R: rms Methods and Generic Functions

rms {rms} | R Documentation

## rms Methods and Generic Functions

### Description

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, called by a modified version of `model.frame.default` to store 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`. In addition to `model.frame.default` being replaced by a modified version, `[.` and `[.factor` are replaced by versions which carry along the `label` attribute of a variable. In this way, when an `na.action` function is called to subset out NAs, labels are still defined for variables in the model.

### Usage

    Design(mf, 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)

### Arguments

`mf` | a model frame
`allow.offset` | set to `TRUE` if model fitter allows an offset term
`intercept` | 1 if an ordinary intercept is present, 0 otherwise

### Value

a data frame augmented with additional information about the predictors and model formulation

### Author(s)

Frank Harrell
Department of Biostatistics, Vanderbilt University
f.harrell@vanderbilt.edu

### See Also

`rms.trans`, `rmsMisc`, `cph`, `lrm`, `ols`, `specs.rms`, `anova.rms`, `summary.rms`, `Predict`, `gendata`, `fastbw`, `predictrms`. `validate`, `calibrate`, `which.influence`, `latex`, `latexrms`, `model.frame.default`, `datadist`, `describe`, `nomogram`, `vif`, `dataRep`

### Examples

    ## 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))))

    ## End(Not run)

[Package rms version 5.1-1 Index]