# classif.gsam

##### Classification Fitting Functional Generalized Additive Models

Computes functional classification using functional (and non functional) explanatory variables by basis representation.

- Keywords
- classif

##### Usage

```
classif.gsam(
formula,
data,
family = binomial(),
weights = "equal",
basis.x = NULL,
CV = FALSE,
prob = 0.5,
type = "1vsall",
...
)
```

##### 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 details of model specification are given under`Details`

.- data
List that containing the variables in the model.

- 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.)- 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.

- basis.x
List of basis for functional explanatory data estimation.

- CV
=TRUE, Cross-validation (CV) is done.

- prob
probability value used for binari 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.- …
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 and non functional explanatory variables, as
`glm`

.

Functional covariates of class `fdata`

or `fd`

are introduced in
the following items in the `data`

list. `basis.x`

is a list of
basis for represent each functional covariate. The basis object can be
created by the function: `create.pc.basis`

, `pca.fd`

`create.pc.basis`

, `create.fdata.basis`

o
`create.basis`

.

##### 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.`type`

Type of classification scheme: 1 vs all or majority voting.

##### Note

If the formula only contains a non functional explanatory variables
(multivariate covariates), the function compute a standard `glm`

procedure.

##### References

Ramsay, James O., and Silverman, Bernard W. (2006), *
Functional Data Analysis*, 2nd ed., Springer, New York.

McCullagh and Nelder (1989), *Generalized Linear Models* 2nd ed.
Chapman and Hall.

Venables, W. N. and Ripley, B. D. (2002) *Modern Applied Statistics
with S*, New York: Springer.
Regression for R. R News 1(2):20-25

##### See Also

See Also as: `fregre.gsam`

. Alternative method:
`classif.np`

, `classif.glm`

and
`classif.gkam`

.

##### Examples

```
# NOT RUN {
require(fda.usc)
data(phoneme)
mlearn<-phoneme[["learn"]]
glearn<-phoneme[["classlearn"]]
mtest<-phoneme[["test"]]
gtest<-phoneme[["classtest"]]
dataf<-data.frame(glearn)
dat=list("df"=dataf,"x"=mlearn)
a1<-classif.gsam(glearn~s(x,k=3),data=dat)
summary(a1)
newdat<-list("x"=mtest)
p1<-predict(a1,newdat)
table(gtest,p1)
sum(p1==gtest)/250
# }
```

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