confint.mboost {mboost} | R Documentation

## Pointwise Bootstrap Confidence Intervals

### Description

Compute and display pointwise confidence intervals

### Usage

    ## 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, ...)

### Arguments

`object` | a fitted model object of class `glmboost`, `gamboost` or `mboost` for which the confidence intervals should be computed.
`parm, which` | a subset of base-learners to take into account for computing confidence intervals. See `mboost_methods` for details. `parm` is just a synonyme for `which` to be in line with the generic `confint` function. Preferably use `which`.
`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 `B` bootstrap samples.
`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 `mclapply` if no inner bootstrap is used to determine the optimal stopping iteration. For details see argument `papply` in `cvrisk`. Be careful with your computing resources if you use parallel computing for both, the inner and the outer bootstrap.
`cvrisk_options` | (optionally) specify a named list with arguments to the inner bootstrap. For example use `cvrisk_options = list(mc.cores = 2)` to specify that the `mclapply` function within `cvrisk` uses 2 cores to compute the optimal `mstop`.
`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 `levelplot` be printed or simply returned for further modifications. This argument is only considered if bivariate effect estimates are plotted. If `print_levelplot` is set to `FALSE`, a list with objects `mean`, `lowerPI` and `upperPI` is returned containing the three `levelplot` objects.
`pe` | logical, should the point estimtate (PE) be also returned?
`...` | additional arguments to the outer bootstrap such as `mc.cores`.

### Details

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).

### Value

An object of class `glmboost.ci` or `mboost.ci` with special `print` and/or `plot` functions.

### Author(s)

Benjamin Hofner <benjamin.hofner@fau.de>

### References

Benjamin Hofner, Thomas Kneib and Torsten Hothorn (2016), A Unified Framework of Constrained Regression. Statistics & Computing, 26, 1–14.

### See Also

`cvrisk` for crossvalidation approaches and `mboost_methods` for other methods.

### Examples

    ############################################################
    ## 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)

[Package mboost version 2.8-1 Index]