View source: R/PLS_beta_kfoldcv.R
PLS_beta_kfoldcv | R Documentation |
This function implements kfold cross validation on complete or incomplete datasets for partial least squares beta regression models
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
)
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
( |
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 |
K |
number of groups |
NK |
number of times the group division is made |
grouplist |
to specify the members of the |
random |
should the |
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. |
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 |
weights |
an optional vector of 'prior weights' to be used in the
fitting process. Should be |
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, " |
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 ? |
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 :
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.
results_kfolds |
list of
|
folds |
list of
|
dataY_kfolds |
list of
|
call |
the call of the function |
Works for complete and incomplete datasets.
Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/
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
kfolds2coeff
,
kfolds2Pressind
, kfolds2Press
,
kfolds2Mclassedind
,
kfolds2Mclassed
and
kfolds2CVinfos_beta
to extract and transform results
from kfold cross validation.
## 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)
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