plot.calibrationband {calibrationband}  R Documentation 
Uses the ggplot2 package to illustrate monotone confidence bands to assess calibration of prediction methods that issue probability forecasts.
## S3 method for class 'calibrationband'
autoplot(
object,
...,
approx.equi = NULL,
cut.bands = FALSE,
p_ribbon = NULL,
p_isoreg = NULL,
p_diag = NULL
)
## S3 method for class 'calibrationband'
autolayer(
object,
...,
approx.equi = NULL,
cut.bands = FALSE,
p_diag = NA,
p_isoreg = NA,
p_ribbon = NA
)
## S3 method for class 'calibrationband'
plot(x, ...)
object 
object of class 
... 
Further arguments to be passed to or from methods. 
approx.equi 
If In large data sets, Note, we add important additional points the initial scalar of

cut.bands 
Cut the bands at most extreme prediction values. Bands will not be extended to 0 and 1 respectively if option is set equal to true. 
p_ribbon 
If non 
p_isoreg 
If non 
p_diag 
If non 
x 
object of class 
When plotting the monotone confidence band, the upper bound should be
extended to the left, that is, the bound at x[i]
is valid on the
interval (x[i1],x[i]]
. The lower bound should be extended to the
right, i.e. the bound at x[i] is extended to the interval [x[i],x[i +
1])
. This function creates x and y values for correct plotting of these
bounds.
autoplot
behaves like any ggplot() + layer()
combination.
That means, customized plots should be created using autoplot
and
autolayer
.
Setting any of the p_*
arguments to NA
disables that layer.
Default parameter values for p_*
p_isoreg  list(color = "darkgray") 
p_diag  list(color = "black", fill="blue", alpha = .1) 
p_ribbon  list(low = "gray", high = "red", guide = "none", limits=c(0,1))

An object inheriting from class 'ggplot'
.
s=.8
n=10000
x < sort(runif(n))
p < function(x,s){p = 1/(1+((1/x*(1x))^(s+1)));return(p)}
dat < data.frame(pr=x, y=rbinom(n,1,p(x,s)))
cb < calibration_bands(x=dat$pr, y=dat$y,alpha=0.05, method="round", digits =3)
#simple plotting
plot(cb)
autoplot(cb)
#customize the plot using ggplot2::autolayer
autoplot(
cb,
approx.equi=NULL,
p_ribbon = NA
)+
ggplot2::autolayer(
cb,
p_ribbon = list(alpha = .3, fill = "gray", colour = "blue"),
)