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,
...
)
```

formula

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`

.

data

List that containing the variables in the model.

weights

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.

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 `family`

for details of family functions.)

par.metric

List of arguments by covariable to pass to the
`metric`

function by covariable.

par.np

List of arguments to pass to the `fregre.np.cv`

function

offset

this can be used to specify an a priori known component to be included in the linear predictor during fitting.

prob

probability value used for binary discriminant.

type

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.

control

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) # }