gendata.RdIf 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, unless expand=FALSE. 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, ..., nobs, viewvals=FALSE, expand=TRUE, factors)a fit object created with rms in effect
predictor settings, if nobs is not given.
number of observations to create if doing it interactively using X-windows
if nobs is given, set viewvals=TRUE to open a window
displaying the possible value of categorical predictors
set to FALSE to prevent expand.grid from being called,
and to instead just convert to a data frame.
a list containing predictor settings with their names. This is an
alternative to specifying the variables separately in .... Unlike the
usage of ..., variables getting default ranges in factors
should have NA as their 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 de 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.
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
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)
#> Error in Design(data, formula = formula): dataset dd not found for options(datadist=)
gendata(f)
#> Error: object 'f' not found
gendata(f, age=50)
#> Error: object 'f' not found
d <- gendata(f, age=50, sex="female") # leave race=reference category
#> Error: object 'f' not found
d <- gendata(f, age=c(50,60), race=c("b","a")) # 4 obs.
#> Error: object 'f' not found
d$Predicted <- predict(f, d, type="fitted")
#> Error: object 'f' not found
d # Predicted column prints at the far right
#> Error: object 'd' not found
options(datadist=NULL)
if (FALSE) { # \dontrun{
d <- gendata(f, nobs=5, view=TRUE) # 5 interactively defined obs.
d[,attr(d,"names.subset")] # print variables which varied
predict(f, d)
} # }