classif.np {fda.usc} | R Documentation |
Kernel Classifier from Functional Data
Description
Fits Nonparametric Supervised Classification for Functional Data.
Usage
classif.np(
group,
fdataobj,
h = NULL,
Ker = AKer.norm,
metric,
weights = "equal",
type.S = S.NW,
par.S = list(),
...
)
classif.knn(
group,
fdataobj,
knn = NULL,
metric,
weights = "equal",
par.S = list(),
...
)
classif.kernel(
group,
fdataobj,
h = NULL,
Ker = AKer.norm,
metric,
weights = "equal",
par.S = list(),
...
)
Arguments
group |
Factor of length n |
fdataobj |
|
h |
Vector of smoothing parameter or bandwidth. |
Ker |
Type of kernel used. |
metric |
Metric function, by default |
weights |
weights. |
type.S |
Type of smothing matrix |
par.S |
List of parameters for |
... |
Arguments to be passed for |
knn |
Vector of number of nearest neighbors considered. |
Details
Make the group classification of a training dataset using kernel or KNN
estimation: Kernel
.
Different types of metric funtions can be used.
Value
-
fdataobj
:fdata
class object. -
group
: Factor of lengthn
. -
group.est
: Estimated vector groups. -
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. -
h.opt
: Optimal smoothing parameter or bandwidht estimated. -
D
: Matrix of distances of the optimal quantile distancehh.opt
. -
prob.classification
: Probability of correct classification by group. -
misclassification
: Vector of probability of misclassification by number of neighborsknn
. -
h
: Vector of smoothing parameter or bandwidht. -
C
: A call of functionclassif.kernel
.
Note
If fdataobj
is a data.frame the function considers the case of
multivariate covariates.
metric.dist
function is used to
compute the distances between the rows of a data matrix (as
dist
function.
Author(s)
Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@udc.es
References
Ferraty, F. and Vieu, P. (2006). Nonparametric functional data analysis. Springer Series in Statistics, New York.
Ferraty, F. and Vieu, P. (2006). NPFDA in practice. Free access on line at https://www.math.univ-toulouse.fr/~ferraty/SOFTWARES/NPFDA/
See Also
See Also as predict.classif
Examples
## Not run:
data(phoneme)
mlearn <- phoneme[["learn"]]
glearn <- phoneme[["classlearn"]]
h <- 9:19
out <- classif.np(glearn,mlearn,h=h)
summary(out)
head(round(out$prob.group,4))
## End(Not run)