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.