gendata

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Generate Data Frame with Predictor Combinations

If nobs is not specified, allows user to specify predictor settings by e.g. age=50, sex="male", and any omitted predictors are set to reference values (default=median for continuous variables, first level for categorical ones - see datadist). If any predictor has more than one value given, expand.grid is called to generate all possible combinations of values. If nobs is given, a data frame is first generated which has nobs of adjust-to values duplicated. Then an editor window is opened which allows the user to subset the variable names down to ones which she intends to vary (this streamlines the data.ed step). Then, if any predictors kept are discrete and viewvals=TRUE, a window (using page) is opened defining the possible values of this subset, to facilitate data editing. Then the data.ed function is invoked to allow interactive overriding of predictor settings in the nobs rows. The subset of variables are combined with the other predictors which were not displayed with data.ed, and a final full data frame is returned. gendata is most useful for creating a newdata data frame to pass to predict.

Keywords
manip, models, methods, regression
Usage
gendata(fit, ...)
## S3 method for class 'rms':
gendata(fit, nobs, viewvals=FALSE,
  editor=.Options$editor, \dots, factors)
## S3 method for class 'default':
gendata(fit, \dots)
Arguments
fit
a fit object created with rms in effect
nobs
number of observations to create if doing it interactively using X-windows
viewvals
if nobs is given, set viewvals=TRUE to open a window displaying the possible value of categorical predictors
editor
editor to use to edit the list of variable names to consider. Default is options(editor=) value ("xedit" is this is not specified by using.X()==TRUE.
...
predictor settings, if nobs is not given.
factors
a list containing predictor settings with their names. This is an alternative to specifying the variables separatey in ....
Details

if you have a variable in ... that is named n, no, nob, nob, add nobs=FALSE to the invocation to prevent that variable from being misrecognized as nobs

Value

  • a data frame with all predictors, and an attribute names.subset if nobs is specified. This attribute contains the vector of variable names for predictors which were passed to data.ed and hence were allowed to vary. If neither nobs nor any predictor settings were given, returns a data frame with adjust-to values.

Side Effects

optionally writes to the terminal, opens X-windows, and generates a temporary file using sink.

See Also

predict.rms, survest.cph, survest.psm, rmsMisc, expand.grid, data.entry, page, print.datadist, edit.data.frame, Predict

Aliases
  • gendata
  • gendata.rms
  • gendata.default
Examples
set.seed(1)
age <- rnorm(200, 50, 10)
sex <- factor(sample(c('female','male'),200,TRUE))
race <- factor(sample(c('a','b','c','d'),200,TRUE))
y <- sample(0:1, 200, TRUE)
dd <- datadist(age,sex,race)
options(datadist="dd")
f <- lrm(y ~ age*sex + race)
gendata(f)
gendata(f, age=50)
d <- gendata(f, age=50, sex="female")  # leave race=reference category
d <- gendata(f, age=c(50,60), race=c("b","a"))  # 4 obs.
d$Predicted <- predict(f, d, type="fitted")
d      # Predicted column prints at the far right
options(datadist=NULL)
d <- gendata(f, nobs=5, view=TRUE)        # 5 interactively defined obs.
d[,attr(d,"names.subset")]             # print variables which varied
predict(f, d)
Documentation reproduced from package rms, version 2.0-2, License: GPL (>= 2)

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