refund: coefboot.pffr – R documentation

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coefboot.pffr

Simple bootstrap CIs for pffr

Description

This function resamples observations in the data set to obtain approximate CIs for different terms and coefficient functions that correct for the effects of dependency and heteroskedasticity of the residuals along the index of the functional response, i.e., it aims for correct inference if the residuals along the index of the functional response are not i.i.d.

Usage

coefboot.pffr(
  object,
  n1 = 100,
  n2 = 40,
  n3 = 20,
  B = 100,
  ncpus = getOption("boot.ncpus", 1),
  parallel = c("no", "multicore", "snow"),
  cl = NULL,
  conf = c(0.9, 0.95),
  type = "percent",
  method = c("resample", "residual", "residual.c"),
  showProgress = TRUE,
  ...
)

Arguments

`object`	a fitted `pffr`-model
`n1`	see `coef.pffr`
`n2`	see `coef.pffr`
`n3`	see `coef.pffr`
`B`	number of bootstrap replicates, defaults to (a measly) 100
`ncpus`	see `boot`. Defaults to `getOption("boot.ncpus", 1L)` (like `boot`).
`parallel`	see `boot`
`cl`	see `boot`
`conf`	desired levels of bootstrap CIs, defaults to 0.90 and 0.95
`type`	type of bootstrap interval, see `boot.ci`. Defaults to "percent" for percentile-based CIs.
`method`	either "resample" (default) to resample response trajectories, or "residual" to resample responses as fitted values plus residual trajectories or "residual.c" to resample responses as fitted values plus residual trajectories that are centered at zero for each gridpoint.
`showProgress`	TRUE/FALSE
`...`	not used

Value

a list with similar structure as the return value of coef.pffr, containing the original point estimates of the various terms along with their bootstrap CIs.

Author(s)

Fabian Scheipl

refund

Regression with Functional Data

v0.1-23

GPL (>= 2)

Authors

Jeff Goldsmith [aut], Fabian Scheipl [aut], Lei Huang [aut], Julia Wrobel [aut, cre], Chongzhi Di [aut], Jonathan Gellar [aut], Jaroslaw Harezlak [aut], Mathew W. McLean [aut], Bruce Swihart [aut], Luo Xiao [aut], Ciprian Crainiceanu [aut], Philip T. Reiss [aut], Yakuan Chen [ctb], Sonja Greven [ctb], Lan Huo [ctb], Madan Gopal Kundu [ctb], So Young Park [ctb], David L. Miller [ctb], Ana-Maria Staicu [ctb]

Initial release

2020-12-03