classif.gkam
Classification Fitting Functional Generalized Kernel Additive Models
Computes functional classification using functional explanatory variables using backfitting algorithm.
- Keywords
- classif
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 underDetails
.- 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 lengthn
, 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 ngroup.est
Estimated vector groupsprob.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.
References
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
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)
# }