R: Partial least squares regression beta models with kfold cross...

PLS_beta_kfoldcv {plsRbeta}

R Documentation

Partial least squares regression beta models with kfold cross validation

Description

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

Usage

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

Arguments

`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

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

The: accepts the links (as names) identity, log and inverse.
list("gaussian"): accepts the links (as names) identity, log and inverse.
family: accepts the links (as names) identity, log and inverse.
The: accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).
list("binomial"): accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).
family: accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).
The: accepts the links inverse, identity and log.
list("Gamma"): accepts the links inverse, identity and log.
family: accepts the links inverse, identity and log.
The: accepts the links log, identity, and sqrt.
list("poisson"): accepts the links log, identity, and sqrt.
family: accepts the links log, identity, and sqrt.
The: accepts the links 1/mu^2, inverse, identity and log.
list("inverse.gaussian"): accepts the links 1/mu^2, inverse, identity and log.
family: accepts the links 1/mu^2, inverse, identity and log.
The: accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.
list("quasi"): accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.
family: accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.
The function: can be used to create a power link function.
list("power"): can be used to create a power link function.

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

Value

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

References

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

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)

[Package plsRbeta version 0.3.0 Index]