View source: R/PLS_beta_kfoldcv_formula.R
PLS_beta_kfoldcv_formula | R Documentation |
This function implements kfold cross validation on complete or incomplete datasets for partial least squares beta regression models (formula specification of the model).
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
)
formula |
an object of class " |
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 |
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 |
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 |
|
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 ? |
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.
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.
results_kfolds |
list of
|
folds |
list of
|
dataY_kfolds |
list of
|
call |
the call of the function |
Work 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")
bbb <- PLS_beta_kfoldcv_formula(yield~.,data=GasolineYield,nt=3,modele="pls-beta")
kfolds2CVinfos_beta(bbb)
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.