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

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`

. `basis.b`

is a list of basis for
represent each functional beta parameter. If `basis.x`

is a list of
functional principal components basis (see `create.pc.basis`

or
`pca.fd`

) the argument `basis.b`

is ignored.

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

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.

basis.b

List of basis for functional beta parameter estimation.

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.

prob

probability value used for binari discriminant.

CV

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

…

Further arguments passed to or from other methods.

Return `glm`

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.

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 as: `fregre.glm`

.
`classif.gsam`

and `classif.gkam`

.

# 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.glm(glearn~x, data = dat) newdat<-list("x"=mtest) p1<-predict(a1,newdat) table(gtest,p1) sum(p1==gtest)/250 # }