classif.gkam: Classification Fitting Functional Generalized Kernel Additive... In fda.usc: Functional Data Analysis and Utilities for Statistical Computing

 classif.gkam R Documentation

Classification Fitting Functional Generalized Kernel Additive Models

Description

Computes functional classification using functional explanatory variables using backfitting algorithm.

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.

Author(s)

Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@udc.es

References

Febrero-Bande M. and Gonzalez-Manteiga W. (2012). Generalized Additive Models for Functional Data. TEST. Springer-Velag. doi: 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`.

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)

## End(Not run)
```

fda.usc documentation built on Oct. 17, 2022, 9:06 a.m.