| 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: | 2024-09-08 06:29:34 UTC |
| Source: | CRAN |
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).
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", ...)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", ...)
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. |
Fit a local polynomial regression with automatic smoothing parameter selection. The predictor x can either one-dimensional or two-dimensional.
a list of a vector of the parametric estimates and an object of class “loess”.
X.F. Wang [email protected]
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.
## 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 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. Two methods are available for the selection of the smoothing parameter: bias-corrected Akaike information criterion (aicc); and generalized cross-validation (gcv).
loess.as(x, y, degree = 1, criterion = c("aicc", "gcv"), family = c("gaussian", "symmetric"), user.span = NULL, plot = FALSE, ...)loess.as(x, y, degree = 1, criterion = c("aicc", "gcv"), family = c("gaussian", "symmetric"), user.span = NULL, plot = FALSE, ...)
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. |
Fit a local polynomial regression with automatic smoothing parameter selection. The predictor x can either one-dimensional or two-dimensional.
An object of class “loess”.
X.F. Wang [email protected]
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.
loess, loess.ancova, T.L2, T.aov, T.var.
## 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))## 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))
To plot a fANCOVA object
## S3 method for class 'fANCOVA' plot(x, test.statistic=TRUE, main="", n=256, legend.position="topright", ...)## S3 method for class 'fANCOVA' plot(x, test.statistic=TRUE, main="", n=256, legend.position="topright", ...)
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 |
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.
X.F. Wang [email protected]
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.
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, ...)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, ...)
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 |
A wild bootstrap algorithm is applied to test the equality of nonparametric curves or surfaces based on an ANOVA-type statistic.
An object of class “fANCOVA”
X.F. Wang [email protected]
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.
T.L2, T.var, loess.as, loess.ancova.
## 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$group)## Nonparametric test the equality of multiple regression curves ## Simulate data sets n1 <- 100 x1 <- runif(n1,min=0, max=3) sd1 <- 0.2 e1 <- rnorm(n1,sd=sd1) y1 <- sin(2*x1) + e1 n2 <- 100 x2 <- runif(n2, min=0, max=3) sd2 <- 0.25 e2 <- rnorm(n2, sd=sd2) y2 <- sin(2*x2) + 1 + e2 n3 <- 120 x3 <- runif(n3, min=0, max=3) sd3 <- 0.25 e3 <- rnorm(n3, sd=sd3) y3 <- sin(2*x3) + e3 data.bind <- rbind(cbind(x1,y1,1), cbind(x2,y2,2),cbind(x3,y3,3)) data.bind <- data.frame(data.bind) colnames(data.bind)=c('x','y','group') t1 <- T.aov(data.bind$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. 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.
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, ...)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, ...)
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. |
A wild bootstrap algorithm is applied to test the equality of nonparametric curves or surfaces based on L2 distance.
An object of class “fANCOVA”.
X.F. Wang [email protected]
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.
T.aov, T.var, loess.as, loess.ancova.
## 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$group, degree=2)## Nonparametric test the equality of multiple regression curves ## Simulate data sets n1 <- 100 x1 <- runif(n1,min=0, max=3) sd1 <- 0.2 e1 <- rnorm(n1,sd=sd1) y1 <- sin(2*x1) + e1 n2 <- 100 x2 <- runif(n2, min=0, max=3) sd2 <- 0.25 e2 <- rnorm(n2, sd=sd2) y2 <- sin(2*x2) + 1 + e2 n3 <- 120 x3 <- runif(n3, min=0, max=3) sd3 <- 0.25 e3 <- rnorm(n3, sd=sd3) y3 <- sin(2*x3) + e3 data.bind <- rbind(cbind(x1,y1,1), cbind(x2,y2,2),cbind(x3,y3,3)) data.bind <- data.frame(data.bind) colnames(data.bind)=c('x','y','group') t1 <- T.L2(data.bind$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. 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.
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, ...)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, ...)
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 |
A wild bootstrap algorithm is applied to test the equality of nonparametric curves or surfaces based on variance estimators.
An object of class “fANCOVA”.
X.F. Wang [email protected]
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.
T.L2, T.aov, loess.as, loess.ancova.
## 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$group)## Nonparametric test the equality of multiple regression curves ## Simulate data sets n1 <- 100 x1 <- runif(n1,min=0, max=3) sd1 <- 0.2 e1 <- rnorm(n1,sd=sd1) y1 <- sin(2*x1) + e1 n2 <- 100 x2 <- runif(n2, min=0, max=3) sd2 <- 0.25 e2 <- rnorm(n2, sd=sd2) y2 <- sin(2*x2) + 1 + e2 n3 <- 120 x3 <- runif(n3, min=0, max=3) sd3 <- 0.25 e3 <- rnorm(n3, sd=sd3) y3 <- sin(2*x3) + e3 data.bind <- rbind(cbind(x1,y1,1), cbind(x2,y2,2),cbind(x3,y3,3)) data.bind <- data.frame(data.bind) colnames(data.bind)=c('x','y','group') t1 <- T.var(data.bind$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 by four groups from 1900 to 1979. The four groups are: Age 0; Age 20; Age 40; Age 60.
data(USpopu)data(USpopu)
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 |
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
data(USpopu) t1 <- T.L2(USpopu$year, USpopu$population, USpopu$age, degree=2) t1 plot(t1) plot(t1, test.statistic=FALSE, legend.position="topleft")data(USpopu) t1 <- T.L2(USpopu$year, USpopu$population, USpopu$age, degree=2) t1 plot(t1) plot(t1, test.statistic=FALSE, legend.position="topleft")
Generate 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.
wild.boot(x, nboot = 1)wild.boot(x, nboot = 1)
x |
a vector of regression residuals. |
nboot |
the number of bootstrap replicates. Usually this will be a single positive integer. |
This function is to generate bootstrap residuals using the wild bootstrap method.
a vector or a matrix.
X.F. Wang [email protected]
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.
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])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])