Package 'plsRbeta' reference manual

Title:	Partial Least Squares Regression for Beta Regression Models
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.
Authors:	Frederic Bertrand [cre, aut] , Myriam Maumy-Bertrand [aut]
Maintainer:	Frederic Bertrand <[email protected]>
License:	GPL-3
Version:	0.3.0
Built:	2025-01-25 03:04:20 UTC
Source:	https://github.com/fbertran/plsrbeta

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")

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")

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

Arguments

`object`	An object of class `plsRbetamodel` to bootstrap
`typeboot`	The type of bootstrap. Either (Y,X) boostrap (`typeboot="plsmodel"`) or (Y,T) bootstrap (`typeboot="fmodel_np"`). Defaults to (Y,T) resampling.
`R`	The number of bootstrap replicates. Usually this will be a single positive integer. For importance resampling, some resamples may use one set of weights and others use a different set of weights. In this case `R` would be a vector of integers where each component gives the number of resamples from each of the rows of weights.
`statistic`	A function which when applied to data returns a vector containing the statistic(s) of interest. `statistic` must take at least two arguments. The first argument passed will always be the original data. The second will be a vector of indices, frequencies or weights which define the bootstrap sample. Further, if predictions are required, then a third argument is required which would be a vector of the random indices used to generate the bootstrap predictions. Any further arguments can be passed to statistic through the `...` argument.
`sim`	A character string indicating the type of simulation required. Possible values are `"ordinary"` (the default), `"balanced"`, `"permutation"`, or `"antithetic"`.
`stype`	A character string indicating what the second argument of `statistic` represents. Possible values of stype are `"i"` (indices - the default), `"f"` (frequencies), or `"w"` (weights).
`stabvalue`	A value to hard threshold bootstrap estimates computed from atypical resamplings.
`...`	Other named arguments for `statistic` which are passed unchanged each time it is called. Any such arguments to `statistic` should follow the arguments which `statistic` is required to have for the simulation. Beware of partial matching to arguments of `boot` listed above.

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
[email protected]
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

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)


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

Arguments

`dataset`	dataset to resample
`ind`	indices for resampling
`nt`	number of components to use
`modele`	name of the PLS glm or PLS beta model to be fitted (`"pls"`, `"pls-glm-Gamma"`, `"pls-glm-gaussian"`, `"pls-glm-inverse.gaussian"`, `"pls-glm-logistic"`, `"pls-glm-poisson"`, `"pls-glm-polr"`, `"pls-beta"`). Use `"modele=pls-glm-family"` to enable the `family` option.
`family`	family to use if GLM model, see plsRbeta
`method`	method for beta regression
`link`	link for beta regression
`link.phi`	link.phi for beta regression
`type`	type of estimates
`maxcoefvalues`	maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples
`ifbootfail`	value to return if the estimation fails on a bootstrap sample
`verbose`	should info messages be displayed ?

Value

Coefficients' Estimates on a sample.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Examples


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


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

Arguments

`dataset`	dataset to resample
`ind`	indices for resampling
`nt`	number of components to use
`modele`	name of the PLS glm or PLS beta model to be fitted (`"pls"`, `"pls-glm-Gamma"`, `"pls-glm-gaussian"`, `"pls-glm-inverse.gaussian"`, `"pls-glm-logistic"`, `"pls-glm-poisson"`, `"pls-glm-polr"`, `"pls-beta"`). Use `"modele=pls-glm-family"` to enable the `family` option.
`family`	family to use if GLM model, see plsRbeta
`method`	method for beta regression
`link`	link for beta regression
`link.phi`	link.phi for beta regression
`type`	type of estimates
`maxcoefvalues`	maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples
`ifbootfail`	value to return if the estimation fails on a bootstrap sample
`verbose`	should info messages be displayed ?

Value

Coefficients' Estimates on a sample.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Examples


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


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

Arguments

`dataRepYtt`	components' coordinates to bootstrap
`ind`	indices for resampling
`nt`	number of components to use
`modele`	type of modele to use, see plsRbeta
`family`	glm family to use, see plsRbeta
`method`	method for beta regression
`link`	link for beta regression
`link.phi`	link.phi for beta regression
`type`	type of estimates
`verbose`	should info messages be displayed ?
`maxcoefvalues`	maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples
`wwetoile`	values of the Wstar matrix in the original fit
`ifbootfail`	value to return if the estimation fails on a bootstrap sample

Value

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

Note

~~some notes~~

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

Examples


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

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
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)

data(colon)
str(colon)

ind_BCa_nt1BC

Description

Variable selection results for the vignette.

Usage

ind_BCa_nt1BC
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) ...

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
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) ...

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
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) ...

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
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) ...

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
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) ...

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
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) ...

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
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) ...

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
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) ...

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
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) ...

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
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) ...

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
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) ...

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
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) ...

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
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) ...

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

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)
kfolds2Chisq(pls_kfolds)

Arguments

pls_kfolds

a kfold cross validated partial least squares regression glm model

Value

`list`	Total Predicted Chisquare vs number of components for the first group partition
`list()`	...
`list`	Total Predicted Chisquare vs number of components for the last group partition

Note

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

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

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)

## 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)
kfolds2Chisqind(pls_kfolds)

Arguments

pls_kfolds

a kfold cross validated partial least squares regression glm model

Value

`list`	Individual PChisq vs number of components for the first group partition
`list()`	...
`list`	Individual PChisq vs number of components for the last group partition

Note

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

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

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)

## 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)

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)
kfolds2CVinfos_beta(pls_kfolds, MClassed = FALSE)

Arguments

`pls_kfolds`	an object computed using `PLS_beta_kfoldcv`
`MClassed`	should number of miss classed be computed

Details

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

Value

`list`	table of fit statistics for first group partition
`list()`	...
`list`	table of fit statistics for last group partition

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

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)

## 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)

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
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)

data(modpls_sub4)
str(modpls_sub4)

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
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)

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

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

Arguments

`dataset`	dataset to resample
`ind`	indices for resampling
`nt`	number of components to use
`modele`	name of the PLS glm or PLS beta model to be fitted (`"pls"`, `"pls-glm-Gamma"`, `"pls-glm-gaussian"`, `"pls-glm-inverse.gaussian"`, `"pls-glm-logistic"`, `"pls-glm-poisson"`, `"pls-glm-polr"`, `"pls-beta"`). Use `"modele=pls-glm-family"` to enable the `family` option.
`family`	family to use if GLM model, see plsRbeta
`method`	method for beta regression
`link`	link for beta regression
`link.phi`	link.phi for beta regression
`type`	type of estimates
`maxcoefvalues`	maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples
`ifbootfail`	value to return if the estimation fails on a
`verbose`	should info messages be displayed ?

Value

Estimates on a bootstrap sample.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

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)


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

Arguments

`dataset`	dataset to resample
`ind`	indices for resampling
`nt`	number of components to use
`modele`	name of the PLS glm or PLS beta model to be fitted (`"pls"`, `"pls-glm-Gamma"`, `"pls-glm-gaussian"`, `"pls-glm-inverse.gaussian"`, `"pls-glm-logistic"`, `"pls-glm-poisson"`, `"pls-glm-polr"`, `"pls-beta"`). Use `"modele=pls-glm-family"` to enable the `family` option.
`family`	family to use if GLM model, see plsRbeta
`method`	method for beta regression
`link`	link for beta regression
`link.phi`	link.phi for beta regression
`type`	type of estimates
`maxcoefvalues`	maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples
`ifbootfail`	value to return if the estimation fails on a
`verbose`	should info messages be displayed ?

Value

Estimates on a bootstrap sample.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

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)


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

Arguments

`dataRepYtt`	components' coordinates to bootstrap
`ind`	indices for resampling
`nt`	number of components to use
`modele`	type of modele to use, see plsRbeta
`family`	glm family to use, see plsRbeta
`maxcoefvalues`	maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples
`wwetoile`	values of the Wstar matrix in the original fit
`ifbootfail`	value to return if the estimation fails on a bootstrap sample

Value

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

Note

~~some notes~~

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

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")

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 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
)
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

`dataY`	response (training) dataset
`dataX`	predictor(s) (training) dataset
`nt`	number of components to be extracted
`limQ2set`	limit value for the Q2
`dataPredictY`	predictor(s) (testing) dataset
`modele`	name of the PLS glm or PLS beta model to be fitted (`"pls"`, `"pls-glm-Gamma"`, `"pls-glm-gaussian"`, `"pls-glm-inverse.gaussian"`, `"pls-glm-logistic"`, `"pls-glm-poisson"`, `"pls-glm-polr"`, `"pls-beta"`). Use `"modele=pls-glm-family"` to enable the `family` option.
`family`	a description of the error distribution and link function to be used in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. (See `family` for details of family functions.) To use the family option, please set `modele="pls-glm-family"`. User defined families can also be defined. See details.
`typeVC`	type of leave one out cross validation. For back compatibility purpose. list("none") no cross validation list("standard") no cross validation list("missingdata") no cross validation list("adaptative") no cross validation
`EstimXNA`	only for `modele="pls"`. Set whether the missing X values have to be estimated.
`scaleX`	scale the predictor(s) : must be set to TRUE for `modele="pls"` and should be for glms pls.
`scaleY`	scale the response : Yes/No. Ignored since not always possible for glm responses.
`pvals.expli`	should individual p-values be reported to tune model selection ?
`alpha.pvals.expli`	level of significance for predictors when pvals.expli=TRUE
`MClassed`	number of missclassified cases, should only be used for binary responses
`tol_Xi`	minimal value for Norm2(Xi) and $\mathrm{det}(pp' \times pp)$ if there is any missing value in the `dataX`. It defaults to $10^{-12}$
`weights`	an optional vector of 'prior weights' to be used in the fitting process. Should be `NULL` or a numeric vector.
`method`	the link function for `pls-glm-polr`, logistic, probit, complementary log-log or cauchit (corresponding to a Cauchy latent variable).
`sparse`	should the coefficients of non-significant predictors (<`alpha.pvals.expli`) be set to 0
`sparseStop`	should component extraction stop when no significant predictors (<`alpha.pvals.expli`) are found
`naive`	use the naive estimates for the Degrees of Freedom in plsR? Default is `FALSE`.
`link`	character specification of the link function in the mean model (mu). Currently, "`logit`", "`probit`", "`cloglog`", "`cauchit`", "`log`", "`loglog`" are supported. Alternatively, an object of class "`link-glm`" can be supplied.
`link.phi`	character specification of the link function in the precision model (phi). Currently, "`identity`", "`log`", "`sqrt`" are supported. The default is "`log`" unless `formula` is of type `y~x` where the default is "`identity`" (for backward compatibility). Alternatively, an object of class "`link-glm`" can be supplied.
`type`	character specification of the type of estimator. Currently, maximum likelihood ("`ML`"), ML with bias correction ("`BC`"), and ML with bias reduction ("`BR`") are supported.
`verbose`	should info messages be displayed ?

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
[email protected]
https://fbertran.github.io/homepage/

References

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")


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
)
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

`formula`	an object of class "`formula`" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under 'Details'.
`data`	an optional data frame, list or environment (or object coercible by `as.data.frame` to a data frame) containing the variables in the model. If not found in `data`, the variables are taken from `environment(formula)`, typically the environment from which `plsRbeta` is called.
`nt`	number of components to be extracted
`limQ2set`	limit value for the Q2
`dataPredictY`	predictor(s) (testing) dataset
`modele`	name of the PLS glm or PLS beta model to be fitted (`"pls"`, `"pls-glm-Gamma"`, `"pls-glm-gaussian"`, `"pls-glm-inverse.gaussian"`, `"pls-glm-logistic"`, `"pls-glm-poisson"`, `"pls-glm-polr"`, `"pls-beta"`). Use `"modele=pls-glm-family"` to enable the `family` option.
`family`	a description of the error distribution and link function to be used in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. (See `family` for details of family functions.) To use the family option, please set `modele="pls-glm-family"`. User defined families can also be defined. See details.
`typeVC`	type of leave one out cross validation. For back compatibility purpose. list("none") no cross validation list("standard") no cross validation list("missingdata") no cross validation list("adaptative") no cross validation
`EstimXNA`	only for `modele="pls"`. Set whether the missing X values have to be estimated.
`scaleX`	scale the predictor(s) : must be set to TRUE for `modele="pls"` and should be for glms pls.
`scaleY`	scale the response : Yes/No. Ignored since not always possible for glm responses.
`pvals.expli`	should individual p-values be reported to tune model selection ?
`alpha.pvals.expli`	level of significance for predictors when pvals.expli=TRUE
`MClassed`	number of missclassified cases, should only be used for binary responses
`tol_Xi`	minimal value for Norm2(Xi) and $\mathrm{det}(pp' \times pp)$ if there is any missing value in the `dataX`. It defaults to $10^{-12}$
`weights`	an optional vector of 'prior weights' to be used in the fitting process. Should be `NULL` or a numeric vector.
`subset`	an optional vector specifying a subset of observations to be used in the fitting process.
`start`	starting values for the parameters in the linear predictor.
`etastart`	starting values for the linear predictor.
`mustart`	starting values for the vector of means.
`offset`	this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be `NULL` or a numeric vector of length equal to the number of cases. One or more `offset` terms can be included in the formula instead or as well, and if more than one is specified their sum is used. See `model.offset`.
`method`	for fitting glms with glm ( the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If `"model.frame"`, the model frame is returned. list("\"pls-glm-Gamma\"") the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If `"model.frame"`, the model frame is returned. , the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If `"model.frame"`, the model frame is returned. list("\"pls-glm-gaussian\"") the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If `"model.frame"`, the model frame is returned. , the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If `"model.frame"`, the model frame is returned. list("\"pls-glm-inverse.gaussian\"") the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If `"model.frame"`, the model frame is returned. , the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If `"model.frame"`, the model frame is returned. list("\"pls-glm-logistic\"") the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If `"model.frame"`, the model frame is returned. , the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If `"model.frame"`, the model frame is returned. list("\"pls-glm-poisson\"") the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If `"model.frame"`, the model frame is returned. , the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If `"model.frame"`, the model frame is returned. list("\"modele=pls-glm-family\"") the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If `"model.frame"`, the model frame is returned. ) the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If `"model.frame"`, the model frame is returned. list("pls-glm-polr") logistic, probit, complementary log-log or cauchit (corresponding to a Cauchy latent variable).
`control`	a list of parameters for controlling the fitting process. For `glm.fit` this is passed to `glm.control`.
`contrasts`	an optional list. See the `contrasts.arg` of `model.matrix.default`.
`sparse`	should the coefficients of non-significant predictors (<`alpha.pvals.expli`) be set to 0
`sparseStop`	should component extraction stop when no significant predictors (<`alpha.pvals.expli`) are found
`naive`	Use the naive estimates for the Degrees of Freedom in plsR? Default is `FALSE`.
`link`	character specification of the link function in the mean model (mu). Currently, "`logit`", "`probit`", "`cloglog`", "`cauchit`", "`log`", "`loglog`" are supported. Alternatively, an object of class "`link-glm`" can be supplied.
`link.phi`	character specification of the link function in the precision model (phi). Currently, "`identity`", "`log`", "`sqrt`" are supported. The default is "`log`" unless `formula` is of type `y~x` where the default is "`identity`" (for backward compatibility). Alternatively, an object of class "`link-glm`" can be supplied.
`type`	character specification of the type of estimator. Currently, maximum likelihood ("`ML`"), ML with bias correction ("`BC`"), and ML with bias reduction ("`BR`") are supported.
`verbose`	should info messages be displayed ?

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

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

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

Note

Use plsRbeta instead.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

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")


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
)
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

`dataY`	response (training) dataset
`dataX`	predictor(s) (training) dataset
`nt`	number of components to be extracted
`limQ2set`	limit value for the Q2
`modele`	name of the PLS glm or PLS beta model to be fitted (`"pls"`, `"pls-glm-Gamma"`, `"pls-glm-gaussian"`, `"pls-glm-inverse.gaussian"`, `"pls-glm-logistic"`, `"pls-glm-poisson"`, `"pls-glm-polr"`, `"pls-beta"`). Use `"modele=pls-glm-family"` to enable the `family` option.
`family`	a description of the error distribution and link function to be used in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. (See `family` for details of family functions.) To use the family option, please set `modele="pls-glm-family"`. User defined families can also be defined. See details.
`K`	number of groups
`NK`	number of times the group division is made
`grouplist`	to specify the members of the `K` groups
`random`	should the `K` groups be made randomly
`scaleX`	scale the predictor(s) : must be set to TRUE for `modele="pls"` and should be for glms pls.
`scaleY`	scale the response : Yes/No. Ignored since non always possible for glm responses.
`keepcoeffs`	shall the coefficients for each model be returned
`keepfolds`	shall the groups' composition be returned
`keepdataY`	shall the observed value of the response for each one of the predicted value be returned
`keepMclassed`	shall the number of miss classed be returned (unavailable)
`tol_Xi`	minimal value for Norm2(Xi) and $\mathrm{det}(pp' \times pp)$ if there is any missing value in the `dataX`. It defaults to $10^{-12}$
`weights`	an optional vector of 'prior weights' to be used in the fitting process. Should be `NULL` or a numeric vector.
`method`	logistic, probit, complementary log-log or cauchit (corresponding to a Cauchy latent variable).
`link`	character specification of the link function in the mean model (mu). Currently, "`logit`", "`probit`", "`cloglog`", "`cauchit`", "`log`", "`loglog`" are supported. Alternatively, an object of class "`link-glm`" can be supplied.
`link.phi`	character specification of the link function in the precision model (phi). Currently, "`identity`", "`log`", "`sqrt`" are supported. The default is "`log`" unless `formula` is of type `y~x` where the default is "`identity`" (for backward compatibility). Alternatively, an object of class "`link-glm`" can be supplied.
`type`	character specification of the type of estimator. Currently, maximum likelihood ("`ML`"), ML with bias correction ("`BC`"), and ML with bias reduction ("`BR`") are supported.
`verbose`	should info messages be displayed ?

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

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.

Value

`results_kfolds`	list of `NK`. Each element of the list sums up the results for a group division: list of `K` matrices of size about `nrow(dataX)/K * nt` with the predicted values for a growing number of components list() ... list of `K` matrices of size about `nrow(dataX)/K * nt` with the predicted values for a growing number of components
`folds`	list of `NK`. Each element of the list sums up the informations for a group division: list of `K` vectors of length about `nrow(dataX)` with the numbers of the rows of `dataX` that were used as a training set list() ... list of `K` vectors of length about `nrow(dataX)` with the numbers of the rows of `dataX` that were used as a training set
`dataY_kfolds`	list of `NK`. Each element of the list sums up the results for a group division: list of `K` matrices of size about `nrow(dataX)/K * 1` with the observed values of the response list() ... list of `K` matrices of size about `nrow(dataX)/K * 1` with the observed values of the response
`call`	the call of the function

Note

Works for complete and incomplete datasets.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

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)

## 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
)
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

`formula`	an object of class "`formula`" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under 'Details'.
`data`	an optional data frame, list or environment (or object coercible by `as.data.frame` to a data frame) containing the variables in the model. If not found in `data`, the variables are taken from `environment(formula)`, typically the environment from which `plsRglm` is called.
`nt`	number of components to be extracted
`limQ2set`	limit value for the Q2
`modele`	name of the PLS glm or PLS beta model to be fitted (`"pls"`, `"pls-glm-Gamma"`, `"pls-glm-gaussian"`, `"pls-glm-inverse.gaussian"`, `"pls-glm-logistic"`, `"pls-glm-poisson"`, `"pls-glm-polr"`, `"pls-beta"`). Use `"modele=pls-glm-family"` to enable the `family` option.
`family`	a description of the error distribution and link function to be used in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. (See `family` for details of family functions.) To use the family option, please set `modele="pls-glm-family"`. User defined families can also be defined. See details.
`K`	number of groups
`NK`	number of times the group division is made
`grouplist`	to specify the members of the `K` groups
`random`	should the `K` groups be made randomly
`scaleX`	scale the predictor(s) : must be set to TRUE for `modele="pls"` and should be for glms pls.
`scaleY`	scale the response : Yes/No. Ignored since non always possible for glm responses.
`keepcoeffs`	shall the coefficients for each model be returned
`keepfolds`	shall the groups' composition be returned
`keepdataY`	shall the observed value of the response for each one of the predicted value be returned
`keepMclassed`	shall the number of miss classed be returned (unavailable)
`tol_Xi`	minimal value for Norm2(Xi) and $\mathrm{det}(pp' \times pp)$ if there is any missing value in the `dataX`. It defaults to $10^{-12}$
`weights`	an optional vector of 'prior weights' to be used in the fitting process. Should be `NULL` or a numeric vector.
`subset`	an optional vector specifying a subset of observations to be used in the fitting process.
`start`	starting values for the parameters in the linear predictor.
`etastart`	starting values for the linear predictor.
`mustart`	starting values for the vector of means.
`offset`	this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be `NULL` or a numeric vector of length equal to the number of cases. One or more `offset` terms can be included in the formula instead or as well, and if more than one is specified their sum is used. See `model.offset`.
`method`	for fitting glms with glm ( the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If "model.frame", the model frame is returned. list("\"pls-glm-Gamma\"") the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If "model.frame", the model frame is returned. , the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If "model.frame", the model frame is returned. list("\"pls-glm-gaussian\"") the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If "model.frame", the model frame is returned. , the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If "model.frame", the model frame is returned. list("\"pls-glm-inverse.gaussian\"") the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If "model.frame", the model frame is returned. , the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If "model.frame", the model frame is returned. list("\"pls-glm-logistic\"") the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If "model.frame", the model frame is returned. , the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If "model.frame", the model frame is returned. list("\"pls-glm-poisson\"") the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If "model.frame", the model frame is returned. , the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If "model.frame", the model frame is returned. list("\"modele=pls-glm-family\"") the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If "model.frame", the model frame is returned. ) the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`. If "model.frame", the model frame is returned. list("pls-glm-polr") logistic, probit, complementary log-log or cauchit (corresponding to a Cauchy latent variable).
`control`	a list of parameters for controlling the fitting process. For `glm.fit` this is passed to `glm.control`.
`contrasts`	an optional list. See the `contrasts.arg` of `model.matrix.default`.
`sparse`	should the coefficients of non-significant predictors (<`alpha.pvals.expli`) be set to 0
`sparseStop`	should component extraction stop when no significant predictors (<`alpha.pvals.expli`) are found
`naive`	Use the naive estimates for the Degrees of Freedom in plsR? Default is `FALSE`.
`link`	character specification of the link function in the mean model (mu). Currently, "`logit`", "`probit`", "`cloglog`", "`cauchit`", "`log`", "`loglog`" are supported. Alternatively, an object of class "`link-glm`" can be supplied.
`link.phi`	character specification of the link function in the precision model (phi). Currently, "`identity`", "`log`", "`sqrt`" are supported. The default is "`log`" unless `formula` is of type `y~x` where the default is "`identity`" (for backward compatibility). Alternatively, an object of class "`link-glm`" can be supplied.
`type`	character specification of the type of estimator. Currently, maximum likelihood ("`ML`"), ML with bias correction ("`BC`"), and ML with bias reduction ("`BR`") are supported.
`verbose`	should info messages be displayed ?

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

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.

Value

`results_kfolds`	list of `NK`. Each element of the list sums up the results for a group division: list of `K` matrices of size about `nrow(dataX)/K * nt` with the predicted values for a growing number of components list() ... list of `K` matrices of size about `nrow(dataX)/K * nt` with the predicted values for a growing number of components
`folds`	list of `NK`. Each element of the list sums up the informations for a group division: list of `K` vectors of length about `nrow(dataX)` with the numbers of the rows of `dataX` that were used as a training set list() ... list of `K` vectors of length about `nrow(dataX)` with the numbers of the rows of `dataX` that were used as a training set
`dataY_kfolds`	list of `NK`. Each element of the list sums up the results for a group division: list of `K` matrices of size about `nrow(dataX)/K * 1` with the observed values of the response list() ... list of `K` matrices of size about `nrow(dataX)/K * 1` with the observed values of the response
`call`	the call of the function

Note

Work for complete and incomplete datasets.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

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)

## 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
)
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

`dataY`	response (training) dataset
`dataX`	predictor(s) (training) dataset
`nt`	number of components to be extracted
`dataPredictY`	predictor(s) (testing) dataset
`modele`	name of the PLS glm or PLS beta model to be fitted (`"pls"`, `"pls-glm-Gamma"`, `"pls-glm-gaussian"`, `"pls-glm-inverse.gaussian"`, `"pls-glm-logistic"`, `"pls-glm-poisson"`, `"pls-glm-polr"`, `"pls-beta"`). Use `"modele=pls-glm-family"` to enable the `family` option.
`family`	a description of the error distribution and link function to be used in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. (See `family` for details of family functions.) To use the family option, please set `modele="pls-glm-family"`. User defined families can also be defined. See details.
`scaleX`	scale the predictor(s) : must be set to TRUE for `modele="pls"` and should be for glms pls.
`scaleY`	scale the response : Yes/No. Ignored since non always possible for glm responses.
`keepcoeffs`	whether the coefficients of the linear fit on link scale of unstandardized eXplanatory variables should be returned or not.
`keepstd.coeffs`	whether the coefficients of the linear fit on link scale of standardized eXplanatory variables should be returned or not.
`tol_Xi`	minimal value for Norm2(Xi) and $\mathrm{det}(pp' \times pp)$ if there is any missing value in the `dataX`. It defaults to $10^{-12}$
`weights`	an optional vector of 'prior weights' to be used in the fitting process. Should be `NULL` or a numeric vector.
`method`	logistic, probit, complementary log-log or cauchit (corresponding to a Cauchy latent variable).
`link`	character specification of the link function in the mean model (mu). Currently, "`logit`", "`probit`", "`cloglog`", "`cauchit`", "`log`", "`loglog`" are supported. Alternatively, an object of class "`link-glm`" can be supplied.
`link.phi`	character specification of the link function in the precision model (phi). Currently, "`identity`", "`log`", "`sqrt`" are supported. The default is "`log`" unless `formula` is of type `y~x` where the default is "`identity`" (for backward compatibility). Alternatively, an object of class "`link-glm`" can be supplied.
`type`	character specification of the type of estimator. Currently, maximum likelihood ("`ML`"), ML with bias correction ("`BC`"), and ML with bias reduction ("`BR`") are supported.
`verbose`	should info messages be displayed ?

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

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.

Value

`valsPredict`	`nrow(dataPredictY) * nt` matrix of the predicted values
`list("coeffs")`	If the coefficients of the eXplanatory variables were requested: i.e. `keepcoeffs=TRUE`. `ncol(dataX) * 1` matrix of the coefficients of the the eXplanatory variables

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

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")


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")

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, ...)
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

`object`	a response (training) dataset or an object of class "`formula`" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under 'Details'.
`dataX`	predictor(s) (training) dataset
`data`	an optional data frame, list or environment (or object coercible by `as.data.frame` to a data frame) containing the variables in the model. If not found in `data`, the variables are taken from `environment(formula)`, typically the environment from which `plsRbeta` is called.
`nt`	number of components to be extracted
`limQ2set`	limit value for the Q2
`dataPredictY`	predictor(s) (testing) dataset
`modele`	name of the PLS glm or PLS beta model to be fitted (`"pls"`, `"pls-glm-Gamma"`, `"pls-glm-gaussian"`, `"pls-glm-inverse.gaussian"`, `"pls-glm-logistic"`, `"pls-glm-poisson"`, `"pls-glm-polr"`, `"pls-beta"`). Use `"modele=pls-glm-family"` to enable the `family` option.
`family`	a description of the error distribution and link function to be used in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. (See `family` for details of family functions.) To use the family option, please set `modele="pls-glm-family"`. User defined families can also be defined. See details.
`typeVC`	type of leave one out cross validation. For back compatibility purpose. `none` no cross validation `standard` no cross validation `missingdata` no cross validation `adaptative` no cross validation
`EstimXNA`	only for `modele="pls"`. Set whether the missing X values have to be estimated.
`scaleX`	scale the predictor(s) : must be set to TRUE for `modele="pls"` and should be for glms pls.
`scaleY`	scale the response : Yes/No. Ignored since non always possible for glm responses.
`pvals.expli`	should individual p-values be reported to tune model selection ?
`alpha.pvals.expli`	level of significance for predictors when pvals.expli=TRUE
`MClassed`	number of missclassified cases, should only be used for binary responses
`tol_Xi`	minimal value for Norm2(Xi) and $\mathrm{det}(pp' \times pp)$ if there is any missing value in the `dataX`. It defaults to $10^{-12}$
`weights`	an optional vector of 'prior weights' to be used in the fitting process. Should be `NULL` or a numeric vector.
`subset`	an optional vector specifying a subset of observations to be used in the fitting process.
`start`	starting values for the parameters in the linear predictor.
`etastart`	starting values for the linear predictor.
`mustart`	starting values for the vector of means.
`offset`	this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be `NULL` or a numeric vector of length equal to the number of cases. One or more `offset` terms can be included in the formula instead or as well, and if more than one is specified their sum is used. See `model.offset`.
`method`	the method to be used in fitting the model. The default method `"glm.fit"` uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as `glm.fit`.
`control`	a list of parameters for controlling the fitting process. For `glm.fit` this is passed to `glm.control`.
`contrasts`	an optional list. See the `contrasts.arg` of `model.matrix.default`.
`sparse`	should the coefficients of non-significant predictors (<`alpha.pvals.expli`) be set to 0
`sparseStop`	should component extraction stop when no significant predictors (<`alpha.pvals.expli`) are found
`naive`	Use the naive estimates for the Degrees of Freedom in plsR? Default is `FALSE`.
`link`	character specification of the link function in the mean model (mu). Currently, "`logit`", "`probit`", "`cloglog`", "`cauchit`", "`log`", "`loglog`" are supported. Alternatively, an object of class "`link-glm`" can be supplied.
`link.phi`	character specification of the link function in the precision model (phi). Currently, "`identity`", "`log`", "`sqrt`" are supported. The default is "`log`" unless `formula` is of type `y~x` where the default is "`identity`" (for backward compatibility). Alternatively, an object of class "`link-glm`" can be supplied.
`type`	character specification of the type of estimator. Currently, maximum likelihood ("`ML`"), ML with bias correction ("`BC`"), and ML with bias reduction ("`BR`") are supported.
`verbose`	should info messages be displayed ?
`...`	arguments to pass to `plsRmodel.default` or to `plsRmodel.formula`

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

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.

Value

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

Note

Use plsRbeta instead.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

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")

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

Arguments

`x`	an object of the class `"plsRbetamodel"`
`...`	not used

Value

NULL

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Examples


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

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

Arguments

`x`	an object of the class `"summary.plsRbetamodel"`
`...`	not used

Value

language

call of the model

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Examples


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

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

Arguments

`totdim`	Number of columns of the X vector (from `ncomp` to hardware limits)
`ncomp`	Number of latent components in the model (from 2 to 6)
`disp`	Tune the shape of the beta distribution (defaults to 1)
`link`	Character specification of the link function in the mean model (mu). Currently, "`logit`", "`probit`", "`cloglog`", "`cauchit`", "`log`", "`loglog`" are supported. Alternatively, an object of class "link-glm" can be supplied.
`type`	Simulation scheme
`phi0`	Simulation scheme "a" parameter

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

vector

$(Y,X_1,\ldots,X_{totdim})$

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

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>

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)


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

Arguments

`object`	an object of the class `"plsRbetamodel"`
`...`	further arguments to be passed to or from methods.

Value

call

function call of plsR beta models

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Examples


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

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
)
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

`object`	An object of class `plsRbetamodel` to bootstrap
`typeboot`	The type of bootstrap. Either (Y,X) boostrap (`typeboot="plsmodel"`) or (Y,T) bootstrap (`typeboot="fmodel_np"`). Defaults to (Y,T) resampling.
`statistic`	A function which when applied to data returns a vector containing the statistic(s) of interest. `statistic` must take at least two arguments. The first argument passed will always be the original data. The second will be a vector of indices, frequencies or weights which define the bootstrap sample. Further, if predictions are required, then a third argument is required which would be a vector of the random indices used to generate the bootstrap predictions. Any further arguments can be passed to statistic through the `...` argument.
`R`	The number of bootstrap replicates. Usually this will be a single positive integer. For importance resampling, some resamples may use one set of weights and others use a different set of weights. In this case `R` would be a vector of integers where each component gives the number of resamples from each of the rows of weights.
`alpha`	The alpha level to which tilting is required. This parameter is ignored if `R[1]` is 0 or if `theta` is supplied, otherwise it is used to find the values of `theta` as quantiles of the initial uniform bootstrap. In this case `R[1]` should be large enough that `min(c(alpha, 1-alpha))*R[1] > 5`, if this is not the case then a warning is generated to the effect that the `theta` are extreme values and so the tilted output may be unreliable.
`sim`	A character string indicating the type of simulation required. Possible values are `"ordinary"` (the default), `"balanced"`, `"permutation"`, or `"antithetic"`.
`stype`	A character string indicating what the second argument of `statistic` represents. Possible values of stype are `"i"` (indices - the default), `"f"` (frequencies), or `"w"` (weights).
`index`	The index of the statistic of interest in the output from `statistic`. By default the first element of the output of `statistic` is used.
`stabvalue`	A value to hard threshold bootstrap estimates computed from atypical resamplings.

Value

An object of class "boot".

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

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)


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)

Cancer infiltration rates

Description

This dataset features cancer infiltration rates and microsatellites data.

Usage

TxTum
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)

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
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)

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
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)

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

Package 'plsRbeta'

Help Index

plsRbeta-package

Description

References

Examples

Non-parametric Bootstrap for PLS beta regression models

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Coefficients function for bootstrap techniques

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Raw coefficients function for bootstrap techniques

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Coefficients for bootstrap computations of PLSBeta models

Description

Usage

Arguments

Value

Note

Author(s)

See Also

Examples

Tumor rate and spectral data

Description

Usage

Format

References

Examples

ind_BCa_nt1BC

Description

Usage

Format

References

Examples

ind_BCa_nt1BR

Description

Usage

Format

References

Examples

ind_BCa_nt2BC

Description

Usage

Format

References

Examples

ind_BCa_nt2BR

Description

Usage

Format

References

Examples

ind_BCa_nt3

Description

Usage

Format

References

Examples

ind_BCa_nt3BC