| Design.trans {Design} | R Documentation |
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.Design, summary.Design,
plot.Design, survplot, fastbw, validate, specs,
which.influence, nomogram.Design and latex.Design use these
attributes to automate certain analyses (e.g., automatic tests of linearity
for each predictor are done by anova.Design). Many of the functions
are called implicitly. Some S functions such as ns derive data-dependent
transformations that are not "remembered" when predicted values are
later computed, so the predictions will be incorrect. The functions listed
here solve that problem.
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),
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.
asis(x, parms, label, name) matrx(x, label, name) pol(x, parms, label, name) lsp(x, parms, label, name) rcs(x, parms, label, name) catg(x, parms, label, name) scored(x, parms, label, name) strat(x, label, name) x1 %ia% x2
x |
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.
|
parms |
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.
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 |
label of predictor for plotting (default = "label" attribute or variable
name)
|
name |
Name 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 |
... |
a variety of things |
Frank Harrell
Department of Biostatistics, Vanderbilt University
f.harrell@vanderbilt.edu
rcspline.eval, rcspline.restate, Design, cph, lrm, ols, datadist
## Not run:
options(knots=4, poly.degree=2)
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 a Design 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
## End(Not run)