calPlot2(object, formula, data, splitMethod = "none", B = 1, M, showY, method = "nne", round = TRUE, bandwidth = NULL, q = 10, density = 55, add = FALSE, diag = !add, legend = !add, axes = !add, xlim, ylim, xlab = "Predicted event probability", ylab = "Observed proportion", col, lwd, lty, pch, cause = 1, percent = TRUE, giveToModel = NULL, na.action = na.fail, cores = 1, verbose = FALSE, ...)call that evaluates to such an R-object (see
examples), (3) a matrix with predicted probabilities
having as many rows as data in one column. For
cross-validation all objects in this list must include
their call.formula is missing, try to
extract a formula from the first element in object.data is missing, try to extract a data set from
the first element in object. none/noPlan: Assess the models in the give
data, usually either in the same data where they
are fitted, or in independent test data.
BootCv: Bootstrap cross validation. The prediction
models are trained on B bootstrap samples, that
are either drawn with replacement of the same size as the
original data or without replacement from data of
the size M. The models are assessed in the
observations that are NOT in the bootstrap sample.
TRUE the observed data are shown
as dots on the plot. "nne": The expected event status is obtained in
the nearest neighborhood around the predicted event
probabilities.
"quantile": The expected event status is obtained
in groups defined by quantiles of the predicted event
probabilities.
TRUE predicted probabilities are
rounded to two digits before smoothing. This may have a
considerable effect on computing efficiency in large data
sets.method="nne"method="quantile".TRUE the line(s) are added to an
existing plot.FALSE no diagonal line is drawn.FALSE no legend is drawn.FALSE no axes are drawn.lines.lines.lines.points.object. Each entry names parts of
the value of the fitted models that should be extracted
and added to the value.model.framemc.cores when
calling mclapply.TRUE report details of the
progress, e.g. count the steps in cross-validation.SmartControl.dpik from the
package KernSmooth for a box kernel function.
set.seed(40)
N=40
Y=rbinom(N,1,.5)
X1=rnorm(N)
X1[Y==1]=rnorm(sum(Y==1),mean=rbinom(sum(Y==1),1,.5))
X2=rnorm(N)
X2[Y==0]=rnorm(sum(Y==0),mean=rbinom(sum(Y==0),3,.5))
dat <- data.frame(Y=Y,X1=X1,X2=X2)
lm1 <- glm(Y~X1,data=dat,family="binomial")
lm2 <- glm(Y~X2,data=dat,family="binomial")
calPlot2(list(lm1,lm2),data=dat)
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