Classification Fitting Functional Generalized Kernel Additive Models
Computes functional classification using functional explanatory variables using backfitting algorithm.
classif.gkam( formula, data, weights = "equal", family = binomial(), par.metric = NULL, par.np = NULL, offset = NULL, prob = 0.5, type = "1vsall", control = NULL, ... )
formula |
an object of class |
data |
List that containing the variables in the model. |
weights |
Weights:
|
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 |
par.metric |
List of arguments by covariable to pass to the
|
par.np |
List of arguments to pass to the |
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 |
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. |
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.
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.
Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@usc.es
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 as: fregre.gkam.
Alternative method:
classif.glm.
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.