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:	0.2.1
Built:	2026-06-01 10:59:41 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 'glmerMod'
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 'glmerMod'
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 glmer, by default, only the fixed effects are corrected. If extra_param=TRUE: all the random effects (variances and correlations) are also corrected.

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)

## generalized linear mixed-effects regression
## Not run: 
  library(lme4)
  fit_glmm <- glmer(incidence / size ~ period + (1 | herd), weights = size,
                    family = binomial, data = cbpp)
  fit_ib <- ib(fit_glmm)
  summary(fit_ib)
  ## correct for variances and correlation
  fit_ib <- ib(fit_glmm, 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
## Not run: 
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
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)

## generalized linear mixed-effects regression
## Not run: 
  library(lme4)
  fit_glmm <- glmer(incidence / size ~ period + (1 | herd), weights = size,
                    family = binomial, data = cbpp)
  fit_ib <- ib(fit_glmm)
  summary(fit_ib)
  ## correct for variances and correlation
  fit_ib <- ib(fit_glmm, 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
## Not run: 
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
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)

Fitted-model wrappers for the iterative bootstrap (`ib`)

Description

S4 classes that wrap fitted model objects and attach extra metadata from the iterative bootstrap procedure.

Members: IbBetareg, IbGlm, IbLm, IbLmer, IbGlmer, IbNegbin, IbNls, IbVglm.

Details

Members include wrappers around models from betareg, stats, lme4, MASS, and VGAM.

These classes are light wrappers: they store the corrected fitted object in @object (see getObject) and additional information in @ib_extra (see getExtra).

Value

Objects of the respective S4 classes.

Slots

object: The original fitted model object, corrected by ib.
ib_extra: A list of extra metadata from 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').

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, see Ib

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, see Ib

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, see Ib

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, see Ib

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 mixed model regression

Description

simulation method for class IbGlmer, see Ib

Usage

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

Arguments

object

an object of class IbGlmer, see Ib

control

a list of parameters for controlling the iterative procedure (see ibControl).

...

further arguments.

Simulation for linear regression

Description

simulation method for class IbLm, see Ib

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, see Ib

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 Ib, see Ib

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 Ib, see Ib

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, see Ib

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, see Ib

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, see Ib

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, see Ib

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, see Ib

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, see Ib

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 generalized linear mixed model regression fit corrected by the iterative bootstrap

Description

summary method for class IbGlmer

Usage

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

Arguments

object

an object of class IbGlmer

...

further arguments passed to summary.merMod of lme4

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

Summaries for `ib` wrappers

Description

S4 classes that wrap summary objects corresponding to the Ib* classes.

Members: SummaryIbBetareg, SummaryIbGlm, SummaryIbLm, SummaryIbLmer, SummaryIbGlmer, SummaryIbNegbin, SummaryIbNls, SummaryIbVglm.

Slots

summ: The summary.* object.
ib_extra: A list of extra metadata from ib.

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

Fitted-model wrappers for the iterative bootstrap (ib)

Description

Details

Value

Slots

See Also

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

Description

Usage

Arguments

Auxiliary for controlling IB

Description

Usage

Arguments

Details

Value

See Also

Method for plotting an object in class union "Ib"

Description

Usage

Fitted-model wrappers for the iterative bootstrap (`ib`)

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