knots is given, locations are computed for that number of knots. If system optionrcspcisTRUEtheparmsvector has an attribute defining the principal components transformation parameters. Forcatg,parmsis the category labels (not needed if variable is an S category or factor variable). If omitted,catgwill useunique(x), orlevels(x)ifxis acategoryor afactor. Forscored,parmsis a vector of unique values of variable (usesunique(x)by default). This is not needed ifxis an Sorderedvariable. Forstrat,parmsis the category labels (not needed if variable is an S category variable). If omitted, will useunique(x), orlevels(x)ifxiscategoryorfactor.parmsis not used formatrix.labellabel of predictor for plotting (default =
"label"attribute or variable name)nameName to use for predictor in model. Default is name of argument to function.
- x1,x2
two continuous variables for which to form a non-doubly-nonlinear interaction
- var
a model term passed from a (usually non-
rms) function- call
call object for a model term