Classification Fitting Functional Generalized Additive Models
Computes functional classification using functional (and non functional) explanatory variables by basis representation.
classif.gsam( formula, data, family = binomial(), weights = "equal", basis.x = NULL, CV = FALSE, prob = 0.5, type = "1vsall", ... )
formula |
an object of class |
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 |
weights |
Weights:
|
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 |
... |
Further arguments passed to or from other methods. |
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.
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.
If the formula only contains a non functional explanatory variables
(multivariate covariates), the function compute a standard glm
procedure.
Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@usc.es
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.gsam.
Alternative method:
classif.np, classif.glm 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.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
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.