Classification Fitting Functional Generalized Additive Models
Source:R/classif.gsam.R
classif.gsam.Rd
Computes functional classification using functional (and non functional) explanatory variables by basis representation.
Usage
classif.gsam(
formula,
data,
family = binomial(),
weights = "equal",
basis.x = NULL,
CV = FALSE,
prob = 0.5,
type = "1vsall",
...
)
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 details of model specification are given underDetails
.- 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 lengthn
, Weight values of each observation.
- 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
"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.- ...
Further arguments passed to or from other methods.
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 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.type
: Type of classification scheme: 1 vs all or majority voting.
Details
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.
Note
If the formula only contains a non functional explanatory variables
(multivariate covariates), the function compute a standard glm
procedure.
References
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
See Also as: fregre.gsam
.
Alternative method:
classif.np
, classif.glm
and
classif.gkam
.
Author
Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@udc.es
Examples
if (FALSE) { # \dontrun{
data(phoneme)
ldat <- ldata("df" = data.frame(y = phoneme[["classlearn"]]),
"x" = phoneme[["learn"]])
classifKgroups <- fda.usc:::classifKgroups
a1 <- classif.gsam( y ~ s(x,k=3),data=ldat)
summary(a1)
newldat <- ldata("df" = data.frame(y = phoneme[["classtest"]]),
"x" = phoneme[["test"]])
p1 <- predict(a1,newldat)
table(newldat$df$y,p1)
sum(p1==newldat$df$y)/250
} # }