Package 'ib' reference manual

Title:	Bias Correction via Iterative Bootstrap
Description:	An implementation of the iterative bootstrap procedure of Kuk (1995) <doi:10.1111/j.2517-6161.1995.tb02035.x> to correct the estimation bias of a fitted model object. This procedure has better bias correction properties than the bootstrap bias correction technique.
Authors:	Samuel Orso [aut, cre], Stéphane Guerrier [ctb], Yuming Zhang [ctb]
Maintainer:	Samuel Orso <[email protected]>
License:	GPL (>= 2)
Version:	0.2.0
Built:	2024-11-03 04:05:51 UTC
Source:	https://github.com/smac-group/ib

Parametric bootstrap

Description

Method for generating parametric bootstrap estimates from a fitted model.

Usage

bootstrap(object, B = 1000, extra_param = FALSE, ...)
bootstrap(object, B = 1000, extra_param = FALSE, ...)

Arguments

`object`	an `object` representing a fitted model (see 'Details').
`B`	an `integer` for number of bootstrap replicates (default 1,000).
`extra_param`	if `TRUE`, bootstrap is also performed for extra parameters (see 'Details').
`...`	additional optional arguments to pass to `ibControl`.

Details

This method is a simple wrapper around the ib method where number of iterations is set to 1.

Value

A matrix p (size of parameter) times B of bootstrapped estimates.

Author(s)

Samuel Orso

Method for extracting coefficients from an object in class union "Ib"

Description

Method for extracting coefficients from an object in class union "Ib"

Usage

## S4 method for signature 'Ib'
coef(object, ...)
## S4 method for signature 'Ib'
coef(object, ...)

Arguments

`object`	an object of class union "Ib"
`...`	further arguments to pass to `coef`

Method for extracting effects from an object in class union "Ib"

Description

Method for extracting effects from an object in class union "Ib"

Usage

## S4 method for signature 'Ib'
effects(object, ...)
## S4 method for signature 'Ib'
effects(object, ...)

Arguments

`object`	an object of class union "Ib"
`...`	further arguments to pass to `effects`

Method for extracting fitted values from an object in class union "Ib"

Description

Method for extracting fitted values from an object in class union "Ib"

Usage

## S4 method for signature 'Ib'
fitted(object, ...)
## S4 method for signature 'Ib'
fitted(object, ...)

Arguments

`object`	an object of class union "Ib"
`...`	further arguments to pass to `fitted`

Accessor to the object in class union "Ib"

Description

Method for obtaining estimates from fitted model within any object of class union Ib.

Usage

getEst(x)

## S4 method for signature 'Ib'
getEst(x)
getEst(x)

## S4 method for signature 'Ib'
getEst(x)

Arguments

`x`	an object of class union "Ib"

Details

This methods allow to access extra parameter estimates. If extra_param=TRUE, it becomes equivalent to coef.

Value

an estimate (as in getExtra).

Accessor to an extra part in class union "Ib"

Description

Method for obtaining a extra values generated by the iterative bootstrap procedure within any object of class union Ib.

Usage

getExtra(x)

## S4 method for signature 'Ib'
getExtra(x)
getExtra(x)

## S4 method for signature 'Ib'
getExtra(x)

Arguments

`x`	an object of class union "Ib"

Value

a list with the following components:

iteration	number of iterations ( $k$ )
of	value of the objective function $\|\|\hat{\pi}-\frac{1}{H}\sum_{h=1}^H\hat{\pi}_h(\hat{\theta}^{k})\|\|$
estimate	value of the estimates $\hat{\theta}^{k}$
test_theta	value for difference of thetas: $\|\|\hat{\theta}^k-\hat{\theta}^{k-1}\|\|$
ib_warn	optional warning message
boot	`matrix` of $H$ bootstrap estimates: $\hat{\pi}(\hat{\theta}^k)$

Accessor to the object in class union "Ib"

Description

Method for obtaining the number of iteration from fitted model within any object of class union Ib.

Usage

getIteration(x)

## S4 method for signature 'Ib'
getIteration(x)
getIteration(x)

## S4 method for signature 'Ib'
getIteration(x)

Arguments

`x`	an object of class union "Ib"

Details

This methods allow to access extra information about the number of iterations.

Value

a number of iterations (as in getExtra).

Accessor to the object in class union "Ib"

Description

Method for obtaining a fitted model within any object of class union Ib.

Usage

getObject(x)

## S4 method for signature 'Ib'
getObject(x)
getObject(x)

## S4 method for signature 'Ib'
getObject(x)

Arguments

`x`	an object of class union "Ib"

Bias correction via iterative bootstrap

Description

ib is used to correct the bias of a fitted model object with the iterative bootstrap procedure.

Usage

ib(object, thetastart = NULL, control = list(...), extra_param = FALSE, ...)

## S4 method for signature 'betareg'
ib(object, thetastart = NULL, control = list(...), extra_param = FALSE, ...)

## S4 method for signature 'glm'
ib(object, thetastart = NULL, control = list(...), extra_param = FALSE, ...)

## S4 method for signature 'lm'
ib(object, thetastart = NULL, control = list(...), extra_param = FALSE, ...)

## S4 method for signature 'lmerMod'
ib(object, thetastart = NULL, control = list(...), extra_param = FALSE, ...)

## S4 method for signature 'nls'
ib(object, thetastart = NULL, control = list(...), extra_param = FALSE, ...)

## S4 method for signature 'vglm'
ib(object, thetastart = NULL, control = list(...), extra_param = FALSE, ...)
ib(object, thetastart = NULL, control = list(...), extra_param = FALSE, ...)

## S4 method for signature 'betareg'
ib(object, thetastart = NULL, control = list(...), extra_param = FALSE, ...)

## S4 method for signature 'glm'
ib(object, thetastart = NULL, control = list(...), extra_param = FALSE, ...)

## S4 method for signature 'lm'
ib(object, thetastart = NULL, control = list(...), extra_param = FALSE, ...)

## S4 method for signature 'lmerMod'
ib(object, thetastart = NULL, control = list(...), extra_param = FALSE, ...)

## S4 method for signature 'nls'
ib(object, thetastart = NULL, control = list(...), extra_param = FALSE, ...)

## S4 method for signature 'vglm'
ib(object, thetastart = NULL, control = list(...), extra_param = FALSE, ...)

Arguments

`object`	an `object` representing a fitted model (see 'Details').
`thetastart`	an optional starting value for the iterative procedure. If `NULL` (default), the procedure starts at the estimates in `object`.
`control`	a `list` of parameters for controlling the iterative procedure (see `ibControl`).
`extra_param`	if `TRUE`, the bias of estimation of extra parameters is performed (see 'Details').
`...`	additional optional arguments (see 'Details').

Details

The iterative bootstrap procedure is described in Kuk (1995) and further studied by Guerrier et al. (2019) and Guerrier et al. (2020). The kth iteration of this algorithm is

$\hat{\theta}^{k} = \hat{\theta}^{k-1} + \hat{\pi} - \frac{1}{H}\sum_{h=1}^H\hat{\pi}_h(\hat{\theta}^{k-1})$

for $k=1,2,\ldots$ and where the sum is over $h=1,\ldots,H$ . The estimate $\hat{\pi}$ is provided by the object. The value $\hat{\pi}_h(\hat{\theta})$ is a parametric bootstrap estimate where the bootstrap sample is generated from $\hat{\theta}$ and a fixed seed (see ibControl). The greater the parameter value $H$ generally the better bias correction but the more computation it requires (see ibControl). If thetastart=NULL, the initial value of the procedure is $\hat{\theta}^{0}=\hat{\pi}$ . The number of iterations are controlled by maxit parameter of ibControl.

By default, the method correct coefficients only. For extra parameters, it depends on the model. These extra parameters may have some constraints (e.g. positivity). If constraint=TRUE (see ibControl), then a transformation from the constraint space to the real is used for the update.

For betareg, extra_param is not available as by default mean and precision parameters are corrected. Currently the 'identity' link function is not supported for precision parameters.

For glm, if extra_param=TRUE: the shape parameter for the Gamma, the variance of the residuals in lm or the overdispersion parameter of the negative binomial regression in glm.nb, are also corrected. Note that the quasi families are not supported for the moment as they have no simulation method (see simulate). Bias correction for extra parameters of the inverse.gaussian is not yet implemented.

For lm, if extra_param=TRUE: the variance of the residuals is also corrected. Note that using the ib is not useful as coefficients are already unbiased, unless one considers different data generating mechanism such as censoring, missing values and outliers (see ibControl).

For lmer, by default, only the fixed effects are corrected. If extra_param=TRUE: all the random effects (variances and correlations) and the variance of the residuals are also corrected. Note that using the ib is certainly not useful with the argument REML=TRUE in lmer as the bias of variance components is already addressed, unless one considers different data generating mechanism such as censoring, missing values and outliers (see ibControl).

For nls, if extra_param=TRUE: the variance of the residuals is also corrected.

For vglm, extra_param is currently not used. Indeed, the philosophy of a vector generalized linear model is to potentially model all parameters of a distribution with a linear predictor. Hence, what would be considered as an extra parameter in glm for instance, may already be captured by the default coefficients. However, correcting the bias of a coefficients does not imply that the bias of the parameter of the distribution is corrected (by Jensen's inequality), so we may use this feature in a future version of the package. Note that we currently only support distributions with a simslot (see simulate.vlm).

Value

A fitted model object of class Ib.

Author(s)

Samuel Orso

References

Guerrier S, Dupuis-Lozeron E, Ma Y, Victoria-Feser M (2019). “Simulation-Based Bias Correction Methods for Complex Models.” Journal of the American Statistical Association, 114(525), 146-157. doi:10.1080/01621459.2017.1380031, https://doi.org/10.1080/01621459.2017.1380031.

Guerrier S, Karemera M, Orso S, Victoria-Feser M, Zhang Y (2020). “A General Approach for Simulation-based Bias Correction in High Dimensional Settings.” https://arxiv.org/pdf/2010.13687.pdf. Version 2: 13 Nov 2020, 2010.13687, https://arxiv.org/pdf/2010.13687.pdf.

Kuk AYC (1995). “Asymptotically Unbiased Estimation in Generalized Linear Models with Random Effects.” Journal of the Royal Statistical Society: Series B (Methodological), 57(2), 395-407. doi:10.1111/j.2517-6161.1995.tb02035.x, https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.2517-6161.1995.tb02035.x, https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/j.2517-6161.1995.tb02035.x.

Examples


## beta regression
library(betareg)
data("GasolineYield", package = "betareg")
## currently link.phi = "identity" is not supported
## fit_beta <- betareg(yield ~ batch + temp, data = GasolineYield)
fit_beta <- betareg(yield ~ batch + temp, link.phi = "log", data = GasolineYield)
fit_ib <- ib(fit_beta)

# precision parameter can also depend on covariates
fit_beta <- betareg(yield ~ batch + temp | temp, data = GasolineYield)
fit_ib <- ib(fit_beta)
## poisson regression
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
pois_fit <- glm(counts ~ outcome + treatment, family = poisson())
fit_ib <- ib(pois_fit)
summary(fit_ib)
## Set H = 1000
## Not run: 
fit_ib <- ib(pois_fit, control=list(H=1000))
summary(fit_ib)

## End(Not run)

## gamma regression
clotting <- data.frame(
  u = c(5,10,15,20,30,40,60,80,100),
  lot1 = c(118,58,42,35,27,25,21,19,18),
  lot2 = c(69,35,26,21,18,16,13,12,12))
fit_gamma <- glm(lot2 ~ log(u), data = clotting, family = Gamma(link = "inverse"))
fit_ib <- ib(fit_gamma)
## summary(fit_ib)
## correct for shape parameter and show iterations
## Not run: 
fit_ib <- ib(fit_gamma, control=list(verbose=TRUE), extra_param = TRUE)
summary(fit_ib)

## End(Not run)

## negative binomial regression
library(MASS)
fit_nb <- glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine)
fit_ib <- ib(fit_nb)
## summary(fit_ib)
## correct for overdispersion with H=100
## Not run: 
fit_ib <- ib(fit_nb, control=list(H=100), extra_param = TRUE)
summary(fit_ib)

## End(Not run)

## linear regression
fit_lm <- lm(disp ~ cyl + hp + wt, data = mtcars)
fit_ib <- ib(fit_lm)
summary(fit_ib)
## correct for variance of residuals
fit_ib <- ib(fit_lm, extra_param = TRUE)
summary(fit_ib)

## linear mixed-effects regression
library(lme4)
fit_lmm <- lmer(Reaction ~ Days + (Days | Subject), data = sleepstudy, REML = FALSE)
fit_ib <- ib(fit_lmm)
summary(fit_ib)
## correct for variances and correlation
## Not run: 
fit_ib <- ib(fit_lmm, extra_param = TRUE)
summary(fit_ib)

## End(Not run)

## nonlinear regression
DNase1 <- subset(DNase, Run == 1)
fit_nls <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), data = DNase1)
fit_ib <- ib(fit_nls)
summary(fit_ib)

## student regression
library(VGAM)
tdata <- data.frame(x = runif(nn <- 1000))
tdata <- transform(tdata,
                   y = rt(nn, df = exp(exp(0.5 - x))))
fit_vglm <- vglm(y ~ x, studentt3, data = tdata)
fit_ib <- ib(fit_vglm)
summary(fit_ib)
## beta regression
library(betareg)
data("GasolineYield", package = "betareg")
## currently link.phi = "identity" is not supported
## fit_beta <- betareg(yield ~ batch + temp, data = GasolineYield)
fit_beta <- betareg(yield ~ batch + temp, link.phi = "log", data = GasolineYield)
fit_ib <- ib(fit_beta)

# precision parameter can also depend on covariates
fit_beta <- betareg(yield ~ batch + temp | temp, data = GasolineYield)
fit_ib <- ib(fit_beta)
## poisson regression
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
pois_fit <- glm(counts ~ outcome + treatment, family = poisson())
fit_ib <- ib(pois_fit)
summary(fit_ib)
## Set H = 1000
## Not run: 
fit_ib <- ib(pois_fit, control=list(H=1000))
summary(fit_ib)

## End(Not run)

## gamma regression
clotting <- data.frame(
  u = c(5,10,15,20,30,40,60,80,100),
  lot1 = c(118,58,42,35,27,25,21,19,18),
  lot2 = c(69,35,26,21,18,16,13,12,12))
fit_gamma <- glm(lot2 ~ log(u), data = clotting, family = Gamma(link = "inverse"))
fit_ib <- ib(fit_gamma)
## summary(fit_ib)
## correct for shape parameter and show iterations
## Not run: 
fit_ib <- ib(fit_gamma, control=list(verbose=TRUE), extra_param = TRUE)
summary(fit_ib)

## End(Not run)

## negative binomial regression
library(MASS)
fit_nb <- glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine)
fit_ib <- ib(fit_nb)
## summary(fit_ib)
## correct for overdispersion with H=100
## Not run: 
fit_ib <- ib(fit_nb, control=list(H=100), extra_param = TRUE)
summary(fit_ib)

## End(Not run)

## linear regression
fit_lm <- lm(disp ~ cyl + hp + wt, data = mtcars)
fit_ib <- ib(fit_lm)
summary(fit_ib)
## correct for variance of residuals
fit_ib <- ib(fit_lm, extra_param = TRUE)
summary(fit_ib)

## linear mixed-effects regression
library(lme4)
fit_lmm <- lmer(Reaction ~ Days + (Days | Subject), data = sleepstudy, REML = FALSE)
fit_ib <- ib(fit_lmm)
summary(fit_ib)
## correct for variances and correlation
## Not run: 
fit_ib <- ib(fit_lmm, extra_param = TRUE)
summary(fit_ib)

## End(Not run)

## nonlinear regression
DNase1 <- subset(DNase, Run == 1)
fit_nls <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), data = DNase1)
fit_ib <- ib(fit_nls)
summary(fit_ib)

## student regression
library(VGAM)
tdata <- data.frame(x = runif(nn <- 1000))
tdata <- transform(tdata,
                   y = rt(nn, df = exp(exp(0.5 - x))))
fit_vglm <- vglm(y ~ x, studentt3, data = tdata)
fit_ib <- ib(fit_vglm)
summary(fit_ib)

`ib` method for `negbin` object from `glm.nb` function of MASS package.

Description

ib method for negbin object from glm.nb function of MASS package.

Usage

## S4 method for signature 'negbin'
ib(object, thetastart = NULL, control = list(...), extra_param = FALSE, ...)
## S4 method for signature 'negbin'
ib(object, thetastart = NULL, control = list(...), extra_param = FALSE, ...)

Arguments

`object`	an `object` representing a fitted model (see 'Details').
`thetastart`	an optional starting value for the iterative procedure. If `NULL` (default), the procedure starts at the estimates in `object`.
`control`	a `list` of parameters for controlling the iterative procedure (see `ibControl`).
`extra_param`	if `TRUE`, the bias of estimation of extra parameters is performed (see 'Details').
`...`	additional optional arguments (see 'Details').

An S4 class union for `ib`

Description

Members of the union are IbBetareg, IbGlm, IbLm, IbLmer, IbNegbin, IbNls, IbVglm

Details

The 'Functions' section describes members of the class union.

Value

Each member of the union has a slot with the initial object corrected by the ib (see getObject) and a second slot with extra meta data from ib (see getExtra).

Functions

IbBetareg-class: fitted model by betareg from betareg
IbGlm-class: fitted model by glm from stats
IbLm-class: fitted model by lm from stats
IbLmer-class: fitted model by lmer from lme4
IbNegbin-class: fitted model by glm.nb from MASS
IbNls-class: fitted model by nls from stats
IbVglm-class: fitted model by vglm from VGAM

Author(s)

Samuel Orso

Auxiliary for controlling IB

Description

Auxiliary function for ib bias correction.

Usage

ibControl(
  tol = 1e-05,
  maxit = 25,
  verbose = FALSE,
  seed = 123L,
  H = 1L,
  constraint = TRUE,
  early_stop = FALSE,
  cens = FALSE,
  right = NULL,
  left = NULL,
  mis = FALSE,
  prop = NULL,
  out = FALSE,
  eps = NULL,
  G = NULL,
  func = function(x) rowMeans(x, na.rm = T),
  sim = NULL
)
ibControl(
  tol = 1e-05,
  maxit = 25,
  verbose = FALSE,
  seed = 123L,
  H = 1L,
  constraint = TRUE,
  early_stop = FALSE,
  cens = FALSE,
  right = NULL,
  left = NULL,
  mis = FALSE,
  prop = NULL,
  out = FALSE,
  eps = NULL,
  G = NULL,
  func = function(x) rowMeans(x, na.rm = T),
  sim = NULL
)

Arguments

`tol`	positive convergence tolerance $\epsilon$ . The `ib` procedure converges when $\|\|\hat{\theta}^{k+1}-\hat{\theta}^k\|\|_2/p<\epsilon$ , where $p$ is the dimension of $\theta$ .
`maxit`	`integer` representing the maximal number of iterations.
`verbose`	if `TRUE`, it prints some output in the console at each iteration.
`seed`	`integer` to set the seed (see `Random`).
`H`	`integer` representing the number of bootstrap estimates (see `ib`).
`constraint`	if `TRUE` (default), constraint for `extra_param` is used in the iterative procedure (see 'Details' of `ib`).
`early_stop`	if `TRUE` (default is `FALSE`), the iterative procedure stops as soon as there is no improvment in the minimization of the objective function (see 'Details' of `ib`).
`cens`	if `TRUE` the simulated responses are censored according to `left` and `right` values.
`right`	`double` for right-censoring (only used if `cens=TRUE`).
`left`	`double` for left-censoring (only used if `cens=TRUE`).
`mis`	if `TRUE` the simulated responses have missing data at random.
`prop`	`double` between 0 and 1 representing the proportion of missing data (only used if `mis=TRUE`).
`out`	if `TRUE` the simulated responses are also generated with a contamination mechanism.
`eps`	`double` between 0 and 1 representing the proportion of outliers in the data (only used if `out=TRUE`).
`G`	a `function` to generate outliers. It takes only a sample size as argument.
`func`	a `function` to reduce the `H` bootstrap estimates (rowwise). By default, the average is computed. The user can supply a function. One could imagine using other function such as the median or a trimmed mean.
`sim`	a user-defined function for simulating responses (see 'Details')

Details

sim allows the user to provide its own function for generating responses. Currently it is only supported for generalized linear models with the prototype 'fun(object, control, extra_param, ...)' (see ib).

Value

a list with components named as the arguments.

Method for plotting an object in class union "Ib"

Description

Method for plotting an object in class union "Ib"

Usage

## S4 method for signature 'Ib,ANY'
plot(x, y = NULL, ...)
## S4 method for signature 'Ib,ANY'
plot(x, y = NULL, ...)

Arguments

`x`	an object of class union "Ib"
`y`	not used
`...`	further arguments to pass to `plot`

Method for making predictions from an object in class union "Ib"

Description

Method for making predictions from an object in class union "Ib"

Usage

## S4 method for signature 'Ib'
predict(object, ...)
## S4 method for signature 'Ib'
predict(object, ...)

Arguments

`object`	an object of class union "Ib"
`...`	further arguments to pass to `predict`

Method for extracting residuals from an object in class union "Ib"

Description

Method for extracting residuals from an object in class union "Ib"

Usage

## S4 method for signature 'Ib'
residuals(object, ...)
## S4 method for signature 'Ib'
residuals(object, ...)

Arguments

`object`	an object of class union "Ib"
`...`	further arguments to pass to `residuals`

Method for printing object in class union "Ib"

Description

Method for printing object in class union "Ib"

Usage

## S4 method for signature 'Ib'
show(object)
## S4 method for signature 'Ib'
show(object)

Arguments

object

an object of class union "Ib"

Summarizing a fitted model corrected by the ib procedure

Description

Method for printing a summary of class union SummaryIb.

Usage

## S4 method for signature 'SummaryIb'
show(object)
## S4 method for signature 'SummaryIb'
show(object)

Arguments

object

a summary object of member of SummaryIb

Generic for simulating from the object

Description

Method for simulating responses from an object.

Usage

simulation(object, control = list(...), ...)

## S4 method for signature 'Ib'
simulation(object, control = list(...), ...)
simulation(object, control = list(...), ...)

## S4 method for signature 'Ib'
simulation(object, control = list(...), ...)

Arguments

`object`	an object of class union "Ib"
`control`	a control list
`...`	further argument to pass

Value

simulated responses.

Examples


## bootstrap poisson regression
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
pois_fit <- glm(counts ~ outcome + treatment, family = poisson())

## make 100 paramtric bootstrap replicates
boot_dist <- simulate(pois_fit, nsim = 100)
## bootstrap poisson regression
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
pois_fit <- glm(counts ~ outcome + treatment, family = poisson())

## make 100 paramtric bootstrap replicates
boot_dist <- simulate(pois_fit, nsim = 100)

Simulation for a beta regression

Description

simulation method for class IbBetareg

Usage

## S4 method for signature 'betareg'
simulation(object, control = list(...), extra = NULL, ...)
## S4 method for signature 'betareg'
simulation(object, control = list(...), extra = NULL, ...)

Arguments

`object`	an object of class IbBetareg
`control`	a `list` of parameters for controlling the iterative procedure (see `ibControl`).
`extra`	`NULL` by default; extra parameters to pass to simulation.
`...`	further arguments

Simulation for a Generalized Linear Model regression

Description

simulation method for class IbGlm

Usage

## S4 method for signature 'glm'
simulation(object, control = list(...), extra = NULL, ...)
## S4 method for signature 'glm'
simulation(object, control = list(...), extra = NULL, ...)

Arguments

`object`	an object of class IbGlm
`control`	a `list` of parameters for controlling the iterative procedure (see `ibControl`).
`extra`	`NULL` by default; extra parameters to pass to simulation.
`...`	further arguments

Simulation for linear regression

Description

simulation method for class IbLm

Usage

## S4 method for signature 'lm'
simulation(object, control = list(...), std = NULL, ...)
## S4 method for signature 'lm'
simulation(object, control = list(...), std = NULL, ...)

Arguments

`object`	an object of class IbLm
`control`	a `list` of parameters for controlling the iterative procedure (see `ibControl`).
`std`	`NULL` by default; standard deviation to pass to simulation.
`...`	further arguments

Simulation for linear mixed model regression

Description

simulation method for class IbLmer

Usage

## S4 method for signature 'lmerMod'
simulation(object, control = list(...), ...)
## S4 method for signature 'lmerMod'
simulation(object, control = list(...), ...)

Arguments

`object`	an object of class IbLmer
`control`	a `list` of parameters for controlling the iterative procedure (see `ibControl`).
`...`	further arguments.

Simulation for a negative binomial regression

Description

simulation method for class IbNegbin

Usage

## S4 method for signature 'negbin'
simulation(object, control = list(...), extra = NULL, ...)
## S4 method for signature 'negbin'
simulation(object, control = list(...), extra = NULL, ...)

Arguments

`object`	an object of class IbNegbin
`control`	a `list` of parameters for controlling the iterative procedure (see `ibControl`).
`extra`	`NULL` by default; extra parameters to pass to simulation.
`...`	further arguments

Simulation for nonlinear regression

Description

simulation method for class IbNls

Usage

## S4 method for signature 'nls'
simulation(object, control = list(...), std = NULL, ...)
## S4 method for signature 'nls'
simulation(object, control = list(...), std = NULL, ...)

Arguments

`object`	an object of class IbNls
`control`	a `list` of parameters for controlling the iterative procedure (see `ibControl`).
`std`	`NULL` by default; standard deviation to pass to simulation.
`...`	further arguments

Simulation for vector generalized linear model regression

Description

simulation method for class IbVglm

Usage

## S4 method for signature 'vglm'
simulation(object, control = list(...), extra_param = NULL, ...)
## S4 method for signature 'vglm'
simulation(object, control = list(...), extra_param = NULL, ...)

Arguments

`object`	an object of class IbVglm
`control`	a `list` of parameters for controlling the iterative procedure (see `ibControl`).
`extra_param`	`NULL` by default; extra parameters to pass to simulation.
`...`	further arguments

Summarizing a beta regression fit corrected by the iterative bootstrap

Description

summary method for class IbBetareg

Usage

## S4 method for signature 'IbBetareg'
summary(object, ...)
## S4 method for signature 'IbBetareg'
summary(object, ...)

Arguments

`object`	an object of class IbBetareg
`...`	further arguments passed to `summary.betareg`

Summarizing a Generalized Linear Model regression fit corrected by the iterative bootstrap

Description

summary method for class IbGlm

Usage

## S4 method for signature 'IbGlm'
summary(object, ...)
## S4 method for signature 'IbGlm'
summary(object, ...)

Arguments

`object`	an object of class IbGlm
`...`	further arguments passed to `summary.glm`

Summarizing a linear regression fit corrected by the iterative bootstrap

Description

summary method for class IbLm

Usage

## S4 method for signature 'IbLm'
summary(object, ...)
## S4 method for signature 'IbLm'
summary(object, ...)

Arguments

`object`	an object of class IbLm
`...`	further arguments passed to `summary.lm`

Summarizing a linear mixed model regression fit corrected by the iterative bootstrap

Description

summary method for class IbLmer

Usage

## S4 method for signature 'IbLmer'
summary(object, ...)
## S4 method for signature 'IbLmer'
summary(object, ...)

Arguments

`object`	an object of class IbLmer
`...`	further arguments passed to `summary.merMod` of lme4

Summarizing a negative binomial regression fits corrected by the iterative bootstrap

Description

summary method for class IbNegbin

Usage

## S4 method for signature 'IbNegbin'
summary(object, ...)
## S4 method for signature 'IbNegbin'
summary(object, ...)

Arguments

`object`	an object of class IbNegbin
`...`	further arguments passed to `summary.negbin`

Summarizing a nonlinear regression fit corrected by the iterative bootstrap

Description

summary method for class IbNls

Usage

## S4 method for signature 'IbNls'
summary(object, ...)
## S4 method for signature 'IbNls'
summary(object, ...)

Arguments

`object`	an object of class IbNls
`...`	further arguments passed to `summary.nls` of stats

Summarizing a vector generalized linear model regression fit corrected by the iterative bootstrap

Description

summary method for class IbVglm

Usage

## S4 method for signature 'IbVglm'
summary(object, ...)
## S4 method for signature 'IbVglm'
summary(object, ...)

Arguments

`object`	an object of class IbVglm
`...`	further arguments passed to `summary.merMod` of VGAM

An S4 class union for `summary`

Description

Members of the union are SummaryIbBetareg, SummaryIbGlm, SummaryIbLm, SummaryIbLmer, SummaryIbNegbin, SummaryIbNls, SummaryIbVglm iterative bootstrap procedure

Details

The 'Functions' section describes members of the class union.

Functions

SummaryIbBetareg-class: summary of class summary.betareg from betareg
SummaryIbGlm-class: summary of class summary.glm from stats
SummaryIbLm-class: summary of class summary.lm from stats
SummaryIbLmer-class: summary of class summary.merMod from lme4
SummaryIbNegbin-class: summary of class summary.negbin from MASS
SummaryIbNls-class: summary of class summary.nls from stats
SummaryIbVglm-class: summary of class summary.vglm from VGAM

Author(s)

Samuel Orso

Method for calculating covariance matrix from an object in class union "Ib"

Description

Method for calculating covariance matrix from an object in class union "Ib"

Usage

## S4 method for signature 'Ib'
vcov(object, ...)
## S4 method for signature 'Ib'
vcov(object, ...)

Arguments

`object`	an object of class union "Ib"
`...`	further arguments to pass to `vcov`

Package 'ib'

Help Index

Parametric bootstrap

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Method for extracting coefficients from an object in class union "Ib"

Description

Usage

Arguments

See Also

Method for extracting effects from an object in class union "Ib"

Description

Usage

Arguments

See Also

Method for extracting fitted values from an object in class union "Ib"

Description

Usage

Arguments

See Also

Accessor to the object in class union "Ib"

Description

Usage

Arguments

Details

Value

See Also

Accessor to an extra part in class union "Ib"

Description

Usage

Arguments

Value

See Also

Accessor to the object in class union "Ib"

Description

Usage

Arguments

Details

Value

See Also

Accessor to the object in class union "Ib"

Description

Usage

Arguments

See Also

Bias correction via iterative bootstrap

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

ib method for negbin object from glm.nb function of MASS package.

Description

Usage

Arguments

An S4 class union for ib

Description

Details

Value

Functions

Author(s)

See Also

Auxiliary for controlling IB

Description

Usage

Arguments

Details

Value

See Also

Method for plotting an object in class union "Ib"

Description

`ib` method for `negbin` object from `glm.nb` function of MASS package.

An S4 class union for `ib`