Computes functional classification using functional explanatory variables using backfitting algorithm.
classif.gkam(
formula,
data,
weights = "equal",
family = binomial(),
par.metric = NULL,
par.np = NULL,
offset = NULL,
prob = 0.5,
type = "1vsall",
control = NULL,
...
)an object of class formula (or one that can be coerced
to that class): a symbolic description of the model to be fitted. The
procedure only considers functional covariates (not implemented for
non-functional covariates). The details of model specification are given
under Details.
List that containing the variables in the model.
Weights:
if character string ='equal' same weights for each observation (by default) and
='inverse' for inverse-probability of weighting.
if numeric vector of length n, Weight values of each observation.
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.)
List of arguments by covariable to pass to the
metric function by covariable.
List of arguments to pass to the fregre.np.cv function
this can be used to specify an a priori known component to be included in the linear predictor during fitting.
probability value used for binary discriminant.
If type is"1vsall" (by default)
a maximum probability scheme is applied: requires G binary classifiers.
If type is "majority" (only for multicalss classification G > 2)
a voting scheme is applied: requires G (G - 1) / 2 binary classifiers.
a list of parameters for controlling the fitting process, by default: maxit, epsilon, trace and inverse.
Further arguments passed to or from other methods.
Return gam object plus:
formula formula.
data List that containing the variables in the model.
group Factor of length n
group.est Estimated vector groups
prob.classification Probability of correct classification by group.
prob.group Matrix of predicted class probabilities. For each
functional point shows the probability of each possible group membership.
max.prob Highest probability of correct classification.
The first item in the data list is called "df" and is a data
frame with the response, as glm. Functional covariates of
class fdata are introduced in the following items in the data
list.
Febrero-Bande M. and Gonzalez-Manteiga W. (2012). Generalized Additive Models for Functional Data. TEST. Springer-Velag. http://dx.doi.org/10.1007/s11749-012-0308-0
McCullagh and Nelder (1989), Generalized Linear Models 2nd ed. Chapman and Hall.
Opsomer J.D. and Ruppert D.(1997). Fitting a bivariate additive model
by local polynomial regression.Annals of Statistics, 25, 186-211.
See Also as: fregre.gkam. Alternative method:
classif.glm.
# NOT RUN {
## Time-consuming: selection of 2 levels
data(phoneme)
mlearn<-phoneme[["learn"]][1:150]
glearn<-factor(phoneme[["classlearn"]][1:150])
dataf<-data.frame(glearn)
dat=list("df"=dataf,"x"=mlearn)
a1<-classif.gkam(glearn~x,data=dat)
summary(a1)
mtest<-phoneme[["test"]][1:150]
gtest<-factor(phoneme[["classtest"]][1:150])
newdat<-list("x"=mtest)
p1<-predict(a1,newdat)
table(gtest,p1)
# }