rms Methods and Generic Functions
This is a series of special transformation functions (asis,
pol, lsp, rcs, catg, scored,
strat, matrx), fitting functions (e.g.,
lrm,cph, psm, or ols), and generic
analysis functions (anova.rms, summary.rms,
Predict, plot.Predict, ggplot.Predict, survplot,
fastbw, validate, calibrate, specs.rms,
which.influence, latexrms, nomogram,
datadist, gendata)
that help automate many
analysis steps, e.g. fitting restricted interactions and multiple
stratification variables, analysis of variance (with tests of linearity
of each factor and pooled tests), plotting effects of variables in the
model, estimating and graphing effects of variables that appear non-linearly in the
model using e.g. inter-quartile-range hazard ratios, bootstrapping
model fits, and constructing nomograms for obtaining predictions manually.
Behind the scene is the Design function which
stores extra attributes. Design() is not intended to be
called by users.
Design causes detailed design attributes
and descriptions of the distribution of predictors to be stored
in an attribute of the terms component called Design.
modelData is a replacement for model.frame.default that is
much streamlined and prepares data for Design(). If a second
formula is present, modelData ensures that missing data deletions
are the same for both formulas, and produces a second model frame for
formula2 as the data2 attribute of the main returned data frame.
modelData(data=environment(formula), formula, formula2=NULL,
weights, subset, na.action=na.delete, dotexpand=TRUE,
callenv=parent.frame(n=2))
Design(mf, formula=NULL, specials=NULL, allow.offset=TRUE, intercept=1)
# not to be called by the user; called by fitting routines
# dist <- datadist(x1,x2,sex,age,race,bp)
# or dist <- datadist(my.data.frame)
# Can omit call to datadist if not using summary.rms, Predict,
# survplot.rms, or if all variable settings are given to them
# options(datadist="dist")
# f <- fitting.function(formula = y ~ rcs(x1,4) + rcs(x2,5) + x1%ia%x2 +
# rcs(x1,4)%ia%rcs(x2,5) +
# strat(sex)*age + strat(race)*bp)
# See rms.trans for rcs, strat, etc.
# %ia% is restricted interaction - not doubly nonlinear
# for x1 by x2 this uses the simple product only, but pools x1*x2
# effect with nonlinear function for overall tests
# specs(f)
# anova(f)
# summary(f)
# fastbw(f)
# pred <- predict(f, newdata=expand.grid(x1=1:10,x2=3,sex="male",
# age=50,race="black"))
# pred <- predict(f, newdata=gendata(f, x1=1:10, x2=3, sex="male"))
# This leaves unspecified variables set to reference values from datadist
# pred.combos <- gendata(f, nobs=10) # Use X-windows to edit predictor settings
# predict(f, newdata=pred.combos)
# plot(Predict(f, x1)) # or ggplot(...)
# latex(f)
# nomogram(f)data |
a data frame or calling environment |
formula |
model formula |
formula2 |
an optional second model formula (see for example
|
weights |
a weight variable or expression |
subset |
a subsetting expression evaluated in the calling frame
or |
na.action |
NA handling function, ideally one such as
|
specials |
a character vector specifying which function
evaluations appearing in |
dotexpand |
set to |
callenv |
the parent frame that called the fitting function |
mf |
a model frame |
allow.offset |
set to |
intercept |
1 if an ordinary intercept is present, 0 otherwise |
a data frame augmented with additional information about the predictors and model formulation
Frank Harrell
Department of Biostatistics, Vanderbilt University
fh@fharrell.com
## Not run:
require(rms)
dist <- datadist(data=2) # can omit if not using summary, (gg)plot, survplot,
# or if specify all variable values to them. Can
# also defer. data=2: get distribution summaries
# for all variables in search position 2
# run datadist once, for all candidate variables
dist <- datadist(age,race,bp,sex,height) # alternative
options(datadist="dist")
f <- cph(Surv(d.time, death) ~ rcs(age,4)*strat(race) +
bp*strat(sex)+lsp(height,60),x=TRUE,y=TRUE)
anova(f)
anova(f,age,height) # Joint test of 2 vars
fastbw(f)
summary(f, sex="female") # Adjust sex to "female" when testing
# interacting factor bp
bplot(Predict(f, age, height)) # 3-D plot
ggplot(Predict(f, age=10:70, height=60))
latex(f) # LaTeX representation of fit
f <- lm(y ~ x) # Can use with any fitting function that
# calls model.frame.default, e.g. lm, glm
specs.rms(f) # Use .rms since class(f)="lm"
anova(f) # Works since Varcov(f) (=Varcov.lm(f)) works
fastbw(f)
options(datadist=NULL)
f <- ols(y ~ x1*x2) # Saves enough information to do fastbw, anova
anova(f) # Will not do Predict since distributions
fastbw(f) # of predictors not saved
plot(f, x1=seq(100,300,by=.5), x2=.5)
# all values defined - don't need datadist
dist <- datadist(x1,x2) # Equivalent to datadist(f)
options(datadist="dist")
plot(f, x1, x2=.5) # Now you can do plot, summary
plot(nomogram(f, interact=list(x2=c(.2,.7))))
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