prmdaCV: Cross validation method for PRM classification models.

In sprm: Sparse and Non-Sparse Partial Robust M Regression and Classification

Description

k-fold cross validation for the selection of the number of components for PRM classification.

Usage

prmdaCV(formula, data, as, nfold = 10, fun = "Hampel", probp1 = 0.95, hampelp2 = 0.975,
hampelp3 = 0.999, probp4 = 0.01, yweights = TRUE, 
class = c("regfit", "lda"), prior = c(0.5, 0.5), center = "median", scale = "qn", 
plot = TRUE, numit = 100, prec = 0.01)

Arguments

formula	a formula, e.g. group ~ X1 + X2 with group a factor with two levels and X1,X2 numeric variables.
data	a data frame or list which contains the variables given in formula. The response specified in the formula needs to be a numeric vector coding the class membership with 1 and-1 or a vector of factors with two levels.
as	a vector with positive integers, which are the number of PRM components to be estimated in the models.
nfold	the number of folds used for cross validation, default is nford=10 for 10-fold CV.
fun	an internal weighting function for case weights. Choices are "Hampel" (preferred), "Huber" or "Fair".
probp1	the 1-alpha value at which to set the first outlier cutoff for the weighting function.
hampelp2	the 1-alpha values for second cutoff. Only applies to fun="Hampel".
hampelp3	the 1-alpha values for third cutoff. Only applies to fun="Hampel".
probp4	a quantile close to zero for the cutoff for potentially wrong class labels (see Reference). Ignorred if yweights=FALSE.
yweights	logical; if TRUE weights are calculated for observations with potentially wrong class labels.
class	type of classification; choices are "regfit" or "lda". If "regfit" an object of class prm is returned.
prior	vector of length 2 with proir probabilities of the groups; only used if class="lda".
center	type of centering of the data in form of a string that matches an R function, e.g. "mean" or "median".
scale	type of scaling for the data in form of a string that matches an R function, e.g. "sd" or "qn" or alternatively "no" for no scaling.
plot	logical, default is TRUE. If TRUE a plot is generated with a mean weighted misclassification rate for each model (see Details).
numit	the number of maximal iterations for the convergence of the coefficient estimates.
prec	a value for the precision of estimation of the coefficients.

Details

The robust cross validation creterion is a weighted misclassification rate. Class assignment of outliers is unreliable. Therefore, the case weights from the model are used to downweight the influence observations which were detected as outliers on the misclassification rate.

Value

opt.mod	object of class prmda. (see prmda)
pcm	matrix with predicted class membership for each observation and each number of components.

Author(s)

Irene Hoffmann

References

Hoffmann, I., Filzmoser, P., Serneels, S., Varmuza, K., Sparse and robust PLS for binary classification.

See Also

prmda, biplot.prmda, predict.prmda, sprmdaCV

Examples

data(iris)
data <- droplevels(subset(iris,iris$Species!="setosa"))
mod <- prmdaCV(Species~.,data, as=1:2, class="lda", numit=10, prec=0.1)
biplot(mod$opt.mod)

sprm documentation built on May 2, 2019, 9:57 a.m.