Light version of PLS_beta for cross validation purposes either on
complete or incomplete datasets.
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
)nrow(dataPredictY) * nt matrix of the
predicted values
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
response (training) dataset
predictor(s) (training) dataset
number of components to be extracted
predictor(s) (testing) dataset
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.
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.
scale the predictor(s) : must be set to TRUE for
modele="pls" and should be for glms pls.
scale the response : Yes/No. Ignored since non always possible for glm responses.
whether the coefficients of the linear fit on link scale of unstandardized eXplanatory variables should be returned or not.
whether the coefficients of the linear fit on link scale of standardized eXplanatory variables should be returned or not.
minimal value for Norm2(Xi) and \(\mathrm{det}(pp' \times
pp)\) if there is any missing value in the dataX. It
defaults to \(10^{-12}\)
an optional vector of 'prior weights' to be used in the
fitting process. Should be NULL or a numeric vector.
logistic, probit, complementary log-log or cauchit (corresponding to a Cauchy latent variable).
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.
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.
character specification of the type of estimator. Currently,
maximum likelihood ("ML"), ML with bias correction ("BC"), and
ML with bias reduction ("BR") are supported.
should info messages be displayed ?
Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/
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 :
ordinary pls models
glm gaussian with inverse link pls models
glm gaussian with identity link pls models
glm binomial with square inverse link pls models
glm binomial with logit link pls models
glm poisson with log link pls models
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.
accepts
the links (as names) identity, log and
inverse.
accepts the links (as names)
identity, log and inverse.
accepts the
links (as names) identity, log and inverse.
accepts the links logit, probit, cauchit,
(corresponding to logistic, normal and Cauchy CDFs respectively) log
and cloglog (complementary log-log).
accepts
the links logit, probit, cauchit, (corresponding to
logistic, normal and Cauchy CDFs respectively) log and cloglog
(complementary log-log).
accepts the links logit,
probit, cauchit, (corresponding to logistic, normal and Cauchy
CDFs respectively) log and cloglog (complementary log-log).
accepts the links inverse, identity and
log.
accepts the links inverse,
identity and log.
accepts the links
inverse, identity and log.
accepts the
links log, identity, and
sqrt.
accepts the links log,
identity, and sqrt.
accepts the links
log, identity, and sqrt.
accepts the links
1/mu^2, inverse, identity and
log.
accepts the links 1/mu^2,
inverse, identity and log.
accepts the
links 1/mu^2, inverse, identity and log.
accepts the links logit, probit, cloglog,
identity, inverse, log, 1/mu^2 and
sqrt.
accepts the links logit,
probit, cloglog, identity, inverse, log,
1/mu^2 and sqrt.
accepts the links
logit, probit, cloglog, identity,
inverse, log, 1/mu^2 and sqrt.
can be used to create a power link function.
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.
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
PLS_beta for more detailed results,
PLS_beta_kfoldcv for cross validating models and
PLS_lm_wvc for the same function dedicated to plsR models
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")
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