plsRbeta {plsRbeta} | R Documentation |
Partial least squares Regression beta regression models
Description
This function implements Partial least squares Regression generalized linear models complete or incomplete datasets.
Usage
plsRbeta(object, ...)
## Default S3 method:
plsRbetamodel(object,dataX,nt=2,limQ2set=.0975,
dataPredictY=dataX,modele="pls",family=NULL,typeVC="none",EstimXNA=FALSE,
scaleX=TRUE,scaleY=NULL,pvals.expli=FALSE,alpha.pvals.expli=.05,
MClassed=FALSE,tol_Xi=10^(-12),weights,method,sparse=FALSE,sparseStop=TRUE,
naive=FALSE,link=NULL,link.phi=NULL,type="ML",verbose=TRUE, ...)
## S3 method for class 'formula'
plsRbetamodel(object,data=NULL,nt=2,limQ2set=.0975,
dataPredictY,modele="pls",family=NULL,typeVC="none",EstimXNA=FALSE,
scaleX=TRUE,scaleY=NULL,pvals.expli=FALSE,alpha.pvals.expli=.05,
MClassed=FALSE,tol_Xi=10^(-12),weights,subset,start=NULL,etastart,
mustart,offset,method="glm.fit",control= list(),contrasts=NULL,
sparse=FALSE,sparseStop=TRUE,naive=FALSE,link=NULL,link.phi=NULL,type="ML",
verbose=TRUE, ...)
Arguments
object |
a response (training) dataset or an object of class " |
dataX |
predictor(s) (training) dataset |
data |
an optional data frame, list or environment (or object coercible by |
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 ( |
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 |
typeVC |
type of leave one out cross validation. For back compatibility purpose.
|
EstimXNA |
only for |
scaleX |
scale the predictor(s) : must be set to TRUE for |
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 |
weights |
an optional vector of 'prior weights' to be used in the fitting process. Should be |
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 |
method |
the method to be used in fitting the model. The default method |
control |
a list of parameters for controlling the fitting process. For |
contrasts |
an optional list. See the |
sparse |
should the coefficients of non-significant predictors (< |
sparseStop |
should component extraction stop when no significant predictors (< |
naive |
Use the naive estimates for the Degrees of Freedom in plsR? Default is |
link |
character specification of the link function in the mean model (mu). Currently, " |
link.phi |
character specification of the link function in the precision model (phi). Currently, " |
type |
character specification of the type of estimator. Currently, maximum likelihood (" |
verbose |
should info messages be displayed ? |
... |
arguments to pass to |
Details
There are seven different predefined models with predefined link functions available :
"pls"
ordinary pls models
"pls-glm-Gamma"
glm gaussian with inverse link pls models
"pls-glm-gaussian"
glm gaussian with identity link pls models
"pls-glm-inverse-gamma"
glm binomial with square inverse link pls models
"pls-glm-logistic"
glm binomial with logit link pls models
"pls-glm-poisson"
glm poisson with log link pls models
"pls-glm-polr"
glm polr with logit link pls models
Using the "family="
option and setting "modele=pls-glm-family"
allows changing the family and link function the same way as for the glm
function. As a consequence user-specified families can also be used.
- The
gaussian
family accepts the links (as names)
identity
,log
andinverse
.- The
binomial
family accepts the links
logit
,probit
,cauchit
, (corresponding to logistic, normal and Cauchy CDFs respectively)log
andcloglog
(complementary log-log).- The
Gamma
family accepts the links
inverse
,identity
andlog
.- The
poisson
family accepts the links
log
,identity
, andsqrt
.- The
inverse.gaussian
family accepts the links
1/mu^2
,inverse
,identity
andlog
.- The
quasi
family accepts the links
logit
,probit
,cloglog
,identity
,inverse
,log
,1/mu^2
andsqrt
.- The function
power
can be used to create a power link function.
A typical predictor has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with any duplicates removed.
A specification of the form first:second indicates the the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.
The terms in the formula will be re-ordered so that main effects come first, followed by the interactions, all second-order, all third-order and so on: to avoid this pass a terms object as the formula.
Non-NULL weights can be used to indicate that different observations have different dispersions (with the values in weights being inversely proportional to the dispersions); or equivalently, when the elements of weights are positive integers w_i, that each response y_i is the mean of w_i unit-weight observations.
The default estimator for Degrees of Freedom is the Kramer and Sugiyama's one which only works for classical plsR models. For these models, Information criteria are computed accordingly to these estimations. Naive Degrees of Freedom and Information Criteria are also provided for comparison purposes. For more details, see Kraemer, N., Sugiyama M. (2010). "The Degrees of Freedom of Partial Least Squares Regression". preprint, http://arxiv.org/abs/1002.4112.
Value
Depends on the model that was used to fit the model.
Note
Use plsRbeta
instead.
Author(s)
Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/
References
Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/215
See Also
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")