Version:	0.3.0
Date:	2023-03-12
Depends:	R (≥ 3.5.0)
Imports:	mvtnorm, boot, Formula, MASS, plsRglm, betareg, methods
Suggests:	bipartite, knitr, markdown, plotrix, pls, plsdof, prettydoc, rmarkdown
Title:	Partial Least Squares Regression for Beta Regression Models
Author:	Frederic Bertrand [cre, aut], Myriam Maumy-Bertrand [aut]
Maintainer:	Frederic Bertrand <frederic.bertrand@utt.fr>
Description:	Provides Partial least squares Regression for (weighted) beta regression models (Bertrand 2013, http://journal-sfds.fr/article/view/215) and k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
License:	GPL-3
Encoding:	UTF-8
Classification/MSC:	62J12, 62J99
LazyData:	true
VignetteBuilder:	knitr
RoxygenNote:	7.2.1
URL:	https://fbertran.github.io/plsRbeta/, https://github.com/fbertran/plsRbeta/
BugReports:	https://github.com/fbertran/plsRbeta/issues/
NeedsCompilation:	no
Packaged:	2023-03-14 00:46:09 UTC; fbertran
Repository:	CRAN
Date/Publication:	2023-03-15 13:40:03 UTC

plsRbeta-package

Description

Provides Partial least squares Regression for (weighted) beta regression models (Bertrand 2013, <http://journal-sfds.fr/article/view/215>) and k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.

References

Partial least squares Regression for (weighted) beta regression models (Bertrand 2013, <http://journal-sfds.fr/article/view/215>), https://github.com/fbertran/plsRbeta/ et https://fbertran.github.io/plsRbeta/

Examples

data("GasolineYield",package="betareg")
modpls <- plsRbeta(yield~.,data=GasolineYield,nt=3,modele="pls-beta")
modpls$pp
modpls$Coeffs
modpls$Std.Coeffs
modpls$InfCrit
modpls$PredictY[1,]
rm("modpls")

data("GasolineYield",package="betareg")
yGasolineYield <- GasolineYield$yield
XGasolineYield <- GasolineYield[,2:5]
modpls <- plsRbeta(yGasolineYield,XGasolineYield,nt=3,modele="pls-beta")
modpls$pp
modpls$Coeffs
modpls$Std.Coeffs
modpls$InfCrit
modpls$PredictY[1,]
rm("modpls")

Partial least squares beta regression models

Description

This function implements Partial least squares beta regression models on complete or incomplete datasets.

Usage

PLS_beta(
  dataY,
  dataX,
  nt = 2,
  limQ2set = 0.0975,
  dataPredictY = dataX,
  modele = "pls",
  family = NULL,
  typeVC = "none",
  EstimXNA = FALSE,
  scaleX = TRUE,
  scaleY = NULL,
  pvals.expli = FALSE,
  alpha.pvals.expli = 0.05,
  MClassed = FALSE,
  tol_Xi = 10^(-12),
  weights,
  method,
  sparse = FALSE,
  sparseStop = TRUE,
  naive = FALSE,
  link = NULL,
  link.phi = NULL,
  type = "ML",
  verbose = TRUE
)

Arguments

Details

There are seven different predefined models with predefined link functions available :

list("\"pls\"")

ordinary pls models

list("\"pls-glm-Gamma\"")

glm gaussian with inverse link pls models

list("\"pls-glm-gaussian\"")

glm gaussian with identity link pls models

list("\"pls-glm-inverse-gamma\"")

glm binomial with square inverse link pls models

list("\"pls-glm-logistic\"")

glm binomial with logit link pls models

list("\"pls-glm-poisson\"")

glm poisson with log link pls models

list("\"pls-glm-polr\"")

glm polr with logit link pls models

Using the "family=" option and setting "modele=pls-glm-family" allows changing the family and link function the same way as for the glm function. As a consequence user-specified families can also be used.

The

accepts the links (as names) identity, log and inverse.

list("gaussian")

accepts the links (as names) identity, log and inverse.

family

accepts the links (as names) identity, log and inverse.

The

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

list("binomial")

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

family

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

The

accepts the links inverse, identity and log.

list("Gamma")

accepts the links inverse, identity and log.

family

accepts the links inverse, identity and log.

The

accepts the links log, identity, and sqrt.

list("poisson")

accepts the links log, identity, and sqrt.

family

accepts the links log, identity, and sqrt.

The

accepts the links 1/mu^2, inverse, identity and log.

list("inverse.gaussian")

accepts the links 1/mu^2, inverse, identity and log.

family

accepts the links 1/mu^2, inverse, identity and log.

The

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

list("quasi")

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

family

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

The function

can be used to create a power link function.

list("power")

can be used to create a power link function.

The default estimator for Degrees of Freedom is the Kramer and Sugiyama's one which only works for classical plsR models. For these models, Information criteria are computed accordingly to these estimations. Naive Degrees of Freedom and Information Criteria are also provided for comparison purposes. For more details, see Kraemer, N., Sugiyama M. (2010). "The Degrees of Freedom of Partial Least Squares Regression". preprint, http://arxiv.org/abs/1002.4112.

Value

Depends on the model that was used to fit the model.

Note

Use plsRbeta instead.

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

References

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215

See Also

PLS_beta_wvc and PLS_beta_kfoldcv

Examples

data("GasolineYield",package="betareg")
yGasolineYield <- GasolineYield$yield
XGasolineYield <- GasolineYield[,2:5]
modpls <- PLS_beta(yGasolineYield,XGasolineYield,nt=3,modele="pls-beta")
modpls$pp
modpls$Coeffs
modpls$Std.Coeffs
modpls$InfCrit
modpls$PredictY[1,]
rm("modpls")

Partial least squares beta regression models

Description

This function implements Partial least squares beta regression models on complete or incomplete datasets (formula specification of the model).

Usage

PLS_beta_formula(
  formula,
  data = NULL,
  nt = 2,
  limQ2set = 0.0975,
  dataPredictY = dataX,
  modele = "pls",
  family = NULL,
  typeVC = "none",
  EstimXNA = FALSE,
  scaleX = TRUE,
  scaleY = NULL,
  pvals.expli = FALSE,
  alpha.pvals.expli = 0.05,
  MClassed = FALSE,
  tol_Xi = 10^(-12),
  weights,
  subset,
  start = NULL,
  etastart,
  mustart,
  offset,
  method,
  control = list(),
  contrasts = NULL,
  sparse = FALSE,
  sparseStop = TRUE,
  naive = FALSE,
  link = NULL,
  link.phi = NULL,
  type = "ML",
  verbose = TRUE
)

Arguments

Details

There are seven different predefined models with predefined link functions available :

list("\"pls\"")

ordinary pls models

list("\"pls-glm-Gamma\"")

glm gaussian with inverse link pls models

list("\"pls-glm-gaussian\"")

glm gaussian with identity link pls models

list("\"pls-glm-inverse-gamma\"")

glm binomial with square inverse link pls models

list("\"pls-glm-logistic\"")

glm binomial with logit link pls models

list("\"pls-glm-poisson\"")

glm poisson with log link pls models

list("\"pls-glm-polr\"")

glm polr with logit link pls models

Using the "family=" option and setting "modele=pls-glm-family" allows changing the family and link function the same way as for the glm function. As a consequence user-specified families can also be used.

The

accepts the links (as names) identity, log and inverse.

list("gaussian")

accepts the links (as names) identity, log and inverse.

family

accepts the links (as names) identity, log and inverse.

The

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

list("binomial")

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

family

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

The

accepts the links inverse, identity and log.

list("Gamma")

accepts the links inverse, identity and log.

family

accepts the links inverse, identity and log.

The

accepts the links log, identity, and sqrt.

list("poisson")

accepts the links log, identity, and sqrt.

family

accepts the links log, identity, and sqrt.

The

accepts the links 1/mu^2, inverse, identity and log.

list("inverse.gaussian")

accepts the links 1/mu^2, inverse, identity and log.

family

accepts the links 1/mu^2, inverse, identity and log.

The

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

list("quasi")

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

family

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

The function

can be used to create a power link function.

list("power")

can be used to create a power link function.

A typical predictor has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with any duplicates removed.

A specification of the form first:second indicates the the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.

The terms in the formula will be re-ordered so that main effects come first, followed by the interactions, all second-order, all third-order and so on: to avoid this pass a terms object as the formula.

Non-NULL weights can be used to indicate that different observations have different dispersions (with the values in weights being inversely proportional to the dispersions); or equivalently, when the elements of weights are positive integers w_i, that each response y_i is the mean of w_i unit-weight observations.

The default estimator for Degrees of Freedom is the Kramer and Sugiyama's one which only works for classical plsR models. For these models, Information criteria are computed accordingly to these estimations. Naive Degrees of Freedom and Information Criteria are also provided for comparison purposes. For more details, see Kraemer, N., Sugiyama M. (2010). "The Degrees of Freedom of Partial Least Squares Regression". preprint, http://arxiv.org/abs/1002.4112.

Value

Depends on the model that was used to fit the model.

Note

Use plsRbeta instead.

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

References

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215

See Also

PLS_beta_wvc and PLS_beta_kfoldcv_formula

Examples

data("GasolineYield",package="betareg")
modpls <- PLS_beta_formula(yield~.,data=GasolineYield,nt=3,modele="pls-beta")
modpls$pp
modpls$Coeffs
modpls$Std.Coeffs
modpls$InfCrit
modpls$PredictY[1,]
rm("modpls")

Partial least squares regression beta models with kfold cross validation

Description

This function implements kfold cross validation on complete or incomplete datasets for partial least squares beta regression models

Usage

PLS_beta_kfoldcv(
  dataY,
  dataX,
  nt = 2,
  limQ2set = 0.0975,
  modele = "pls",
  family = NULL,
  K = nrow(dataX),
  NK = 1,
  grouplist = NULL,
  random = FALSE,
  scaleX = TRUE,
  scaleY = NULL,
  keepcoeffs = FALSE,
  keepfolds = FALSE,
  keepdataY = TRUE,
  keepMclassed = FALSE,
  tol_Xi = 10^(-12),
  weights,
  method,
  link = NULL,
  link.phi = NULL,
  type = "ML",
  verbose = TRUE
)

Arguments

Details

Predicts 1 group with the K-1 other groups. Leave one out cross validation is thus obtained for K==nrow(dataX).

There are seven different predefined models with predefined link functions available :

list("\"pls\"")

ordinary pls models

list("\"pls-glm-Gamma\"")

glm gaussian with inverse link pls models

list("\"pls-glm-gaussian\"")

glm gaussian with identity link pls models

list("\"pls-glm-inverse-gamma\"")

glm binomial with square inverse link pls models

list("\"pls-glm-logistic\"")

glm binomial with logit link pls models

list("\"pls-glm-poisson\"")

glm poisson with log link pls models

list("\"pls-glm-polr\"")

glm polr with logit link pls models

Using the "family=" option and setting "modele=pls-glm-family" allows changing the family and link function the same way as for the glm function. As a consequence user-specified families can also be used.

The

accepts the links (as names) identity, log and inverse.

list("gaussian")

accepts the links (as names) identity, log and inverse.

family

accepts the links (as names) identity, log and inverse.

The

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

list("binomial")

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

family

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

The

accepts the links inverse, identity and log.

list("Gamma")

accepts the links inverse, identity and log.

family

accepts the links inverse, identity and log.

The

accepts the links log, identity, and sqrt.

list("poisson")

accepts the links log, identity, and sqrt.

family

accepts the links log, identity, and sqrt.

The

accepts the links 1/mu^2, inverse, identity and log.

list("inverse.gaussian")

accepts the links 1/mu^2, inverse, identity and log.

family

accepts the links 1/mu^2, inverse, identity and log.

The

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

list("quasi")

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

family

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

The function

can be used to create a power link function.

list("power")

can be used to create a power link function.

Non-NULL weights can be used to indicate that different observations have different dispersions (with the values in weights being inversely proportional to the dispersions); or equivalently, when the elements of weights are positive integers w_i, that each response y_i is the mean of w_i unit-weight observations.

Value

Note

Works for complete and incomplete datasets.

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

References

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215

See Also

kfolds2coeff, kfolds2Pressind, kfolds2Press, kfolds2Mclassedind, kfolds2Mclassed and kfolds2CVinfos_beta to extract and transform results from kfold cross validation.

Examples

## Not run: 
data("GasolineYield",package="betareg")
yGasolineYield <- GasolineYield$yield
XGasolineYield <- GasolineYield[,2:5]
bbb <- PLS_beta_kfoldcv(yGasolineYield,XGasolineYield,nt=3,modele="pls-beta")
kfolds2CVinfos_beta(bbb)

## End(Not run)

Partial least squares regression beta models with kfold cross validation

Description

This function implements kfold cross validation on complete or incomplete datasets for partial least squares beta regression models (formula specification of the model).

Usage

PLS_beta_kfoldcv_formula(
  formula,
  data = NULL,
  nt = 2,
  limQ2set = 0.0975,
  modele = "pls",
  family = NULL,
  K = nrow(dataX),
  NK = 1,
  grouplist = NULL,
  random = FALSE,
  scaleX = TRUE,
  scaleY = NULL,
  keepcoeffs = FALSE,
  keepfolds = FALSE,
  keepdataY = TRUE,
  keepMclassed = FALSE,
  tol_Xi = 10^(-12),
  weights,
  subset,
  start = NULL,
  etastart,
  mustart,
  offset,
  method,
  control = list(),
  contrasts = NULL,
  sparse = FALSE,
  sparseStop = TRUE,
  naive = FALSE,
  link = NULL,
  link.phi = NULL,
  type = "ML",
  verbose = TRUE
)

Arguments

Details

Predicts 1 group with the K-1 other groups. Leave one out cross validation is thus obtained for K==nrow(dataX).

There are seven different predefined models with predefined link functions available :

list("\"pls\"")

ordinary pls models

list("\"pls-glm-Gamma\"")

glm gaussian with inverse link pls models

list("\"pls-glm-gaussian\"")

glm gaussian with identity link pls models

list("\"pls-glm-inverse-gamma\"")

glm binomial with square inverse link pls models

list("\"pls-glm-logistic\"")

glm binomial with logit link pls models

list("\"pls-glm-poisson\"")

glm poisson with log link pls models

list("\"pls-glm-polr\"")

glm polr with logit link pls models

Using the "family=" option and setting "modele=pls-glm-family" allows changing the family and link function the same way as for the glm function. As a consequence user-specified families can also be used.

The

accepts the links (as names) identity, log and inverse.

list("gaussian")

accepts the links (as names) identity, log and inverse.

family

accepts the links (as names) identity, log and inverse.

The

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

list("binomial")

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

family

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

The

accepts the links inverse, identity and log.

list("Gamma")

accepts the links inverse, identity and log.

family

accepts the links inverse, identity and log.

The

accepts the links log, identity, and sqrt.

list("poisson")

accepts the links log, identity, and sqrt.

family

accepts the links log, identity, and sqrt.

The

accepts the links 1/mu^2, inverse, identity and log.

list("inverse.gaussian")

accepts the links 1/mu^2, inverse, identity and log.

family

accepts the links 1/mu^2, inverse, identity and log.

The

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

list("quasi")

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

family

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

The function

can be used to create a power link function.

list("power")

can be used to create a power link function.

A typical predictor has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with any duplicates removed.

A specification of the form first:second indicates the the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.

The terms in the formula will be re-ordered so that main effects come first, followed by the interactions, all second-order, all third-order and so on: to avoid this pass a terms object as the formula.

Non-NULL weights can be used to indicate that different observations have different dispersions (with the values in weights being inversely proportional to the dispersions); or equivalently, when the elements of weights are positive integers w_i, that each response y_i is the mean of w_i unit-weight observations.

Value

Note

Work for complete and incomplete datasets.

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

References

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215

See Also

kfolds2coeff, kfolds2Pressind, kfolds2Press, kfolds2Mclassedind, kfolds2Mclassed and kfolds2CVinfos_beta to extract and transform results from kfold cross validation.

Examples

## Not run: 
data("GasolineYield",package="betareg")
bbb <- PLS_beta_kfoldcv_formula(yield~.,data=GasolineYield,nt=3,modele="pls-beta")
kfolds2CVinfos_beta(bbb)

## End(Not run)

Light version of PLS_beta for cross validation purposes

Description

Light version of PLS_beta for cross validation purposes either on complete or incomplete datasets.

Usage

PLS_beta_wvc(
  dataY,
  dataX,
  nt = 2,
  dataPredictY = dataX,
  modele = "pls",
  family = NULL,
  scaleX = TRUE,
  scaleY = NULL,
  keepcoeffs = FALSE,
  keepstd.coeffs = FALSE,
  tol_Xi = 10^(-12),
  weights,
  method = "logistic",
  link = NULL,
  link.phi = NULL,
  type = "ML",
  verbose = TRUE
)

Arguments

Details

This function is called by PLS_glm_kfoldcv_formula in order to perform cross validation either on complete or incomplete datasets.

There are seven different predefined models with predefined link functions available :

list("\"pls\"")

ordinary pls models

list("\"pls-glm-Gamma\"")

glm gaussian with inverse link pls models

list("\"pls-glm-gaussian\"")

glm gaussian with identity link pls models

list("\"pls-glm-inverse-gamma\"")

glm binomial with square inverse link pls models

list("\"pls-glm-logistic\"")

glm binomial with logit link pls models

list("\"pls-glm-poisson\"")

glm poisson with log link pls models

list("\"pls-glm-polr\"")

glm polr with logit link pls models

Using the "family=" option and setting "modele=pls-glm-family" allows changing the family and link function the same way as for the glm function. As a consequence user-specified families can also be used.

The

accepts the links (as names) identity, log and inverse.

list("gaussian")

accepts the links (as names) identity, log and inverse.

family

accepts the links (as names) identity, log and inverse.

The

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

list("binomial")

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

family

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

The

accepts the links inverse, identity and log.

list("Gamma")

accepts the links inverse, identity and log.

family

accepts the links inverse, identity and log.

The

accepts the links log, identity, and sqrt.

list("poisson")

accepts the links log, identity, and sqrt.

family

accepts the links log, identity, and sqrt.

The

accepts the links 1/mu^2, inverse, identity and log.

list("inverse.gaussian")

accepts the links 1/mu^2, inverse, identity and log.

family

accepts the links 1/mu^2, inverse, identity and log.

The

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

list("quasi")

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

family

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

The function

can be used to create a power link function.

list("power")

can be used to create a power link function.

Non-NULL weights can be used to indicate that different observations have different dispersions (with the values in weights being inversely proportional to the dispersions); or equivalently, when the elements of weights are positive integers w_i, that each response y_i is the mean of w_i unit-weight observations.

Value

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

References

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215

See Also

PLS_beta for more detailed results, PLS_beta_kfoldcv for cross validating models and PLS_lm_wvc for the same function dedicated to plsR models

Examples

data("GasolineYield",package="betareg")
yGasolineYield <- GasolineYield$yield
XGasolineYield <- GasolineYield[,2:5]
modpls <- PLS_beta_wvc(yGasolineYield,XGasolineYield,nt=3,modele="pls-beta")
modpls
rm("modpls")

Cancer infiltration rates

Description

This dataset features cancer infiltration rates and microsatellites data.

Usage

TxTum

Format

A data frame with 106 rows and 60 variables.

CELTUMCO

a numeric vector

age

a numeric vector

sexe

a numeric vector

HISTOADK

a numeric vector

H2

a numeric vector

P3

a numeric vector

P4

a numeric vector

E1

a numeric vector

P5

a numeric vector

R10

a numeric vector

C3M

a numeric vector

P6

a numeric vector

RB

a numeric vector

FL7A

a numeric vector

P53

a numeric vector

W2

a numeric vector

P2

a numeric vector

P1

a numeric vector

W4

a numeric vector

MT1

a numeric vector

MT2

a numeric vector

MT4

a numeric vector

MT3

a numeric vector

HLA

a numeric vector

HLD

a numeric vector

HLC

a numeric vector

HLB

a numeric vector

EA1

a numeric vector

EA3

a numeric vector

EA2

a numeric vector

EA4

a numeric vector

EB1

a numeric vector

EB2

a numeric vector

EB3

a numeric vector

EB4

a numeric vector

EGF1

a numeric vector

EGF2

a numeric vector

EGF3

a numeric vector

EGF4

a numeric vector

EGF5

a numeric vector

EGF6

a numeric vector

FL7B

a numeric vector

VSFGF7

a numeric vector

F3A

a numeric vector

F3B

a numeric vector

VSFGFR3

a numeric vector

F4

a numeric vector

Q5

a numeric vector

VSTOP1

a numeric vector

VSTOP2A

a numeric vector

VSEGFR

a numeric vector

AFRAEGFR

a numeric vector

SRXRA

a numeric vector

SMT

a numeric vector

QMTAMPN

a numeric vector

QMTDELN

a numeric vector

SHL

a numeric vector

SEA

a numeric vector

SEB

a numeric vector

QPCRFGF7

a numeric vector

References

Régression Bêta PLS. (French) [PLS Beta regression.], F. Bertrand, N. Meyer, M. Beau-Faller, K. El Bayed, N. Izzie-J., M. Maumy-Bertrand, (2013), J. SFdS, 154(3):143-159

Partial Least Squares Regression for Beta Regression Models. F. Bertrand, M. Maumy (2021). useR! 2021, Zurich.

Examples

data
print(TxTum)
summary(TxTum)

Bootstrap distribution TxTum BC1 model

Description

A precomputed bootstrap distribution of the coefficients of a model used in the vignette.

Usage

TxTum.mod.bootBC1

Format

a class boot object

References

Régression Bêta PLS. (French) [PLS Beta regression.], F. Bertrand, N. Meyer, M. Beau-Faller, K. El Bayed, N. Izzie-J., M. Maumy-Bertrand, (2013), J. SFdS, 154(3):143-159

Partial Least Squares Regression for Beta Regression Models. F. Bertrand, M. Maumy (2021). useR! 2021, Zurich.

Examples

data(TxTum.mod.bootBC1)
str(TxTum.mod.bootBC1)
plot(TxTum.mod.bootBC1)

Bootstrap distribution TxTum BR6 model

Description

A precomputed bootstrap distribution of the coefficients of a model used in the vignette.

Usage

TxTum.mod.bootBR6

Format

a class boot object

References

Régression Bêta PLS. (French) [PLS Beta regression.], F. Bertrand, N. Meyer, M. Beau-Faller, K. El Bayed, N. Izzie-J., M. Maumy-Bertrand, (2013), J. SFdS, 154(3):143-159

Partial Least Squares Regression for Beta Regression Models. F. Bertrand, M. Maumy (2021). useR! 2021, Zurich.

Examples

data(TxTum.mod.bootBR6)
str(TxTum.mod.bootBR6)
plot(TxTum.mod.bootBR6)

Non-parametric Bootstrap for PLS beta regression models

Description

Provides a wrapper for the bootstrap function boot from the boot R package.
Implements non-parametric bootstrap for PLS beta regression models by case resampling.

Usage

bootplsbeta(
  object,
  typeboot = "plsmodel",
  R = 250,
  statistic = NULL,
  sim = "ordinary",
  stype = "i",
  stabvalue = 1e+06,
  ...
)

Arguments

Details

More details on bootstrap techniques are available in the help of the boot function.

Value

An object of class "boot". See the Value part of the help of the function boot.

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

References

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215

See Also

boot

Examples

data("GasolineYield",package="betareg")

# Std coefficients
modplsbeta <- plsRbeta(yield~., data=GasolineYield, nt=3, modele="pls-beta")
GazYield.boot <- bootplsbeta(modplsbeta, sim="ordinary", stype="i", R=250)
boot::boot.ci(GazYield.boot, conf = c(0.90,0.95), 
type = c("norm","basic","perc","bca"), index=1)
boot::boot.ci(GazYield.boot, conf = c(0.90,0.95), 
type = c("norm","basic","perc","bca"), index=2)
boot::boot.ci(GazYield.boot, conf = c(0.90,0.95), 
type = c("norm","basic","perc","bca"), index=3)
boot::boot.ci(GazYield.boot, conf = c(0.90,0.95), 
type = c("norm","basic","perc","bca"), index=4)
boot::boot.ci(GazYield.boot, conf = c(0.90,0.95), 
type = c("norm","basic","perc","bca"), index=5)
boot::boot.ci(GazYield.boot, conf = c(0.90,0.95), 
type = c("norm","basic","perc","bca"), index=6)

plsRglm::boxplots.bootpls(GazYield.boot)
plsRglm::confints.bootpls(GazYield.boot)
plsRglm::plots.confints.bootpls(plsRglm::confints.bootpls(GazYield.boot))

#Raw coefficients
modplsbeta <- plsRbeta(yield~.,data=GasolineYield,nt=3, modele="pls-beta")
GazYield.boot.raw <- bootplsbeta(modplsbeta, sim="ordinary", stype="i", 
R=250, statistic=coefs.plsRbeta.raw)
boot::boot.ci(GazYield.boot.raw, conf = c(0.90,0.95), 
type = c("norm","basic","perc","bca"), index=1)
boot::boot.ci(GazYield.boot.raw, conf = c(0.90,0.95), 
type = c("norm","basic","perc","bca"), index=2)
boot::boot.ci(GazYield.boot.raw, conf = c(0.90,0.95), 
type = c("norm","basic","perc","bca"), index=3)
boot::boot.ci(GazYield.boot.raw, conf = c(0.90,0.95), 
type = c("norm","basic","perc","bca"), index=4)
boot::boot.ci(GazYield.boot.raw, conf = c(0.90,0.95), 
type = c("norm","basic","perc","bca"), index=5)
boot::boot.ci(GazYield.boot.raw, conf = c(0.90,0.95), 
type = c("norm","basic","perc","bca"), index=6)

plsRglm::boxplots.bootpls(GazYield.boot.raw)
plsRglm::confints.bootpls(GazYield.boot.raw)
plsRglm::plots.confints.bootpls(plsRglm::confints.bootpls(GazYield.boot.raw))


plot(GazYield.boot.raw,index=2)
boot::jack.after.boot(GazYield.boot.raw, index=2, useJ=TRUE, nt=3)
plot(GazYield.boot.raw, index=2,jack=TRUE)

# PLS bootstrap balanced

GazYield.boot.bal <- bootplsbeta(plsRbeta(yield~.,data=GasolineYield,nt=3,
modele="pls-beta"), sim="balanced", stype="i", R=250)
boot::boot.ci(GazYield.boot.bal, conf = c(0.90,0.95), 
type = c("norm","basic","perc","bca"), index=1)
boot::boot.ci(GazYield.boot.bal, conf = c(0.90,0.95), 
type = c("norm","basic","perc","bca"), index=2)
boot::boot.ci(GazYield.boot.bal, conf = c(0.90,0.95), 
type = c("norm","basic","perc","bca"), index=3)
boot::boot.ci(GazYield.boot.bal, conf = c(0.90,0.95), 
type = c("norm","basic","perc","bca"), index=4)
boot::boot.ci(GazYield.boot.bal, conf = c(0.90,0.95), 
type = c("norm","basic","perc","bca"), index=5)
boot::boot.ci(GazYield.boot.bal, conf = c(0.90,0.95), 
type = c("norm","basic","perc","bca"), index=6)


plsRglm::boxplots.bootpls(GazYield.boot.bal)
plsRglm::confints.bootpls(GazYield.boot.bal)
plsRglm::plots.confints.bootpls(plsRglm::confints.bootpls(GazYield.boot.bal))



plot(GazYield.boot.bal)
boot::jack.after.boot(GazYield.boot.bal, index=1, useJ=TRUE, nt=3)
plot(GazYield.boot.bal,jack=TRUE)


# PLS permutation bootstrap

GazYield.boot.perm <- bootplsbeta(plsRbeta(yield~.,data=GasolineYield,nt=3,
modele="pls-beta"), sim="permutation", stype="i", R=250)
boot::boot.ci(GazYield.boot.perm, conf = c(0.90,0.95), 
type = c("norm","basic","perc"), index=1)
boot::boot.ci(GazYield.boot.perm, conf = c(0.90,0.95), 
type = c("norm","basic","perc"), index=2)
boot::boot.ci(GazYield.boot.perm, conf = c(0.90,0.95), 
type = c("norm","basic","perc"), index=3)
boot::boot.ci(GazYield.boot.perm, conf = c(0.90,0.95), 
type = c("norm","basic","perc"), index=4)
boot::boot.ci(GazYield.boot.perm, conf = c(0.90,0.95), 
type = c("norm","basic","perc"), index=5)
boot::boot.ci(GazYield.boot.perm, conf = c(0.90,0.95), 
type = c("norm","basic","perc"), index=6)
plsRglm::boxplots.bootpls(GazYield.boot.perm)
plot(GazYield.boot.perm)

Coefficients function for bootstrap techniques

Description

Returns the coefficients of a "plsRbeta" model.

Usage

coefs.plsRbeta(
  dataset,
  ind,
  nt,
  modele,
  family = NULL,
  method = "logistic",
  link = NULL,
  link.phi = NULL,
  type = "ML",
  maxcoefvalues,
  ifbootfail,
  verbose = TRUE
)

Arguments

Value

Coefficients' Estimates on a sample.

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

References

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215

See Also

See also bootplsbeta.

Examples

data("GasolineYield",package="betareg")
bootplsbeta(plsRbeta(yield~.,data=GasolineYield,nt=3, modele="pls-beta"), typeboot="plsmodel", 
R=250, statistic=coefs.plsRbeta)

Raw coefficients function for bootstrap techniques

Description

Returns the coefficients of a "plsRbeta" model.

Usage

coefs.plsRbeta.raw(
  dataset,
  ind,
  nt,
  modele,
  family = NULL,
  method = "logistic",
  link = NULL,
  link.phi = NULL,
  type = "ML",
  maxcoefvalues,
  ifbootfail,
  verbose = TRUE
)

Arguments

Value

Coefficients' Estimates on a sample.

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

References

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215

See Also

See also bootplsbeta.

Examples

data("GasolineYield",package="betareg")
bootplsbeta(plsRbeta(yield~.,data=GasolineYield,nt=3, modele="pls-beta"), typeboot="fmodel_par", 
R=250, statistic=coefs.plsRbeta.raw)

Coefficients for bootstrap computations of PLSBeta models

Description

A function passed to boot to perform bootstrap.

Usage

coefs.plsRbetanp(
  dataRepYtt,
  ind,
  nt,
  modele,
  family = NULL,
  method = "logistic",
  link = NULL,
  link.phi = NULL,
  type = "ML",
  verbose = TRUE,
  maxcoefvalues,
  wwetoile,
  ifbootfail
)

Arguments

Value

estimates on a bootstrap sample or ifbootfail value if the bootstrap computation fails.

Note

~~some notes~~

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

See Also

See also bootplsbeta

Examples

data("GasolineYield",package="betareg")
bootplsbeta(plsRbeta(yield~.,data=GasolineYield,nt=3, modele="pls-beta"), typeboot="fmodel_np", 
R=250, statistic=coefs.plsRbetanp)

Tumor rate and spectral data

Description

This dataset feature tumor rate data with spectral data descriptors. It is used as an example in the second vignette of the package.

Usage

colon

Format

A data frame with 80 observations on the following 180 variables.

X..Tumor.Cells

a numeric vector

X4.69499969

a numeric vector

X4.68499947

a numeric vector

X4.67499971

a numeric vector

X4.66499949

a numeric vector

X4.65499973

a numeric vector

X4.6449995

a numeric vector

X4.63499975

a numeric vector

X4.62499952

a numeric vector

X4.61499977

a numeric vector

X4.60499954

a numeric vector

X4.59499979

a numeric vector

X4.58499956

a numeric vector

X4.57499981

a numeric vector

X4.56499958

a numeric vector

X4.55499983

a numeric vector

X4.5449996

a numeric vector

X4.53499985

a numeric vector

X4.52499962

a numeric vector

X4.51499987

a numeric vector

X4.50499964

a numeric vector

X4.49499989

a numeric vector

X4.48499966

a numeric vector

X4.4749999

a numeric vector

X4.46499968

a numeric vector

X4.45499992

a numeric vector

X4.44499969

a numeric vector

X4.43499947

a numeric vector

X4.42499971

a numeric vector

X4.41499949

a numeric vector

X4.40499973

a numeric vector

X4.3949995

a numeric vector

X4.38499975

a numeric vector

X4.37499952

a numeric vector

X4.36499977

a numeric vector

X4.35499954

a numeric vector

X4.34499979

a numeric vector

X4.33499956

a numeric vector

X4.32499981

a numeric vector

X4.31499958

a numeric vector

X4.30499983

a numeric vector

X4.2949996

a numeric vector

X4.28499985

a numeric vector

X4.27499962

a numeric vector

X4.26499987

a numeric vector

X4.25499964

a numeric vector

X4.24499989

a numeric vector

X4.23499966

a numeric vector

X4.2249999

a numeric vector

X4.21499968

a numeric vector

X4.20499992

a numeric vector

X4.19499969

a numeric vector

X4.18499994

a numeric vector

X4.17499971

a numeric vector

X4.16499949

a numeric vector

X4.15499973

a numeric vector

X4.1449995

a numeric vector

X4.13499975

a numeric vector

X4.12499952

a numeric vector

X4.11499977

a numeric vector

X4.10499954

a numeric vector

X4.09499979

a numeric vector

X4.08499956

a numeric vector

X4.07499981

a numeric vector

X4.06499958

a numeric vector

X4.05499983

a numeric vector

X4.0449996

a numeric vector

X4.03499985

a numeric vector

X4.02499962

a numeric vector

X4.01499987

a numeric vector

X4.00499964

a numeric vector

X3.99499965

a numeric vector

X3.98499966

a numeric vector

X3.97499967

a numeric vector

X3.96499968

a numeric vector

X3.95499969

a numeric vector

X3.94499969

a numeric vector

X3.9349997

a numeric vector

X3.92499971

a numeric vector

X3.91499972

a numeric vector

X3.90499973

a numeric vector

X3.89499974

a numeric vector

X3.88499975

a numeric vector

X3.87499976

a numeric vector

X3.86499977

a numeric vector

X3.85499978

a numeric vector

X3.84499979

a numeric vector

X3.8349998

a numeric vector

X3.82499981

a numeric vector

X3.81499982

a numeric vector

X3.80499983

a numeric vector

X3.7949996

a numeric vector

X3.78499961

a numeric vector

X3.77499962

a numeric vector

X3.76499963

a numeric vector

X3.75499964

a numeric vector

X3.74499965

a numeric vector

X3.73499966

a numeric vector

X3.72499967

a numeric vector

X3.71499968

a numeric vector

X3.70499969

a numeric vector

X3.69499969

a numeric vector

X3.6849997

a numeric vector

X3.67499971

a numeric vector

X3.66499972

a numeric vector

X3.65499973

a numeric vector

X3.64499974

a numeric vector

X3.63499975

a numeric vector

X3.62499976

a numeric vector

X3.61499977

a numeric vector

X3.60499978

a numeric vector

X3.59499979

a numeric vector

X3.5849998

a numeric vector

X3.57499981

a numeric vector

X3.56499982

a numeric vector

X3.55499983

a numeric vector

X3.54499984

a numeric vector

X3.53499961

a numeric vector

X3.52499962

a numeric vector

X3.51499963

a numeric vector

X3.50499964

a numeric vector

X3.49499965

a numeric vector

X3.48499966

a numeric vector

X3.47499967

a numeric vector

X3.46499968

a numeric vector

X3.45499969

a numeric vector

X3.44499969

a numeric vector

X3.4349997

a numeric vector

X3.42499971

a numeric vector

X3.41499972

a numeric vector

X3.40499973

a numeric vector

X3.39499974

a numeric vector

X3.38499975

a numeric vector

X3.37499976

a numeric vector

X3.36499977

a numeric vector

X3.35499978

a numeric vector

X3.34499979

a numeric vector

X3.3349998

a numeric vector

X3.32499981

a numeric vector

X3.31499982

a numeric vector

X3.30499983

a numeric vector

X3.29499984

a numeric vector

X3.28499961

a numeric vector

X3.27499962

a numeric vector

X3.26499963

a numeric vector

X3.25499964

a numeric vector

X3.24499965

a numeric vector

X3.23499966

a numeric vector

X3.22499967

a numeric vector

X3.21499968

a numeric vector

X3.20499969

a numeric vector

X3.19499969

a numeric vector

X3.1849997

a numeric vector

X3.17499971

a numeric vector

X3.16499972

a numeric vector

X3.15499973

a numeric vector

X3.14499974

a numeric vector

X3.13499975

a numeric vector

X3.12499976

a numeric vector

X3.11499977

a numeric vector

X3.10499978

a numeric vector

X3.09499979

a numeric vector

X3.0849998

a numeric vector

X3.07499981

a numeric vector

X3.06499982

a numeric vector

X3.05499983

a numeric vector

X3.04499984

a numeric vector

X3.03499985

a numeric vector

X3.02499962

a numeric vector

X3.01499963

a numeric vector

X3.00499964

a numeric vector

X2.99499965

a numeric vector

X2.98499966

a numeric vector

X2.97499967

a numeric vector

X2.96499968

a numeric vector

X2.95499969

a numeric vector

X2.94499969

a numeric vector

X2.9349997

a numeric vector

X2.92499971

a numeric vector

X2.91499972

a numeric vector

References

Régression Bêta PLS. (French) [PLS Beta regression.], F. Bertrand, N. Meyer, M. Beau-Faller, K. El Bayed, N. Izzie-J., M. Maumy-Bertrand, (2013), J. SFdS, 154(3):143-159

Partial Least Squares Regression for Beta Regression Models. F. Bertrand, M. Maumy (2021). useR! 2021, Zurich.

Examples

data(colon)
str(colon)

ind_BCa_nt1BC

Description

Variable selection results for the vignette.

Usage

ind_BCa_nt1BC

Format

Logical vector of length 60.

References

Régression Bêta PLS. (French) [PLS Beta regression.], F. Bertrand, N. Meyer, M. Beau-Faller, K. El Bayed, N. Izzie-J., M. Maumy-Bertrand, (2013), J. SFdS, 154(3):143-159

Partial Least Squares Regression for Beta Regression Models. F. Bertrand, M. Maumy (2021). useR! 2021, Zurich.

Examples

data(ind_BCa_nt1BC)
## maybe str(ind_BCa_nt1BC) ; plot(ind_BCa_nt1BC) ...

ind_BCa_nt1BR

Description

Variable selection results for the vignette.

Usage

ind_BCa_nt1BR

Format

Logical vector of length 60.

References

Régression Bêta PLS. (French) [PLS Beta regression.], F. Bertrand, N. Meyer, M. Beau-Faller, K. El Bayed, N. Izzie-J., M. Maumy-Bertrand, (2013), J. SFdS, 154(3):143-159

Partial Least Squares Regression for Beta Regression Models. F. Bertrand, M. Maumy (2021). useR! 2021, Zurich.

Examples

data(ind_BCa_nt1BR)
## maybe str(ind_BCa_nt1BR) ; plot(ind_BCa_nt1BR) ...

ind_BCa_nt2BC

Description

Variable selection results for the vignette.

Usage

ind_BCa_nt2BC

Format

Logical vector of length 60.

References

Régression Bêta PLS. (French) [PLS Beta regression.], F. Bertrand, N. Meyer, M. Beau-Faller, K. El Bayed, N. Izzie-J., M. Maumy-Bertrand, (2013), J. SFdS, 154(3):143-159

Partial Least Squares Regression for Beta Regression Models. F. Bertrand, M. Maumy (2021). useR! 2021, Zurich.

Examples

data(ind_BCa_nt2BC)
## maybe str(ind_BCa_nt2BC) ; plot(ind_BCa_nt2BC) ...

ind_BCa_nt2BR

Description

Variable selection results for the vignette.

Usage

ind_BCa_nt2BR

Format

Logical vector of length 60.

References

Régression Bêta PLS. (French) [PLS Beta regression.], F. Bertrand, N. Meyer, M. Beau-Faller, K. El Bayed, N. Izzie-J., M. Maumy-Bertrand, (2013), J. SFdS, 154(3):143-159

Partial Least Squares Regression for Beta Regression Models. F. Bertrand, M. Maumy (2021). useR! 2021, Zurich.

Examples

data(ind_BCa_nt2BR)
## maybe str(ind_BCa_nt2BR) ; plot(ind_BCa_nt2BR) ...

ind_BCa_nt3

Description

Variable selection results for the vignette.

Usage

ind_BCa_nt3

Format

Logical vector of length 60.

References

Régression Bêta PLS. (French) [PLS Beta regression.], F. Bertrand, N. Meyer, M. Beau-Faller, K. El Bayed, N. Izzie-J., M. Maumy-Bertrand, (2013), J. SFdS, 154(3):143-159

Partial Least Squares Regression for Beta Regression Models. F. Bertrand, M. Maumy (2021). useR! 2021, Zurich.

Examples

data(ind_BCa_nt3)
## maybe str(ind_BCa_nt3) ; plot(ind_BCa_nt3) ...

ind_BCa_nt3BC

Description

Variable selection results for the vignette.

Usage

ind_BCa_nt3BC

Format

Logical vector of length 60.

References

Régression Bêta PLS. (French) [PLS Beta regression.], F. Bertrand, N. Meyer, M. Beau-Faller, K. El Bayed, N. Izzie-J., M. Maumy-Bertrand, (2013), J. SFdS, 154(3):143-159

Partial Least Squares Regression for Beta Regression Models. F. Bertrand, M. Maumy (2021). useR! 2021, Zurich.

Examples

data(ind_BCa_nt3BC)
## maybe str(ind_BCa_nt3BC) ; plot(ind_BCa_nt3BC) ...

ind_BCa_nt3BR

Description

Variable selection results for the vignette.

Usage

ind_BCa_nt3BR

Format

Logical vector of length 60.

References

Régression Bêta PLS. (French) [PLS Beta regression.], F. Bertrand, N. Meyer, M. Beau-Faller, K. El Bayed, N. Izzie-J., M. Maumy-Bertrand, (2013), J. SFdS, 154(3):143-159

Partial Least Squares Regression for Beta Regression Models. F. Bertrand, M. Maumy (2021). useR! 2021, Zurich.

Examples

data(ind_BCa_nt3BR)
## maybe str(ind_BCa_nt3BR) ; plot(ind_BCa_nt3BR) ...

ind_BCa_nt4BC

Description

Variable selection results for the vignette.

Usage

ind_BCa_nt4BC

Format

Logical vector of length 60.

References

Régression Bêta PLS. (French) [PLS Beta regression.], F. Bertrand, N. Meyer, M. Beau-Faller, K. El Bayed, N. Izzie-J., M. Maumy-Bertrand, (2013), J. SFdS, 154(3):143-159

Partial Least Squares Regression for Beta Regression Models. F. Bertrand, M. Maumy (2021). useR! 2021, Zurich.

Examples

data(ind_BCa_nt4BC)
## maybe str(ind_BCa_nt4BC) ; plot(ind_BCa_nt4BC) ...

ind_BCa_nt4BR

Description

Variable selection results for the vignette.

Usage

ind_BCa_nt4BR

Format

Logical vector of length 60.

References

Régression Bêta PLS. (French) [PLS Beta regression.], F. Bertrand, N. Meyer, M. Beau-Faller, K. El Bayed, N. Izzie-J., M. Maumy-Bertrand, (2013), J. SFdS, 154(3):143-159

Partial Least Squares Regression for Beta Regression Models. F. Bertrand, M. Maumy (2021). useR! 2021, Zurich.

Examples

data(ind_BCa_nt4BR)
## maybe str(ind_BCa_nt4BR) ; plot(ind_BCa_nt4BR) ...

ind_BCa_nt5BC

Description

Variable selection results for the vignette.

Usage

ind_BCa_nt5BC

Format

Logical vector of length 60.

References

Régression Bêta PLS. (French) [PLS Beta regression.], F. Bertrand, N. Meyer, M. Beau-Faller, K. El Bayed, N. Izzie-J., M. Maumy-Bertrand, (2013), J. SFdS, 154(3):143-159

Partial Least Squares Regression for Beta Regression Models. F. Bertrand, M. Maumy (2021). useR! 2021, Zurich.

Examples

data(ind_BCa_nt5BC)
## maybe str(ind_BCa_nt5BC) ; plot(ind_BCa_nt5BC) ...

ind_BCa_nt5BR

Description

Variable selection results for the vignette.

Usage

ind_BCa_nt5BR

Format

Logical vector of length 60.

References

Régression Bêta PLS. (French) [PLS Beta regression.], F. Bertrand, N. Meyer, M. Beau-Faller, K. El Bayed, N. Izzie-J., M. Maumy-Bertrand, (2013), J. SFdS, 154(3):143-159

Partial Least Squares Regression for Beta Regression Models. F. Bertrand, M. Maumy (2021). useR! 2021, Zurich.

Examples

data(ind_BCa_nt5BR)
## maybe str(ind_BCa_nt5BR) ; plot(ind_BCa_nt5BR) ...

ind_BCa_nt6BC

Description

Variable selection results for the vignette.

Usage

ind_BCa_nt6BC

Format

Logical vector of length 60.

References

Régression Bêta PLS. (French) [PLS Beta regression.], F. Bertrand, N. Meyer, M. Beau-Faller, K. El Bayed, N. Izzie-J., M. Maumy-Bertrand, (2013), J. SFdS, 154(3):143-159

Partial Least Squares Regression for Beta Regression Models. F. Bertrand, M. Maumy (2021). useR! 2021, Zurich.

Examples

data(ind_BCa_nt6BC)
## maybe str(ind_BCa_nt6BC) ; plot(ind_BCa_nt6BC) ...

ind_BCa_nt6BR

Description

Variable selection results for the vignette.

Usage

ind_BCa_nt6BR

Format

Logical vector of length 60.

References

Régression Bêta PLS. (French) [PLS Beta regression.], F. Bertrand, N. Meyer, M. Beau-Faller, K. El Bayed, N. Izzie-J., M. Maumy-Bertrand, (2013), J. SFdS, 154(3):143-159

Partial Least Squares Regression for Beta Regression Models. F. Bertrand, M. Maumy (2021). useR! 2021, Zurich.

Examples

data(ind_BCa_nt6BR)
## maybe str(ind_BCa_nt6BR) ; plot(ind_BCa_nt6BR) ...

Extracts and computes information criteria and fits statistics for kfold cross validated partial least squares beta regression models

Description

This function extracts and computes information criteria and fits statistics for kfold cross validated partial least squares beta regression models for both formula or classic specifications of the model.

Usage

kfolds2CVinfos_beta(pls_kfolds, MClassed = FALSE)

Arguments

Details

The Mclassed option should only set to TRUE if the response is binary.

Value

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

References

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215

See Also

kfolds2coeff, kfolds2Pressind, kfolds2Press, kfolds2Mclassedind and kfolds2Mclassed to extract and transforms results from kfold cross validation.

Examples

## Not run: 
data("GasolineYield",package="betareg")
bbb <- PLS_beta_kfoldcv_formula(yield~.,data=GasolineYield,nt=3,modele="pls-beta")
kfolds2CVinfos_beta(bbb)

## End(Not run)

Computes Predicted Chisquare for kfold cross validated partial least squares beta regression models.

Description

This function computes Predicted Chisquare for kfold cross validated partial least squares beta regression models.

Usage

kfolds2Chisq(pls_kfolds)

Arguments

Value

Note

Use PLS_beta_kfoldcv to create kfold cross validated partial least squares regression glm and beta models.

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

References

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215

See Also

kfolds2coeff, kfolds2Press, kfolds2Pressind, kfolds2Chisqind, kfolds2Mclassedind and kfolds2Mclassed to extract and transforms results from kfold cross validation.

Examples

## Not run: 
data("GasolineYield",package="betareg")
yGasolineYield <- GasolineYield$yield
XGasolineYield <- GasolineYield[,2:5]
bbb <- PLS_beta_kfoldcv(yGasolineYield,XGasolineYield,nt=3,modele="pls-beta")
kfolds2Chisq(bbb)

## End(Not run)

Computes individual Predicted Chisquare for kfold cross validated partial least squares beta regression models.

Description

This function computes individual Predicted Chisquare for kfold cross validated partial least squares beta regression models.

Usage

kfolds2Chisqind(pls_kfolds)

Arguments

Value

Note

Use PLS_beta_kfoldcv to create kfold cross validated partial least squares regression glm models.

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

References

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215

See Also

kfolds2coeff, kfolds2Press, kfolds2Pressind, kfolds2Chisq, kfolds2Mclassedind and kfolds2Mclassed to extract and transforms results from kfold cross validation.

Examples

## Not run: 
data("GasolineYield",package="betareg")
yGasolineYield <- GasolineYield$yield
XGasolineYield <- GasolineYield[,2:5]
bbb <- PLS_beta_kfoldcv(yGasolineYield,XGasolineYield,nt=3,modele="pls-beta")
kfolds2Chisqind(bbb)

## End(Not run)

Bootstrap distribution of a 3 components model

Description

A precomputed bootstrap distribution of the coefficients of a model used in the vignette.

Usage

modpls.boot3

Format

a class boot object

References

Régression Bêta PLS. (French) [PLS Beta regression.], F. Bertrand, N. Meyer, M. Beau-Faller, K. El Bayed, N. Izzie-J., M. Maumy-Bertrand, (2013), J. SFdS, 154(3):143-159

Partial Least Squares Regression for Beta Regression Models. F. Bertrand, M. Maumy (2021). useR! 2021, Zurich.

Examples

data(modpls.boot3)
str(modpls.boot3)
plot(modpls.boot3)

A plsRbetamodel model on a data subset

Description

A precomputed four components plsRbetamodel fitted to a subset of an example dataset and used in the vignette.

Usage

modpls_sub4

Format

a class plsRbetamodel object

References

Régression Bêta PLS. (French) [PLS Beta regression.], F. Bertrand, N. Meyer, M. Beau-Faller, K. El Bayed, N. Izzie-J., M. Maumy-Bertrand, (2013), J. SFdS, 154(3):143-159

Partial Least Squares Regression for Beta Regression Models. F. Bertrand, M. Maumy (2021). useR! 2021, Zurich.

Examples

data(modpls_sub4)
str(modpls_sub4)

Coefficients function for permutation bootstrap techniques

Description

A function passed to boot to perform bootstrap.

Usage

permcoefs.plsRbeta(
  dataset,
  ind,
  nt,
  modele,
  family = NULL,
  method = "logistic",
  link = "logit",
  link.phi = NULL,
  type = "ML",
  maxcoefvalues,
  ifbootfail,
  verbose = TRUE
)

Arguments

Value

Estimates on a bootstrap sample.

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

References

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215

See Also

See also bootplsbeta.

Examples

data("GasolineYield",package="betareg")

GazYield.boot <- bootplsbeta(plsRbeta(yield~.,data=GasolineYield,nt=3,
modele="pls-beta", verbose=FALSE), sim="ordinary", stype="i", R=250, statistic=permcoefs.plsRbeta)

Raw coefficients function for permutation bootstrap techniques

Description

A function passed to boot to perform bootstrap.

Usage

permcoefs.plsRbeta.raw(
  dataset,
  ind,
  nt,
  modele,
  family = NULL,
  method = "logistic",
  link = "logit",
  link.phi = NULL,
  type = "ML",
  maxcoefvalues,
  ifbootfail,
  verbose = TRUE
)

Arguments

Value

Estimates on a bootstrap sample.

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

References

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215

See Also

See also bootplsbeta.

Examples

data("GasolineYield",package="betareg")
modplsbeta <- plsRbeta(yield~.,data=GasolineYield,nt=3, modele="pls-beta")
GazYield.boot.raw <- bootplsbeta(modplsbeta, sim="permutation", stype="i", 
R=250, statistic=coefs.plsRbeta.raw)

Coefficients for permutation bootstrap computations of PLSBeta models

Description

A function passed to boot to perform bootstrap.

Usage

permcoefs.plsRbetanp(
  dataRepYtt,
  ind,
  nt,
  modele,
  family = NULL,
  maxcoefvalues,
  wwetoile,
  ifbootfail
)

Arguments

Value

estimates on a bootstrap sample or ifbootfail value if the bootstrap computation fails.

Note

~~some notes~~

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

See Also

See also bootplsbeta

Examples

data("GasolineYield",package="betareg")
modplsbeta <- plsRbeta(yield~.,data=GasolineYield,nt=3, modele="pls-beta")
bootplsbeta(modplsbeta, R=250, statistic=permcoefs.plsRbetanp, typeboot="fmodel_np")

Partial least squares Regression beta regression models

Description

This function implements Partial least squares Regression generalized linear models complete or incomplete datasets.

Usage

plsRbeta(object, ...)
## Default S3 method:
plsRbetamodel(object,dataX,nt=2,limQ2set=.0975,
dataPredictY=dataX,modele="pls",family=NULL,typeVC="none",EstimXNA=FALSE,
scaleX=TRUE,scaleY=NULL,pvals.expli=FALSE,alpha.pvals.expli=.05,
MClassed=FALSE,tol_Xi=10^(-12),weights,method,sparse=FALSE,sparseStop=TRUE,
naive=FALSE,link=NULL,link.phi=NULL,type="ML",verbose=TRUE, ...)
## S3 method for class 'formula'
plsRbetamodel(object,data=NULL,nt=2,limQ2set=.0975,
dataPredictY,modele="pls",family=NULL,typeVC="none",EstimXNA=FALSE,
scaleX=TRUE,scaleY=NULL,pvals.expli=FALSE,alpha.pvals.expli=.05,
MClassed=FALSE,tol_Xi=10^(-12),weights,subset,start=NULL,etastart,
mustart,offset,method="glm.fit",control= list(),contrasts=NULL,
sparse=FALSE,sparseStop=TRUE,naive=FALSE,link=NULL,link.phi=NULL,type="ML",
verbose=TRUE, ...)

Arguments

Details

There are seven different predefined models with predefined link functions available :

"pls"

ordinary pls models

"pls-glm-Gamma"

glm gaussian with inverse link pls models

"pls-glm-gaussian"

glm gaussian with identity link pls models

"pls-glm-inverse-gamma"

glm binomial with square inverse link pls models

"pls-glm-logistic"

glm binomial with logit link pls models

"pls-glm-poisson"

glm poisson with log link pls models

"pls-glm-polr"

glm polr with logit link pls models

Using the "family=" option and setting "modele=pls-glm-family" allows changing the family and link function the same way as for the glm function. As a consequence user-specified families can also be used.

The gaussian family

accepts the links (as names) identity, log and inverse.

The binomial family

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

The Gamma family

accepts the links inverse, identity and log.

The poisson family

accepts the links log, identity, and sqrt.

The inverse.gaussian family

accepts the links 1/mu^2, inverse, identity and log.

The quasi family

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

The function power

can be used to create a power link function.

A typical predictor has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with any duplicates removed.

A specification of the form first:second indicates the the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.

The terms in the formula will be re-ordered so that main effects come first, followed by the interactions, all second-order, all third-order and so on: to avoid this pass a terms object as the formula.

Non-NULL weights can be used to indicate that different observations have different dispersions (with the values in weights being inversely proportional to the dispersions); or equivalently, when the elements of weights are positive integers w_i, that each response y_i is the mean of w_i unit-weight observations.

The default estimator for Degrees of Freedom is the Kramer and Sugiyama's one which only works for classical plsR models. For these models, Information criteria are computed accordingly to these estimations. Naive Degrees of Freedom and Information Criteria are also provided for comparison purposes. For more details, see Kraemer, N., Sugiyama M. (2010). "The Degrees of Freedom of Partial Least Squares Regression". preprint, http://arxiv.org/abs/1002.4112.

Value

Depends on the model that was used to fit the model.

Note

Use plsRbeta instead.

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

References

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215

See Also

plsR and plsRglm

Examples

data("GasolineYield",package="betareg")
modpls <- plsRbeta(yield~.,data=GasolineYield,nt=3,modele="pls-beta")
modpls$pp
modpls$Coeffs
modpls$Std.Coeffs
modpls$InfCrit
modpls$PredictY[1,]
rm("modpls")

data("GasolineYield",package="betareg")
yGasolineYield <- GasolineYield$yield
XGasolineYield <- GasolineYield[,2:5]
modpls <- plsRbeta(yGasolineYield,XGasolineYield,nt=3,modele="pls-beta")
modpls$pp
modpls$Coeffs
modpls$Std.Coeffs
modpls$InfCrit
modpls$PredictY[1,]
rm("modpls")

Print method for plsRbeta models

Description

This function provides a print method for the class "plsRbetamodel"

Usage

## S3 method for class 'plsRbetamodel'
print(x, ...)

Arguments

Value

NULL

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

References

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215

See Also

print

Examples

data("GasolineYield",package="betareg")
modpls <- plsRbeta(yield~.,data=GasolineYield,nt=3,modele="pls-beta")
print(modpls)

Print method for summaries of plsRbeta models

Description

This function provides a print method for the class "summary.plsRbetamodel"

Usage

## S3 method for class 'summary.plsRbetamodel'
print(x, ...)

Arguments

Value

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

References

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215

See Also

print and summary

Examples

data("GasolineYield",package="betareg")
modpls <- plsRbeta(yield~.,data=GasolineYield,nt=3,modele="pls-beta")
print(summary(modpls))

Data generating function for univariate beta plsR models

Description

This function generates a single univariate rate response value Y and a vector of explanatory variables (X_1,\ldots,X_{totdim}) drawn from a model with a given number of latent components.

Usage

simul_data_UniYX_beta(
  totdim,
  ncomp,
  disp = 1,
  link = "logit",
  type = "a",
  phi0 = 20
)

Arguments

Details

This function should be combined with the replicate function to give rise to a larger dataset. The algorithm used is a modification of a port of the one described in the article of Li which is a multivariate generalization of the algorithm of Naes and Martens.

Value

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

References

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215

T. Naes, H. Martens (1985). Comparison of prediction methods for multicollinear data. Commun. Stat., Simul., 14:545-576. <doi:10.1080/03610918508812458>

Baibing Li, Julian Morris, Elaine B. Martin (2002). Model selection for partial least squares regression, Chemometrics and Intelligent Laboratory Systems, 64:79-89. <doi:110.1016/S0169-7439(02)00051-5>

See Also

simul_data_UniYX

Examples

# logit link
layout(matrix(1:4,nrow=2))
hist(t(replicate(100,simul_data_UniYX_beta(4,4)))[,1])
hist(t(replicate(100,simul_data_UniYX_beta(4,4,disp=3)))[,1])
hist(t(replicate(100,simul_data_UniYX_beta(4,4,disp=5)))[,1])
hist(t(replicate(100,simul_data_UniYX_beta(4,4,disp=15)))[,1])
layout(1)

# probit link
layout(matrix(1:4,nrow=2))
hist(t(replicate(100,simul_data_UniYX_beta(4,4,link="probit")))[,1])
hist(t(replicate(100,simul_data_UniYX_beta(4,4,disp=3,link="probit")))[,1])
hist(t(replicate(100,simul_data_UniYX_beta(4,4,disp=5,link="probit")))[,1])
hist(t(replicate(100,simul_data_UniYX_beta(4,4,disp=15,link="probit")))[,1])
layout(1)

# cloglog link
layout(matrix(1:4,nrow=2))
hist(t(replicate(100,simul_data_UniYX_beta(4,4,link="cloglog")))[,1])
hist(t(replicate(100,simul_data_UniYX_beta(4,4,disp=3,link="cloglog")))[,1])
hist(t(replicate(100,simul_data_UniYX_beta(4,4,disp=5,link="cloglog")))[,1])
hist(t(replicate(100,simul_data_UniYX_beta(4,4,disp=15,link="cloglog")))[,1])
layout(1)

# cauchit link
layout(matrix(1:4,nrow=2))
hist(t(replicate(100,simul_data_UniYX_beta(4,4,link="cauchit")))[,1])
hist(t(replicate(100,simul_data_UniYX_beta(4,4,disp=3,link="cauchit")))[,1])
hist(t(replicate(100,simul_data_UniYX_beta(4,4,disp=5,link="cauchit")))[,1])
hist(t(replicate(100,simul_data_UniYX_beta(4,4,disp=15,link="cauchit")))[,1])
layout(1)

# loglog link
layout(matrix(1:4,nrow=2))
hist(t(replicate(100,simul_data_UniYX_beta(4,4,link="loglog")))[,1])
hist(t(replicate(100,simul_data_UniYX_beta(4,4,disp=3,link="loglog")))[,1])
hist(t(replicate(100,simul_data_UniYX_beta(4,4,disp=5,link="loglog")))[,1])
hist(t(replicate(100,simul_data_UniYX_beta(4,4,disp=15,link="loglog")))[,1])
layout(1)

# log link
layout(matrix(1:4,nrow=2))
hist(t(replicate(100,simul_data_UniYX_beta(4,4,link="log")))[,1])
hist(t(replicate(100,simul_data_UniYX_beta(4,4,disp=3,link="log")))[,1])
hist(t(replicate(100,simul_data_UniYX_beta(4,4,disp=5,link="log")))[,1])
hist(t(replicate(100,simul_data_UniYX_beta(4,4,disp=15,link="log")))[,1])
layout(1)

Summary method for plsRbeta models

Description

This function provides a summary method for the class "plsRbetamodel"

Usage

## S3 method for class 'plsRbetamodel'
summary(object, ...)

Arguments

Value

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

References

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215

See Also

summary

Examples

data("GasolineYield",package="betareg")
modpls <- plsRbeta(yield~.,data=GasolineYield,nt=3,modele="pls-beta")
summary(modpls)

Non-parametric tilted bootstrap for PLS beta regression models

Description

Provides a wrapper for the bootstrap function tilt.boot from the boot R package.
Implements non-parametric tilted bootstrap for PLS beta regression models by case resampling : the tilt.boot function will run an initial bootstrap with equal resampling probabilities (if required) and will use the output of the initial run to find resampling probabilities which put the value of the statistic at required values. It then runs an importance resampling bootstrap using the calculated probabilities as the resampling distribution.

Usage

tilt.bootplsbeta(
  object,
  typeboot = "plsmodel",
  statistic = coefs.plsRbeta,
  R = c(499, 250, 250),
  alpha = c(0.025, 0.975),
  sim = "ordinary",
  stype = "i",
  index = 1,
  stabvalue = 1e+06
)

Arguments

Value

An object of class "boot".

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

References

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215

See Also

tilt.boot

Examples

data("GasolineYield",package="betareg")
yGasolineYield <- GasolineYield$yield
XGasolineYield <- GasolineYield[,2:5]
modplsRbeta <- plsRbeta(yGasolineYield, XGasolineYield, nt=3, 
modele="pls-beta")
# GazYield.tilt.boot <- tilt.bootplsbeta(modplsRbeta,
# statistic=coefs.plsRbeta, R=c(499, 100, 100), 
# alpha=c(0.025, 0.975), sim="balanced", stype="i", index=1)
# boxplots.bootpls(GazYield.tilt.boot,1:2)