Package 'fANCOVA'

Title: Nonparametric Analysis of Covariance
Description: A collection of R functions to perform nonparametric analysis of covariance for regression curves or surfaces. Testing the equality or parallelism of nonparametric curves or surfaces is equivalent to analysis of variance (ANOVA) or analysis of covariance (ANCOVA) for one-sample functional data. Three different testing methods are available in the package, including one based on L-2 distance, one based on an ANOVA statistic, and one based on variance estimators.
Authors: Xiaofeng Wang [aut, cre], Xinge Ji [ctb]
Maintainer: Xiaofeng Wang <[email protected]>
License: GPL-3
Version: 0.6-1
Built: 2024-12-07 06:38:50 UTC
Source: CRAN

Help Index


Fit a semiparametric ANCOVA model with a local polynomial smoother

Description

Fit a semiparametric ANCOVA model with a local polynomial smoother. The specific model considered here is

y_ij= g_i + m(x_ij) + e_ij,

where the parametric part of the model, g_i, is a factor variable; the nonparametric part of the model, m(.), is a nonparametric smooth function; e_ij are independent identically distributed errors. The errors e_ij do not have to be independent N(0, sigma^2) errors. The errors can be heteroscedastic, i.e., e_ij = sigma_i(x_ij) * u_ij, where u_ij are independent identically distributed errors with mean 0 and variance 1. The model is fitted by the direct estimation method (Speckman, 1988), or by the backfitting method (Buja, Hastie and Tibshirani, 1989; Hastie and Tibshirani, 1990).

Usage

loess.ancova(x, y, group, degree = 2, criterion = c("aicc", "gcv"), 
		family = c("gaussian", "symmetric"), method=c("Speckman", "Backfitting"), 
		iter = 10, tol = 0.01, user.span = NULL, plot = FALSE, 
		data.points = FALSE, legend.position = "topright", ...)

Arguments

x

a vector or two-column matrix of covariate values.

y

a vector of response values.

group

a vector of group indicators that has the same length as y.

degree

the degree of the local polynomials to be used. It can ben 0, 1 or 2.

criterion

the criterion for automatic smoothing parameter selection: “aicc” denotes bias-corrected AIC criterion, “gcv” denotes generalized cross-validation.

family

if “gaussian” fitting is by least-squares, and if “symmetric” a re-descending M estimator is used with Tukey's biweight function.

method

if “Speckman” the direct estimation method by Speckman (1988) will be used, and if “Backfitting” The model is fitted by the backfitting method (Buja, Hastie and Tibshirani, 1989; Hastie and Tibshirani, 1990).

iter

the number of iterations.

tol

the number of tolerance in the iterations.

user.span

the user-defined parameter which controls the degree of smoothing. If it is not specified, the smoothing parameter will be selected by “aicc” or “gcv” criterion.

plot

if TRUE (when x is one-dimensional), the fitted curves for all groups will be generated; if TRUE (when x is two-dimensional), only the smooth component in the model will be plotted.

data.points

if TRUE, the data points will be displayed in the plot.

legend.position

the position of legend in the plot: “topright”, “topleft”, “bottomright”, “bottomleft”, etc.

...

control parameters.

Details

Fit a local polynomial regression with automatic smoothing parameter selection. The predictor x can either one-dimensional or two-dimensional.

Value

a list of a vector of the parametric estimates and an object of class “loess”.

Author(s)

X.F. Wang [email protected]

References

Speckman, P. (1988). Kernel Smoothing in Partial Linear Models. Journal of the Royal Statistical Society. Series B (Methodological), 50, 413–436.

Buja, A., Hastie, T. J. and Tibshirani, R. J. (1989). Linear smoothers and additive models (with discussion). Annals of Statistics, 17, 453–555.

Hastie, T. J. and Tibshirani, R. J. (1990). Generalized Additive Models. Vol. 43 of Monographs on Statistics and Applied Probability, Chapman and Hall, London.

See Also

loess.

Examples

## Fit semiparametric ANCOVA model
set.seed(555)
n1 <- 80
x1 <- runif(n1,min=0, max=3)
sd1 <- 0.3
e1 <- rnorm(n1,sd=sd1)
y1 <- 3*cos(pi*x1/2)  + 6 + e1

n2 <- 75
x2 <- runif(n2, min=0, max=3)
sd2 <- 0.2
e2 <- rnorm(n2, sd=sd2)
y2 <- 3*cos(pi*x2/2) + 3 + e2

n3 <- 90
x3 <- runif(n3, min=0, max=3)
sd3 <- 0.3
e3 <- rnorm(n3, sd=sd3)
y3 <- 3*cos(pi*x3/2)  + e3

data.bind <- rbind(cbind(x1,y1,1), cbind(x2,y2,2),cbind(x3,y3,3))
data.bind <- data.frame(data.bind)
colnames(data.bind)=c('x','y','group') 

x <- data.bind[,1]
y <- data.bind[,2]
group <- data.bind[,3]

loess.ancova(x,y,group, plot=TRUE, data.points=TRUE)

## Fit semiparametric ANCOVA model when the predictor is two-dimensional
n1 <- 100
x11 <- runif(n1,min=0, max=3)
x12 <- runif(n1,min=0, max=3)
sd1 <- 0.2
e1 <- rnorm(n1,sd=sd1)
y1 <- sin(2*x11) + sin(2*x12)  + e1

n2 <- 100
x21 <- runif(n2, min=0, max=3)
x22 <- runif(n2, min=0, max=3)
sd2 <- 0.25
e2 <- rnorm(n2, sd=sd2)
y2 <- sin(2*x21) + sin(2*x22) + 1 + e2

n3 <- 120
x31 <- runif(n3, min=0, max=3)
x32 <- runif(n3, min=0, max=3)
sd3 <- 0.25
e3 <- rnorm(n3, sd=sd3)
y3 <- sin(2*x31) + sin(2*x32) + 3 + e3

data.bind <- rbind(cbind(x11, x12 ,y1,1), cbind(x21, x22, y2,2),cbind(x31, x32,y3,3))
data.bind <- data.frame(data.bind)
colnames(data.bind)=c('x1','x2', 'y','group') 

loess.ancova(data.bind[,c(1,2)], data.bind$y, data.bind$group, plot=TRUE)

Fit a local polynomial regression with automatic smoothing parameter selection

Description

Fit a local polynomial regression with automatic smoothing parameter selection. Two methods are available for the selection of the smoothing parameter: bias-corrected Akaike information criterion (aicc); and generalized cross-validation (gcv).

Usage

loess.as(x, y, degree = 1, criterion = c("aicc", "gcv"), 
		family = c("gaussian", "symmetric"), user.span = NULL, 
		plot = FALSE, ...)

Arguments

x

a vector or two-column matrix of covariate values.

y

a vector of response values.

degree

the degree of the local polynomials to be used. It can ben 0, 1 or 2.

criterion

the criterion for automatic smoothing parameter selection: “aicc” denotes bias-corrected AIC criterion, “gcv” denotes generalized cross-validation.

family

if “gaussian” fitting is by least-squares, and if “symmetric” a re-descending M estimator is used with Tukey's biweight function.

user.span

the user-defined parameter which controls the degree of smoothing.

plot

if TRUE, the fitted curve or surface will be generated.

...

control parameters.

Details

Fit a local polynomial regression with automatic smoothing parameter selection. The predictor x can either one-dimensional or two-dimensional.

Value

An object of class “loess”.

Author(s)

X.F. Wang [email protected]

References

Cleveland, W. S. (1979) Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association. 74, 829–836.

Hurvich, C.M., Simonoff, J.S., and Tsai, C.L. (1998), Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion. Journal of the Royal Statistical Society B. 60, 271–293.

Golub, G., Heath, M. and Wahba, G. (1979). Generalized cross validation as a method for choosing a good ridge parameter. Technometrics. 21, 215–224.

See Also

loess, loess.ancova, T.L2, T.aov, T.var.

Examples

## Fit Local Polynomial Regression with Automatic Smoothing Parameter Selection
n1 <- 100
x1 <- runif(n1,min=0, max=3)
sd1 <- 0.2
e1 <- rnorm(n1,sd=sd1)
y1 <- sin(2*x1) + e1

(y1.fit <- loess.as(x1, y1, plot=TRUE))

n2 <- 100
x21 <- runif(n2, min=0, max=3)
x22 <- runif(n2, min=0, max=3)
sd2 <- 0.25
e2 <- rnorm(n2, sd=sd2)
y2 <- sin(2*x21) + sin(2*x22) + 1 + e2

(y2.fit <- loess.as(cbind(x21, x22), y2, plot=TRUE))

Plot a fANCOVA Object

Description

To plot a fANCOVA object

Usage

## S3 method for class 'fANCOVA'
plot(x, test.statistic=TRUE, main="", n=256, legend.position="topright", ...)

Arguments

x

a fANCOVA object

test.statistic

if TRUE, plot the density of the test statistic under null hypothesis; if FALSE, plot the estimated curves.

main

the title of the plot

n

the number of points that are used to draw the curves or surfaces in the plot.

legend.position

the position of legend in the plot: “topright”, “topleft”, “bottomright”, “bottomleft”, etc.

...

control parameters of the plot function

Details

This function is to plot a fANCOVA object. The plot will be generated only if the predictor x is one-dimensional. if “test.statistic=TRUE”, a density plot of the test statistic under null hypothesis will be generated; if “test.statistic=FALSE”, the estimated curves for all groups are drawn.

Author(s)

X.F. Wang [email protected]

See Also

T.L2, T.aov, T.var.


Test the equality of nonparametric curves or surfaces based on an ANOVA-type statistic

Description

Test the equality of nonparametric curves or surfaces based on an ANOVA-type statistic. The specific model considered here is

y_ij= m_i(x_ij) + e_ij,

where m_i(.), are nonparametric smooth functions; e_ij are independent identically distributed errors. The errors e_ij do not have to be independent N(0, sigma^2) errors. The errors can be heteroscedastic, i.e., e_ij = sigma_i(x_ij) * u_ij, where u_ij are independent identically distributed errors with mean 0 and variance 1.

We are interested in the problem of testing the equality of the regression curves (when x is one-dimensional) or surfaces (when x is two-dimensional),

H_0: m_1(.) = m_2(.) = ... v.s. H_1: otherwise

The problem can also be viewed as the test of the equality in the one-sample problem for functional data.

Usage

T.aov(x, ...)
## Default S3 method:
T.aov(x, y, group, B = 200, degree = 1, criterion = c("aicc", "gcv"),
		family = c("gaussian", "symmetric"), tstat = c("DN", "YB"),
		user.span = NULL, ...)

Arguments

x

a vector or two-column matrix of covariate values.

y

a vector of response values.

group

a vector of group indicators that has the same length as y.

B

the number of bootstrap replicates. Usually this will be a single positive integer.

degree

the degree of the local polynomials to be used. It can ben 0, 1 or 2.

criterion

the criterion for automatic smoothing parameter selection: “aicc” denotes bias-corrected AIC criterion, “gcv” denotes generalized cross-validation.

family

if “gaussian” fitting is by least-squares, and if “symmetric” a re-descending M estimator is used with Tukey's biweight function.

tstat

the test statistic used here: if “DN” Dette, H., Neumeyer, N. (2001)'s statistic is used; if “YB” Young, S.G. and Bowman, A.W. (1995)'s statistic is used.

user.span

The user-defined parameter which controls the degree of smoothing.

...

some control parameters can also be supplied directly

Details

A wild bootstrap algorithm is applied to test the equality of nonparametric curves or surfaces based on an ANOVA-type statistic.

Value

An object of class “fANCOVA”

Author(s)

X.F. Wang [email protected]

References

Dette, H., Neumeyer, N. (2001). Nonparametric analysis of covariance. Annals of Statistics. 29, 1361–1400.

Young, S.G. and Bowman, A.W. (1995). Nonparametric analysis of covariance. Biometrics. 51, 920–931.

Wang. X.F. and Ye, D. (2010). On nonparametric comparison of images and regression surfaces. Journal of Statistical Planning and Inference. 140, 2875–2884.

See Also

T.L2, T.var, loess.as, loess.ancova.

Examples

## Nonparametric test the equality of multiple regression curves
## Simulate data sets

n1 <- 100
x1 <- runif(n1,min=0, max=3)
sd1 <- 0.2
e1 <- rnorm(n1,sd=sd1)
y1 <- sin(2*x1) + e1

n2 <- 100
x2 <- runif(n2, min=0, max=3)
sd2 <- 0.25
e2 <- rnorm(n2, sd=sd2)
y2 <- sin(2*x2) + 1 + e2

n3 <- 120
x3 <- runif(n3, min=0, max=3)
sd3 <- 0.25
e3 <- rnorm(n3, sd=sd3)
y3 <- sin(2*x3)  + e3

data.bind <- rbind(cbind(x1,y1,1), cbind(x2,y2,2),cbind(x3,y3,3))
data.bind <- data.frame(data.bind)
colnames(data.bind)=c('x','y','group')


t1 <- T.aov(data.bind$x, data.bind$y, data.bind$group)
t1
plot(t1)
plot(t1, test.statistic=FALSE)

########
## Nonparametric test the equality for regression surfaces
## Simulate data sets

n1 <- 100
x11 <- runif(n1,min=0, max=3)
x12 <- runif(n1,min=0, max=3)
sd1 <- 0.2
e1 <- rnorm(n1,sd=sd1)
y1 <- sin(2*x11) + sin(2*x12)  + e1

n2 <- 100
x21 <- runif(n2, min=0, max=3)
x22 <- runif(n2, min=0, max=3)
sd2 <- 0.25
e2 <- rnorm(n2, sd=sd2)
y2 <- sin(2*x21) + sin(2*x22) + 1 + e2

n3 <- 120
x31 <- runif(n3, min=0, max=3)
x32 <- runif(n3, min=0, max=3)
sd3 <- 0.25
e3 <- rnorm(n3, sd=sd3)
y3 <- sin(2*x31) + sin(2*x32) + e3

data.bind <- rbind(cbind(x11, x12 ,y1,1), cbind(x21, x22, y2,2),cbind(x31, x32,y3,3))
data.bind <- data.frame(data.bind)
colnames(data.bind)=c('x1','x2', 'y','group')

T.aov(data.bind[,c(1,2)], data.bind$y, data.bind$group)

Test the equality of nonparametric curves or surfaces based on L2 distance

Description

Test the equality of nonparametric curves or surfaces based on L2 distance. The specific model considered here is

y_ij= m_i(x_ij) + e_ij,

where m_i(.), are nonparametric smooth functions; e_ij are independent identically distributed errors. The errors e_ij do not have to be independent N(0, sigma^2) errors. The errors can be heteroscedastic, i.e., e_ij = sigma_i(x_ij) * u_ij, where u_ij are independent identically distributed errors with mean 0 and variance 1.

We are interested in the problem of testing the equality of the regression curves (when x is one-dimensional) or surfaces (when x is two-dimensional),

H_0: m_1(.) = m_2(.) = ... v.s. H_1: otherwise

The problem can also be viewed as the test of the equality in the one-sample problem for functional data.

Usage

T.L2(x, ...)
## Default S3 method:
T.L2(x, y, group, B = 200, degree = 1, criterion = c("aicc", "gcv"),
		family = c("gaussian", "symmetric"), m = 225, user.span = NULL, ...)

Arguments

x

a vector or two-column matrix of covariate values.

y

a vector of response values.

group

a vector of group indicators that has the same length as y.

B

the number of bootstrap replicates. Usually this will be a single positive integer.

degree

the degree of the local polynomials to be used. It can ben 0, 1 or 2.

criterion

the criterion for automatic smoothing parameter selection: “aicc” denotes bias-corrected AIC criterion, “gcv” denotes generalized cross-validation.

family

if “gaussian” fitting is by least-squares, and if “symmetric” a re-descending M estimator is used with Tukey's biweight function.

m

the number of the sampling points for the Monte-Carlo integration.

user.span

the user-defined parameter which controls the degree of smoothing.

...

some control parameters can also be supplied directly.

Details

A wild bootstrap algorithm is applied to test the equality of nonparametric curves or surfaces based on L2 distance.

Value

An object of class “fANCOVA”.

Author(s)

X.F. Wang [email protected]

References

Dette, H., Neumeyer, N. (2001). Nonparametric analysis of covariance. Annals of Statistics. 29, 1361–1400.

Wang. X.F. and Ye, D. (2010). On nonparametric comparison of images and regression surfaces. Journal of Statistical Planning and Inference. 140, 2875–2884.

See Also

T.aov, T.var, loess.as, loess.ancova.

Examples

## Nonparametric test the equality of multiple regression curves
## Simulate data sets

n1 <- 100
x1 <- runif(n1,min=0, max=3)
sd1 <- 0.2
e1 <- rnorm(n1,sd=sd1)
y1 <- sin(2*x1) + e1

n2 <- 100
x2 <- runif(n2, min=0, max=3)
sd2 <- 0.25
e2 <- rnorm(n2, sd=sd2)
y2 <- sin(2*x2) + 1 + e2

n3 <- 120
x3 <- runif(n3, min=0, max=3)
sd3 <- 0.25
e3 <- rnorm(n3, sd=sd3)
y3 <- sin(2*x3)  + e3

data.bind <- rbind(cbind(x1,y1,1), cbind(x2,y2,2),cbind(x3,y3,3))
data.bind <- data.frame(data.bind)
colnames(data.bind)=c('x','y','group')


t1 <- T.L2(data.bind$x, data.bind$y, data.bind$group, degree=2)
t1
plot(t1)
plot(t1, test.statistic=FALSE)

########
## Nonparametric test the equality for regression surfaces
## Simulate data sets

n1 <- 100
x11 <- runif(n1,min=0, max=3)
x12 <- runif(n1,min=0, max=3)
sd1 <- 0.2
e1 <- rnorm(n1,sd=sd1)
y1 <- sin(2*x11) + sin(2*x12)  + e1

n2 <- 100
x21 <- runif(n2, min=0, max=3)
x22 <- runif(n2, min=0, max=3)
sd2 <- 0.25
e2 <- rnorm(n2, sd=sd2)
y2 <- sin(2*x21) + sin(2*x22) + 1 + e2

n3 <- 120
x31 <- runif(n3, min=0, max=3)
x32 <- runif(n3, min=0, max=3)
sd3 <- 0.25
e3 <- rnorm(n3, sd=sd3)
y3 <- sin(2*x31) + sin(2*x32) + e3

data.bind <- rbind(cbind(x11, x12 ,y1,1), cbind(x21, x22, y2,2),cbind(x31, x32,y3,3))
data.bind <- data.frame(data.bind)
colnames(data.bind)=c('x1','x2', 'y','group')

T.L2(data.bind[,c(1,2)], data.bind$y, data.bind$group, degree=2)

Test the equality of nonparametric curves or surfaces based on variance estimators

Description

Test the equality of nonparametric curves or surfaces based on variance estimators. The specific model considered here is

y_ij= m_i(x_ij) + e_ij,

where m_i(.), are nonparametric smooth functions; e_ij are independent identically distributed errors. The errors e_ij do not have to be independent N(0, sigma^2) errors. The errors can be heteroscedastic, i.e., e_ij = sigma_i(x_ij) * u_ij, where u_ij are independent identically distributed errors with mean 0 and variance 1.

We are interested in the problem of testing the equality of the regression curves (when x is one-dimensional) or surfaces (when x is two-dimensional),

H_0: m_1(.) = m_2(.) = ... v.s. H_1: otherwise

The problem can also be viewed as the test of the equality in the one-sample problem for functional data.

Usage

T.var(x, ...)
## Default S3 method:
T.var(x, y, group, B = 200, degree = 1, criterion = c("aicc", "gcv"),
		family = c("gaussian", "symmetric"), user.span = NULL, ...)

Arguments

x

a vector or two-column matrix of covariate values.

y

a vector of response values.

group

a vector of group indicators that has the same length as y.

B

the number of bootstrap replicates. Usually this will be a single positive integer.

degree

the degree of the local polynomials to be used. It can ben 0, 1 or 2.

criterion

the criterion for automatic smoothing parameter selection: “aicc” denotes bias-corrected AIC criterion, “gcv” denotes generalized cross-validation.

family

if “gaussian” fitting is by least-squares, and if “symmetric” a re-descending M estimator is used with Tukey's biweight function.

user.span

the user-defined parameter which controls the degree of smoothing.

...

some control parameters can also be supplied directly

Details

A wild bootstrap algorithm is applied to test the equality of nonparametric curves or surfaces based on variance estimators.

Value

An object of class “fANCOVA”.

Author(s)

X.F. Wang [email protected]

References

Dette, H., Neumeyer, N. (2001). Nonparametric analysis of covariance. Annals of Statistics. 29, 1361–1400.

Wang. X.F. and Ye, D. (2010). On nonparametric comparison of images and regression surfaces. Journal of Statistical Planning and Inference. 140, 2875–2884.

See Also

T.L2, T.aov, loess.as, loess.ancova.

Examples

## Nonparametric test the equality of multiple regression curves
## Simulate data sets

n1 <- 100
x1 <- runif(n1,min=0, max=3)
sd1 <- 0.2
e1 <- rnorm(n1,sd=sd1)
y1 <- sin(2*x1) + e1

n2 <- 100
x2 <- runif(n2, min=0, max=3)
sd2 <- 0.25
e2 <- rnorm(n2, sd=sd2)
y2 <- sin(2*x2) + 1 + e2

n3 <- 120
x3 <- runif(n3, min=0, max=3)
sd3 <- 0.25
e3 <- rnorm(n3, sd=sd3)
y3 <- sin(2*x3)  + e3

data.bind <- rbind(cbind(x1,y1,1), cbind(x2,y2,2),cbind(x3,y3,3))
data.bind <- data.frame(data.bind)
colnames(data.bind)=c('x','y','group')


t1 <- T.var(data.bind$x, data.bind$y, data.bind$group, degree=2, criterion="gcv")
t1
plot(t1)
plot(t1, test.statistic=FALSE)

########
## Nonparametric test the equality for regression surfaces
## Simulate data sets

n1 <- 100
x11 <- runif(n1,min=0, max=3)
x12 <- runif(n1,min=0, max=3)
sd1 <- 0.2
e1 <- rnorm(n1,sd=sd1)
y1 <- sin(2*x11) + sin(2*x12)  + e1

n2 <- 100
x21 <- runif(n2, min=0, max=3)
x22 <- runif(n2, min=0, max=3)
sd2 <- 0.25
e2 <- rnorm(n2, sd=sd2)
y2 <- sin(2*x21) + sin(2*x22) + 1 + e2

n3 <- 120
x31 <- runif(n3, min=0, max=3)
x32 <- runif(n3, min=0, max=3)
sd3 <- 0.25
e3 <- rnorm(n3, sd=sd3)
y3 <- sin(2*x31) + sin(2*x32) + e3

data.bind <- rbind(cbind(x11, x12 ,y1,1), cbind(x21, x22, y2,2),cbind(x31, x32,y3,3))
data.bind <- data.frame(data.bind)
colnames(data.bind)=c('x1','x2', 'y','group')

T.var(data.bind[,c(1,2)], data.bind$y, data.bind$group)

US national population

Description

US national population by four groups from 1900 to 1979. The four groups are: Age 0; Age 20; Age 40; Age 60.

Usage

data(USpopu)

Format

A data frame with 320 observations on 3 variables.

age numeric the group variable of age
year numeric a numeric vector, giving year
population numeric a numeric vector, giving population in millions

References

https://www.census.gov/data/tables/time-series/demo/popest/pre-1980-national.html, U.S. Census Bureau, National Intercensal Tables: 1900-1990. Last Revised: November 30, 2016

See Also

T.L2, T.aov, T.var.

Examples

data(USpopu)
t1 <- T.L2(USpopu$year, USpopu$population, USpopu$age, degree=2)
t1
plot(t1)
plot(t1, test.statistic=FALSE, legend.position="topleft")

Generate one or multiple bootstrap samples of regression residuals using the wild bootstrap method

Description

Generate bootstrap samples using the wild bootstrap method introduced by Wu (1986). One of the advantages for the wild bootstrap method is that it allows for a heterogeneous variance in the residuals in regression analysis.

Usage

wild.boot(x, nboot = 1)

Arguments

x

a vector of regression residuals.

nboot

the number of bootstrap replicates. Usually this will be a single positive integer.

Details

This function is to generate bootstrap residuals using the wild bootstrap method.

Value

a vector or a matrix.

Author(s)

X.F. Wang [email protected]

References

Wu, C. (1986) Jackknife, bootstrap and other resampling methods in regression analysis (with discussion). Annals of Statistics. 14, 1261–1350.

Mammen, E. (1991). Bootstrap, wild bootstrap, and asymptotic normality. Probability Theory and Related Fields. 93, 439–455.

See Also

T.L2, T.aov, T.var.

Examples

n <- 1000
x <- runif(n, min=0, max=1)
## generate heteroscedastic error variances
sig.x <- sqrt(exp(x)/2.5-0.4)
err <- sapply(sig.x, function(x) rnorm(1, sd=x))
x2 <- x^2
y <- 10+3*x+2*x2 +err
plot(x,y)
fit <- lm(y ~ x + x2)
## obtain 12 samples of the wild bootstrap residuals 
res.boot <- wild.boot(fit$res, nboot=12)
## obtain 12 samples of the wild bootstrap responses
y.boot <- matrix(rep(fit$fit,time=12), ncol=12) + res.boot
## plot the 12 wild bootstrap samples
## The wild bootstrap method keeps the patterns of variance heterogeneity 
## in the orginal sample.
par(mfrow=c(4,3))
for (i in 1:12) plot(x, y.boot[,i])