rms (version 5.1-2)

rms.trans: rms Special Transformation Functions


This is a series of functions (asis, pol, lsp, rcs, catg, scored, strat, matrx, 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).

In the list below, functions asis through strat can have arguments x, parms, label, name except that parms does not apply to asis, matrx, strat.


x1 %ia% x2


The arguments … above contain the following.


a predictor variable (or a function of one). If you specify e.g. pol(pmin(age,10),3), a cubic polynomial will be fitted in pmin(age,10) (pmin is the S vector element--by--element function). The predictor will be labeled age in the output, and plots with have age in its original units on the axes. If you use a function such as pmin, 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.


parameters of transformation (e.g. number or location of knots). For pol the argument is the order of the polynomial, e.g. 2 for quadratic (the usual default). For lsp it is a vector of knot locations (lsp will not estimate knot locations). For rcs it is the number of knots (if scalar), or vector of knot locations (if >2 elements). The default number is the nknots system option if parms is not given. If the number of knots is given, locations are computed for that number of knots. If system option rcspc is TRUE the parms vector has an attribute defining the principal components transformation parameters. For catg, parms is the category labels (not needed if variable is an S category or factor variable). If omitted, catg will use unique(x), or levels(x) if x is a category or a factor. For scored, parms is a vector of unique values of variable (uses unique(x) by default). This is not needed if x is an S ordered variable. For strat, parms is the category labels (not needed if variable is an S category variable). If omitted, will use unique(x), or levels(x) if x is category or factor. parms is not used for matrix.


label of predictor for plotting (default = "label" attribute or variable name)


Name to use for predictor in model. Default is name of argument to function.


two continuous variables for which to form a non-doubly-nonlinear interaction

See Also

rcspline.eval, rcspline.restate, rms, cph, lrm, ols, datadist


Run this code
options(knots=4, poly.degree=2)
# To get the old behavior of rcspline.eval knot placement (which didnt' handle
# clumping at the lowest or highest value of the predictor very well):
# options(fractied = 1.0)   # see rcspline.eval for details
country <- factor(country.codes)
blood.pressure <- cbind(sbp=systolic.bp, dbp=diastolic.bp)
fit <- lrm(Y ~ sqrt(x1)*rcs(x2) + rcs(x3,c(5,10,15)) + 
       lsp(x4,c(10,20)) + country + blood.pressure + poly(age,2))
# sqrt(x1) is an implicit asis variable, but limits of x1, not sqrt(x1)
#       are used for later plotting and effect estimation
# x2 fitted with restricted cubic spline with 4 default knots
# x3 fitted with r.c.s. with 3 specified knots
# x4 fitted with linear spline with 2 specified knots
# country is an implied catg variable
# blood.pressure is an implied matrx variable
# since poly is not an rms function (pol is), it creates a
#       matrx type variable with no automatic linearity testing
#       or plotting
f1 <- lrm(y ~ rcs(x1) + rcs(x2) + rcs(x1) %ia% rcs(x2))
# %ia% restricts interactions. Here it removes terms nonlinear in
# both x1 and x2
f2 <- lrm(y ~ rcs(x1) + rcs(x2) + x1 %ia% rcs(x2))
# interaction linear in x1
f3 <- lrm(y ~ rcs(x1) + rcs(x2) + x1 %ia% x2)
# simple product interaction (doubly linear)
# Use x1 %ia% x2 instead of x1:x2 because x1 %ia% x2 triggers
# anova to pool x1*x2 term into x1 terms to test total effect
# of x1
# }

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