# classif.gkam

##### Classification Fitting Functional Generalized Kernel Additive Models

Computes functional classification using functional explanatory variables using backfitting algorithm.

- Keywords
- classif

##### Usage

```
classif.gkam(
formula,
data,
weights = "equal",
family = binomial(),
par.metric = NULL,
par.np = NULL,
offset = NULL,
prob = 0.5,
type = "1vsall",
control = NULL,
...
)
```

##### Arguments

- 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.

##### Details

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.

##### Value

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.

##### References

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

See Also as: `fregre.gkam`

. Alternative method:
`classif.glm`

.

##### Examples

```
# 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)
# }
```

*Documentation reproduced from package fda.usc, version 2.0.1, License: GPL-2*