| gendata {Design} | R Documentation |
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.
gendata(fit, ...) ## S3 method for class 'Design': gendata(fit, nobs, viewvals=FALSE, editor=.Options$editor, ..., factors) ## Default S3 method: gendata(fit, ...)
fit |
a fit object created with Design in effect
|
nobs |
number of observations to create if doing it interactively using X-windows |
viewvals |
if nobs is given, set viewvals=TRUE to open a window displaying the
possible value of categorical predictors
|
editor |
editor to use to edit the list of variable names to consider.
Default is options(editor=) value ("xedit" is this is not specified
by using.X()==TRUE.
|
... |
predictor settings, if nobs is not given.
|
factors |
a list containing predictor settings with their names. This is an alternative to specifying the variables separatey in ... . |
if you have a variable in ... that is named n, no, nob, nob, add
nobs=FALSE to the invocation to prevent that variable from being misrecognized
as nobs
a data frame with all predictors, and an attribute names.subset if
nobs is specified. This attribute contains the vector of variable
names for predictors which were passed to data.ed and hence were
allowed to vary. If neither nobs nor any predictor settings were
given, returns a data frame with adjust-to values.
optionally writes to the terminal, opens X-windows, and generates a
temporary file using sink.
Frank Harrell
Department of Biostatistics
Vanderbilt University
f.harrell@vanderbilt.edu
predict.Design, survest.cph, survest.psm, Design.Misc, expand.grid, data.entry, page,
print.datadist, edit.data.frame,
plot.Design
set.seed(1)
age <- rnorm(200, 50, 10)
sex <- factor(sample(c('female','male'),200,TRUE))
race <- factor(sample(c('a','b','c','d'),200,TRUE))
y <- sample(0:1, 200, TRUE)
dd <- datadist(age,sex,race)
options(datadist="dd")
f <- lrm(y ~ age*sex + race)
gendata(f)
gendata(f, age=50)
d <- gendata(f, age=50, sex="female") # leave race=reference category
d <- gendata(f, age=c(50,60), race=c("b","a")) # 4 obs.
d$Predicted <- predict(f, d, type="fitted")
d # Predicted column prints at the far right
options(datadist=NULL)
## Not run:
d <- gendata(f, nobs=5, view=TRUE) # 5 interactively defined obs.
d[,attr(d,"names.subset")] # print variables which varied
predict(f, d)
## End(Not run)