Title: Penalized Orthogonal-Components Regression
Version: 0.6.0
Date: 2022-03-15
Author: Dabao Zhang, Zhongli Jiang, Zeyu Zhang, Yu-ting Chen
Maintainer: Dabao Zhang <zhangdb@purdue.edu>
Description: Penalized orthogonal-components regression (POCRE) is a supervised dimension reduction method for high-dimensional data. It sequentially constructs orthogonal components (with selected features) which are maximally correlated to the response residuals. POCRE can also construct common components for multiple responses and thus build up latent-variable models.
Imports: stats,utils,ggplot2 (≥ 2.2.0),pracma,EbayesThresh
License: GPL-2
NeedsCompilation: no
Packaged: 2022-03-16 17:48:42 UTC; admin-zhangdb
Repository: CRAN
Date/Publication: 2022-03-16 18:10:02 UTC

Use k-Fold Cross-Validation to Choose the Tuning Parameter for POCRE

Description

Choose the optimal tuning parameter via k-fold cross-validation for POCRE.

Usage

cvpocre(y, x, n.folds=10, delta=0.1, maxvar=dim(x)[1]/2,
        ptype=c('ebtz','ebt','l1','scad','mcp'), maxit=100,
        maxcmp=10, gamma=3.7, lambda.init=1, tol=1e-6,
        crit=c('press','Pearson','Spearman','Kendall'))

Arguments

y

n*q matrix, values of q response variables (allow for multiple response variables).

x

n*p matrix, values of p predicting variables (excluding the intercept).

n.folds

number of folds to split the data (10-fold CV by default).

delta

step size of different values of the tuning parameter.

maxvar

maximum number of selected variables.

ptype

a character to indicate the type of penalty: 'ebtz' (emprical Bayes thresholding after Fisher's z-transformation, by default), 'ebt' (emprical Bayes thresholding by Johnstone & Silverman (2004)), 'l1' (L_1 penalty), 'scad' (SCAD by Fan & Li (2001)), 'mcp' (MCP by Zhang (2010)).

maxit

maximum number of iterations to be allowed.

maxcmp

maximum number of components to be constructed.

gamma

a parameter used by SCAD and MCP (=3.7 by default).

lambda.init

initial value of the tuning parameter (=1 by default).

tol

tolerance of precision in iterations.

crit

a character to indicate the validation criterion: 'press' (prediction residual error sum of squares, by default), 'Pearson' (Pearson correlation coefficient), 'Spearman' (Spearman's rank correlation coefficient), 'Kendall' (Kendall's rank correlation coefficient).

Details

Use k-folds cross-validation to find the optinal value for the tuning parameter. The validation criterion can be chosen from PRESS, or different types of correlation coefficients, such as Pearson's, Spearman's, or Kendall's.

Value

The optimal value of the tuning parameter.

Author(s)

Dabao Zhang, Zhongli Jiang, Zeyu Zhang, Department of Statistics, Purdue University

References

Fan J and Li R (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96:1348-1360

Johnstone IM and Silverman BW (2004). Needles and straw in haystacks: empirical Bayes estimates of possibly sparse sequences. Annals of Statistics, 32: 1594-1649.

Zhang C-H (2010). Nearly unbiased variable selection under minimax concave penalty. The Annals of Statistics, 38: 894-942.

Zhang D, Lin Y, and Zhang M (2009). Penalized orthogonal-components regression for large p small n data. Electronic Journal of Statistics, 3: 781-796.

See Also

pocrescreen, pocrepath, pocre.

Examples

## Not run: 
data(simdata)
n <- dim(simdata)[1]
xx <- simdata[,-1]
yy <- simdata[,1]

# tp <- cvpocre(yy,xx,delta=0.01)
tp <- cvpocre(yy,xx)
print(paste("  pocre: Optimal Tuning Parameter = ", tp))
cvpres <- pocre(yy,xx,lambda=tp,maxvar=n/log(n))

## End(Not run)

Screen Variables for Generalized Linear Models via Generalized POCRE

Description

A pre-specified number (i.e., maxvar) of covariates will be selected for generalized linear models by constructing maxcmp components with generalized POCRE. Each component will be constructed by selecting maxvar/macmp covariates which are most relevant to the response variable(s). Similar to pocrescreen, gps selects covariates for their top relevance to the response variable(s) without penalization.

Usage

gps(y, x, family="binomial", bc.method="optimal", x.include=NULL,
    weights=NULL, maxcmp=10, maxvar=NULL, tol = 1e-6, maxit = 100)

Arguments

y

n*q matrix, values of q response variables (allow for multiple response variables).

x

n*p matrix, values of p predicting variables (excluding the intercept).

family

Family objects as family. Currently only support "gaussian", "binomial" (by default), and "poisson".

bc.method

Bias correction method.

x.include

a vector of indices indicating covariates which should always be included in the model (so not counted into selected maxvar covariates).

weights

A vector, including a prespecified weight for each observation (set as 1/n by default).

maxcmp

maximum number of components to be constructed.

maxvar

maximum number of selected variables.

tol

tolerance of precision in iterations.

maxit

maximum number of iterations to be allowed.

Value

a vector of indices of selected covariates (excluding those in x.include).

Author(s)

Dabao Zhang, Zhongli Jiang, Yu-ting Chen, Department of Statistics, Purdue University

References

Zhang D, Lin Y, and Zhang M (2009). Penalized orthogonal-components regression for large p small n data. Electronic Journal of Statistics, 3: 781-796.

See Also

pocrescreen.

Examples

  # Binomial Data
  data(simbin)
  gps(simbin[,1], simbin[,-1], maxcmp=3, maxvar=9)
  gps(simbin[,1], simbin[,-1], x.include=103:104, maxcmp=3, maxvar=9)
 
 # Count Data
  data(simpoi)
  gps(simpoi[,1], simpoi[,-1], family='poisson',maxcmp=5,maxvar=10)

Visualization of a pocre Object

Description

Plot the regression coefficients, and the loadings of all components for a fitted model by POCRE.

Usage

## S3 method for class 'pocre'
plot(x, x.id = NA, which=1:2, cex=.5, ...)

Arguments

x

a pocre object, i.e., the result from pocre.

x.id

a vector indicating the indices or positions of the covariates in the original data.

which

1 for plotting the regression coefficients, 2 for plotting the loadings of all components.

cex

A numerical value giving the amount by which plotting text and symbols should be magnified relative to the default, see par.

...

additional arguments accepted by ggplot.

Author(s)

Dabao Zhang, Zhongli Jiang, Zeyu Zhang, Department of Statistics, Purdue University

References

Zhang D (2018). R package POCRE: Exploring high-dimensional data via supervised dimension reduction. Manuscript.

Zhang D, Lin Y, and Zhang M (2009). Penalized orthogonal-components regression for large p small n data. Electronic Journal of Statistics, 3: 781-796.

See Also

pocre, plot.pocrepath, pocrepath.

Examples

data(simdata)
xx <- scale(as.matrix(simdata[,-1]))
yy <- scale(as.matrix(simdata[,1]))

##Fit with pocre()
pres <- pocre(yy, xx, lambda=0.9)

# plot(pres,which=1)
plot(pres)

Visulaization of a POCRE Path

Description

For a series models built by POCRE for different tuning paramter values, it provides three types of plots to help select an appropriate tuning parameter value.

Usage

## S3 method for class 'pocrepath'
plot(x, which=1:3, cex=.5, lwd=1, ...)

Arguments

x

a pocrepath object, i.e., the result from pocrepath.

which

1 for plotting the tuning parameter vs. (beta, #[beta!=0]), 2 for plotting the tuning parameter vs. (beta, R^2), 3 for plotting the tuning parameter vs. (R^2, #[beta!=0]).

cex

A numerical value giving the amount by which plotting text and symbols should be magnified relative to the default, see par.

lwd

line width, see par.

...

additional arguments accepted by ggplot.

Author(s)

Dabao Zhang, Zhongli Jiang, Zeyu Zhang, Department of Statistics, Purdue University

References

Zhang D (2018). R package POCRE: Exploring high-dimensional data via supervised dimension reduction. Manuscript.

Zhang D, Lin Y, and Zhang M (2009). Penalized orthogonal-components regression for large p small n data. Electronic Journal of Statistics, 3: 781-796.

See Also

pocrepath, plot.pocre, pocre.

Examples

data(simdata)
xx <- scale(as.matrix(simdata[,-1]))
yy <- scale(as.matrix(simdata[,1]))

# ppres <- pocrepath(yy, xx, delta=0.01)
ppres <- pocrepath(yy, xx)

# plot(ppres)
plot(ppres,which=3)

Penalized Orthogonal-Components Regression (POCRE)

Description

Apply POCRE with a pre-specified tuning parameter to build a linear regression model with orthogonal components X\vartheta_1, X\vartheta_2, \dots,

Y=\mu+\sum_j (X\varpi_j)\vartheta_j+\epsilon=\mu+X\beta+\epsilon,

where var[\epsilon]=\sigma^2 and \beta=\sum_j \varpi_j\vartheta_j. These orthogonal components are sequentially constructed according to supervised dimension reduction under penalty set by the pre-specified tuning parameter.

While the orthogonal components are constructed using the centralized covariates, the intercept \mu and regression coefficients in \beta are estimated for original covariates. The sequential construction stops when no new component can be constructed (returning bSparse=1), or the new component is constructed with more than maxvar covariates (returning bSparse=0).

Usage

pocre(y, x, lambda=1, x.nop=NA, maxvar=dim(x)[1]/2,
      maxcmp=10, ptype=c('ebtz','ebt','l1','scad','mcp'),
      maxit=100, tol=1e-6, gamma=3.7, pval=FALSE)

Arguments

y

n*q matrix, values of q response variables (allow for multiple response variables).

x

n*p matrix, values of p predicting variables (excluding the intercept).

lambda

the tuning parameter (=1 by default).

x.nop

a vector indicating indices of covariates which are excluded only when evaluating the significance of components.

maxvar

maximum number of selected variables.

maxcmp

maximum number of components to be constructed.

ptype

a character to indicate the type of penalty: 'ebtz' (emprical Bayes thresholding after Fisher's z-transformation, by default), 'ebt' (emprical Bayes thresholding by Johnstone & Silverman (2004)), 'l1' (L_1 penalty), 'scad' (SCAD by Fan & Li (2001)), 'mcp' (MCP by Zhang (2010)).

maxit

maximum number of iterations to be allowed.

tol

tolerance of precision in iterations.

gamma

a parameter used by SCAD and MCP (=3.7 by default).

pval

a logical value indicating whether to calculate the p-values of components.

Value

mu

estimated intercept of the linear regression.

beta

estimated coefficients of the linear regression.

varpi

loadings of the constructed components.

vartheta

the regression coefficients of the constructed components.

bSparse

a logical value indicating whether estimated beta has less than maxvar nonzero values.

lambda

value of the tuning paramete.

nCmp

number of constructed components.

n

sample size.

p

number of covariates.

xShift

the column means of x.

yShift

the column means of y.

sigmae2

estimated error variance \sigma^2.

rsq

R^2 value of the fitted regression model.

nzBeta

number of non-zero regression coefficients in \beta.

omega

internal matrix.

theta

internal matrix.

pvalue

p-values of constructed components, available when pval=TRUE.

seqpv

Type I p-values of components when sequentially including them into the model, available when pval=TRUE.

indpv

p-values of components when marginally testing each component, available when pval=TRUE.

loglik

the loglikelihood function, available when pval=TRUE.

effp

the effective number of predictors, excluding redundant ones, available when pval=TRUE.

Author(s)

Dabao Zhang, Zhongli Jiang, Zeyu Zhang, Department of Statistics, Purdue University

References

Fan J and Li R (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96:1348-1360

Johnstone IM and Silverman BW (2004). Needles and straw in haystacks: empirical Bayes estimates of possibly sparse sequences. Annals of Statistics, 32: 1594-1649.

Zhang C-H (2010). Nearly unbiased variable selection under minimax concave penalty. The Annals of Statistics, 38: 894-942.

Zhang D, Lin Y, and Zhang M (2009). Penalized orthogonal-components regression for large p small n data. Electronic Journal of Statistics, 3: 781-796.

See Also

plot.pocre, pocrescreen, pocrepath, cvpocre.

Examples

data(simdata)
xx <- simdata[,-1]
yy <- simdata[,1]

#pres <- pocre(yy,xx,lambda=0.9)
pres <- pocre(yy,xx)   # lambda=1 by default

Build a POCRE Path for Different Values of Tuning Parameters

Description

Applying POCRE for a series of tuning parameters chosen by a pre-specified step size. The tuning parameter will increase until non-component can be constructed, and then decrease until a non-sparse regression is constructed (i.e., the number of non-zero coefficients in \beta is more than maxvar).

Usage

pocrepath(y, x, delta=0.1, maxvar=dim(x)[1]/2, x.nop=NA, maxcmp=10,
          ptype=c('ebtz','ebt','l1','scad','mcp'), lambda.init=1,
          maxit=100, tol=1e-6, maxtps=500, gamma=3.7, pval=(dim(y)[2]==1))

Arguments

y

n*q matrix, values of q response variables (allow for multiple response variables).

x

n*p matrix, values of p predicting variables (excluding the intercept).

delta

step size to increase or decrase from current tuning parameter.

maxvar

maximum number of selected variables.

x.nop

a vector indicating indices of covariates which are excluded only when evaluating the significance of components.

maxcmp

maximum number of components to be constructed.

ptype

a character to indicate the type of penalty: 'ebtz' (emprical Bayes thresholding after Fisher's z-transformation, by default), 'ebt' (emprical Bayes thresholding by Johnstone & Silverman (2004)), 'l1' (L_1 penalty), 'scad' (SCAD by Fan & Li (2001)), 'mcp' (MCP by Zhang (2010)).

lambda.init

initial value of the tuning parameter (=1 by default).

maxit

maximum number of iterations to be allowed.

tol

tolerance of precision in iterations.

maxtps

maximum number of different values that the tuning parameter is allowed.

gamma

a parameter used by SCAD and MCP (=3.7 by default).

pval

a logical value indicating whether to calculate the p-values of components (not implemented for q>1, i.e., multiple response variables).

Value

A list of results from pocre, each for a specific value of the tuning parameter.

Author(s)

Dabao Zhang, Zhongli Jiang, Zeyu Zhang, Department of Statistics, Purdue University

References

Fan J and Li R (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96:1348-1360

Johnstone IM and Silverman BW (2004). Needles and straw in haystacks: empirical Bayes estimates of possibly sparse sequences. Annals of Statistics, 32: 1594-1649.

Zhang C-H (2010). Nearly unbiased variable selection under minimax concave penalty. The Annals of Statistics, 38: 894-942.

Zhang D (2018). R package POCRE: Exploring high-dimensional data via supervised dimension reduction. Manuscript.

Zhang D, Lin Y, and Zhang M (2009). Penalized orthogonal-components regression for large p small n data. Electronic Journal of Statistics, 3: 781-796.

See Also

plot.pocrepath, selectmodel, pocre.

Examples

data(simdata)
xx <- simdata[,-1]
yy <- simdata[,1]

ppres <- pocrepath(yy,xx)

Screen Variables Using Penalized Orthogonal-Components Regression (POCRE)

Description

Screen for a pre-specified number (i.e., maxvar) of covariates by constructing maxcmp components with POCRE. Each component will be constructed by selecting maxvar/macmp covariates which are most relevant to the response variable(s). Here POCRE selects covariates for their top relevance to the response variable(s) without penalization.

Usage

pocrescreen(y, x, maxvar=nrow(x), maxcmp=5, x.include=NULL,
            tol=1e-6, maxit=100)

Arguments

y

n*q matrix, values of q response variables (allow for multiple response variables).

x

n*p matrix, values of p predicting variables (excluding the intercept).

maxvar

maximum number of selected variables.

maxcmp

maximum number of components to be constructed.

x.include

a vector of indices indicating covariates which should always be included in the model (so not counted into selected maxvar covariates).

tol

tolerance of precision in iterations.

maxit

maximum number of iterations to be allowed.

Value

a vector of indices of selected covariates (excluding those in x.include).

Author(s)

Dabao Zhang, Zhongli Jiang, Zeyu Zhang, Department of Statistics, Purdue University

References

Zhang D (2018). R package POCRE: Exploring high-dimensional data via supervised dimension reduction. Manuscript.

Zhang D, Lin Y, and Zhang M (2009). Penalized orthogonal-components regression for large p small n data. Electronic Journal of Statistics, 3: 781-796.

See Also

pocre, pocrepath, cvpocre.

Examples

data(simdata)
xx <- simdata[,-1]
yy <- simdata[,1]

# Screen for 50 covariates
sidx <- pocrescreen(yy,xx,maxvar=50)

# Screen for 50 additional covariates besides the first 10
xinc <- 1:10
sidx <- pocrescreen(yy,xx,maxvar=50,x.include=xinc)
sidx <- c(xinc,sidx)

Select the Optimal Model

Description

Select the optimal model from those fitted by POCRE, on the basis of prespecified criterion, such as EBIC, BIC, AIC, and AICc.

Usage

selectmodel(ppobj, msc=NULL)

Arguments

ppobj

output from pocrepath.

msc

a value indicating the information criterion: 0 for BIC, (0,1] for EBIC (by default), 2 for AIC, 3 for AICc.

Value

output of pocre for the optimal model.

Author(s)

Dabao Zhang, Zhongli Jiang, Zeyu Zhang, Department of Statistics, Purdue University

References

Chen J and Chen Z (2008) Extended Bayesian information criteria for model selection with large model spaces. Biometrika, 95: 759-771.

Zhang D, Lin Y, and Zhang M (2009). Penalized orthogonal-components regression for large p small n data. Electronic Journal of Statistics, 3: 781-796.

See Also

pocrepath, plot.pocrepath.

Examples

data(simdata)
xx <- scale(as.matrix(simdata[,-1]))
yy <- scale(as.matrix(simdata[,1]))

# ppres <- pocrepath(yy,xx,delta=0.01)
ppres <- pocrepath(yy,xx)
fres <- selectmodel(ppres)

A Set of Simulated Data with Multiple Response Variables

Description

A simulated data set with 100 observations, each with five response variable and 1,000 covariates.

Usage

data("sim5ydata")

Format

A data frame with 100 observations on 1005 variables with the first five columns for the response variables, and the rest for the covariates.

Details

The 1,000 covariates are from 10 blocks of independent variables, with each block consisting 100 autoregressively correlated variables. There are a total of 12 covariates affecting the response variables: x_{50}, x_{51}, x_{150}, x_{153}, x_{250}, x_{256}, x_{350}, x_{359}, x_{450}, x_{467}, x_{550}, x_{583}.

Author(s)

Dabao Zhang, Zhongli Jiang, Zeyu Zhang, Department of Statistics, Purdue University

References

Zhang D, Lin Y, and Zhang M (2009). Penalized orthogonal-components regression for large p small n data. Electronic Journal of Statistics, 3: 781-796.

See Also

pocrescreen, pocrepath, pocre, cvpocre.

Examples

data(sim5ydata)

A Set of Simulated Binomial Data.

Description

A simulated data set with 100 observations, each with one binary response variable and 1,000 covariates.

Usage

data("simbin")

Format

A data frame with 100 observations on 1001 variables with the first column for the response variable, and the rest for the covariates.

Details

The true covariates are 1, 2, 103, 104, 205, and 206.

Author(s)

Dabao Zhang, Zhongli Jiang, Yu-ting Chen, Department of Statistics, Purdue University

References

Zhang D, Lin Y, and Zhang M (2009). Penalized orthogonal-components regression for large p small n data. Electronic Journal of Statistics, 3: 781-796.

See Also

gps

Examples

  data(simbin)

A Set of Simulated Data with Single Gaussian Response Variable

Description

A simulated data set with 100 observations, each with one response variable and 1,000 covariates.

Usage

data("simdata")

Format

A data frame with 100 observations on 1001 variables with the first column for the response variable, and the rest for the covariates.

Details

The 1,000 covariates are from 10 blocks of independent variables, with each block consisting 100 autoregressively correlated variables. There are a total of 20 covariates affecting the response variables: x_1, \dots, x_{10}, x_{101}, \dots, x_{110}.

Author(s)

Dabao Zhang, Zhongli Jiang, Zeyu Zhang, Department of Statistics, Purdue University

References

Zhang D, Lin Y, and Zhang M (2009). Penalized orthogonal-components regression for large p small n data. Electronic Journal of Statistics, 3: 781-796.

See Also

pocrescreen, pocrepath, pocre.

Examples

data(simdata)

A Set of Simulated Poisson Data.

Description

A simulated data set with 100 observations, each with one count response variable and 1,000 covariates.

Usage

data("simpoi")

Format

A data frame with 100 observations on 1001 variables with the first column for the response variable, and the rest for the covariates.

Details

The 1,000 covariates are from 10 blocks of independent variables, with each block consisting 100 autoregressively correlated variables. There are a total of 20 covariates affecting the response variables: x_1, \dots, x_{10}, x_{101}, \dots, x_{110}.

Author(s)

Dabao Zhang, Yu-ting Chen, Department of Statistics, Purdue University

References

Zhang D, Lin Y, and Zhang M (2009). Penalized orthogonal-components regression for large p small n data. Electronic Journal of Statistics, 3: 781-796.

See Also

gps

Examples

  data(simpoi)

Penalized Orthogonal-Components Regression (POCRE) with Significance Inference

Description

Applying POCRE to select variables and evaluate the significance of selected variables using the multiple splitting method by Meinshausen et al. (2009). The tuning parameter may be selected based on either an information criterion or k-fold cross-validation. The tuning parameter can also be fixed at a prespecified value.

Usage

sipocre(y, x, n.splits=10, delta=0.1, crit=c('ic','cv','fixed'),
        ptype=c('ebtz','ebt','l1','scad','mcp'), maxvar=dim(x)[1]/2,
        msc=NA, maxit=100, maxcmp=50, gamma=3.7, tol=1e-6,
        n.folds=10, lambda=1, n.train=round(nrow(x)/2))

Arguments

y

n*q matrix, values of q response variables (allow for multiple response variables).

x

n*p matrix, values of p predicting variables (excluding the intercept).

n.splits

number of random splits (=10 by default).

delta

step size to increase or decrase from current tuning parameter.

crit

character indicating the criterion to choose the tuning parameter: 'ic' (information criteria such as AIC, AICc, BIC, EBIC), 'cv' (k-folds cross-valdiation) or 'fixed' (a pre-specified value).

ptype

a character to indicate the type of penalty: 'ebtz' (emprical Bayes thresholding after Fisher's z-transformation, by default), 'ebt' (emprical Bayes thresholding by Johnstone & Silverman (2004)), 'l1' (L_1 penalty), 'scad' (SCAD by Fan & Li (2001)), 'mcp' (MCP by Zhang (2010)).

maxvar

maximum number of selected variables.

msc

value(s) to indicate the penalty related to the information criterion: 0~1 for (E)BIC, 2 for AIC, 3 for AICc, used when crit='ic'.

maxit

maximum number of iterations to be allowed.

maxcmp

maximum number of components to be constructed.

gamma

a parameter used by SCAD and MCP (=3.7 by default).

tol

tolerance of precision in iterations.

n.folds

number of folds in k-folds cross-validation, used when crit='cv'.

lambda

pre-sepcified value for the tuning parameter, used when crit='fixed'.

n.train

sample size of the training data set.

Value

a list consisting of the following components,

cpv

component-based p-values which are calculated by testing the constructed components, either a matrix (when crit='ic', in this case each column corresponds to one value in msc) or a vector (when crit='cv' or crit='fixed').

xpv

traditional p-values, either a matrix (when crit='ic', in this case each column corresponds to one value in msc) or a vector (when crit='cv' or crit='fixed').

Author(s)

Dabao Zhang, Zhongli Jiang, Zeyu Zhang, Department of Statistics, Purdue University

References

Fan J and Li R (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96:1348-1360

Johnstone IM and Silverman BW (2004). Needles and straw in haystacks: empirical Bayes estimates of possibly sparse sequences. Annals of Statistics, 32: 1594-1649.

Meinshausen N, Meier L, and Buhlmann P (2009) p-Values for High-Dimensional Regression. Journal of the American Statistical Association, 104: 1671-1681.

Zhang C-H (2010). Nearly unbiased variable selection under minimax concave penalty. The Annals of Statistics, 38: 894-942.

Zhang D, Lin Y, and Zhang M (2009). Penalized orthogonal-components regression for large p small n data. Electronic Journal of Statistics, 3: 781-796.

See Also

pocre.

Examples

## Not run: 
data(simdata)
xx <- simdata[,-1]
yy <- simdata[,1]

sipres <- sipocre(yy,xx)

## End(Not run)