Description Usage Arguments Details Value Author(s) References See Also Examples
Compute and display pointwise confidence intervals
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  ## S3 method for class 'mboost'
confint(object, parm = NULL, level = 0.95, B = 1000,
B.mstop = 25, newdata = NULL, which = parm,
papply = ifelse(B.mstop == 0, mclapply, lapply),
cvrisk_options = list(), ...)
## S3 method for class 'mboost.ci'
plot(x, which, level = x$level, ylim = NULL, type = "l", col = "black",
ci.col = rgb(170, 170, 170, alpha = 85, maxColorValue = 255),
raw = FALSE, print_levelplot = TRUE,...)
## S3 method for class 'mboost.ci'
lines(x, which, level = x$level,
col = rgb(170, 170, 170, alpha = 85, maxColorValue = 255),
raw = FALSE, ...)
## S3 method for class 'glmboost'
confint(object, parm = NULL, level = 0.95,
B = 1000, B.mstop = 25, which = parm, ...)
## S3 method for class 'glmboost.ci'
print(x, which = NULL, level = x$level, pe = FALSE, ...)

object 
a fitted model object of class 
parm, which 
a subset of baselearners to take into account for computing
confidence intervals. See 
level 
the confidence level required. 
B 
number of outer bootstrap replicates used to compute the empirical bootstrap confidence intervals. 
B.mstop 
number of inner bootstrap replicates used to determine the optimal
mstop on each of the 
newdata 
optionally, a data frame on which to compute the predictions for the confidence intervals. 
papply 
(parallel) apply function for the outer bootstrap, defaults to

cvrisk_options 
(optionally) specify a named list with arguments to the inner
bootstrap. For example use 
x 
a confidence interval object. 
ylim 
limits of the y scale. Per default computed from the data to plot. 
type 
type of graphic for the point estimate, i.e., for the predicted function. Per default a line is plotted. 
col 
color of the point estimate, i.e., for the predicted function. 
ci.col 
color of the confidence interval. 
raw 
logical, should the raw function estimates or the derived confidence estimates be plotted? 
print_levelplot 
logical, should the lattice 
pe 
logical, should the point estimtate (PE) be also returned? 
... 
additional arguments to the outer bootstrap such as 
Use a nested boostrap approach to compute pointwise confidence intervals for the predicted partial functions or regression parameters. The approach is further described in Hofner et al. (2016).
An object of class glmboost.ci
or mboost.ci
with special
print
and/or plot
functions.
Benjamin Hofner <benjamin.hofner@pei.de>
Benjamin Hofner, Thomas Kneib and Torsten Hothorn (2016), A Unified Framework of Constrained Regression. Statistics & Computing, 26, 1–14.
cvrisk
for crossvalidation approaches and
mboost_methods
for other methods.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44  ## Not run:
############################################################
## Do not run these examples automatically as they take
## some time (~ 30 seconds depending on the system)
### a simple linear example
set.seed(1907)
data < data.frame(x1 = rnorm(100), x2 = rnorm(100),
z = factor(sample(1:3, 100, replace = TRUE)))
data$y < rnorm(100, mean = data$x1  data$x2  1 * (data$z == 2) +
1 * (data$z == 3), sd = 0.1)
linmod < glmboost(y ~ x1 + x2 + z, data = data,
control = boost_control(mstop = 200))
## compute confidence interval from 10 samples. Usually one should use
## at least 1000 samples.
CI < confint(linmod, B = 10, level = 0.9)
CI
## to compute a confidence interval for another level simply change the
## level in the print function:
print(CI, level = 0.8)
## or print a subset (with point estimates):
print(CI, level = 0.8, pe = TRUE, which = "z")
### a simple smooth example
set.seed(1907)
data < data.frame(x1 = rnorm(100), x2 = rnorm(100))
data$y < rnorm(100, mean = data$x1^2  sin(data$x2), sd = 0.1)
gam < gamboost(y ~ x1 + x2, data = data,
control = boost_control(mstop = 200))
## compute confidence interval from 10 samples. Usually one should use
## at least 1000 samples.
CI_gam < confint(gam, B = 10, level = 0.9)
par(mfrow = c(1, 2))
plot(CI_gam, which = 1)
plot(CI_gam, which = 2)
## to compute a confidence interval for another level simply change the
## level in the plot or lines function:
lines(CI_gam, which = 2, level = 0.8)
## End(Not run)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.