rrlda
From rrlda v1.1
by Moritz Gschwandtner
Robust Regularized Linear Discriminant Analysis
Performs Robust Regularized Linear Discriminant Analysis.
 Keywords
 file
Usage
rrlda(x, grouping, prior=NULL, lambda=0.5, hp=0.75, nssamples=30, maxit=50, penalty="L2")
Arguments
 x
 Matrix or data.frame of observations.
 grouping
 Grouping variable. A vector of numeric values >= 1 is recommended. Length has to correspond to nrow(x).
 prior
 Vector of prior probabilities for each group. If not supplied the priors are computed from the data.
 lambda
 Penalty parameter which controls the sparseness of the resulting inverse scatter matrix. Default is 0.5
 hp
 Robustness parameter which specifies the amount of observations to be included in the computations. Default is 0.75
 nssamples
 Number of start samples to be user for iterated estimations.
 maxit
 Maximum number of iterations of the algorithm. Default is 10.
 penalty
 Type of penalty to be applied. Possible values are "L1" and "L2".
Details
Performs Robust Regularized Discriminant Analysis using a sparse estimation of the inverse covariance matrix. The sparseness is controlled by a penalty parameter lambda. Possible outliers are dealt with by a robustness parameter alpha which specifies the amount of observations for which the likelihood function is maximized.
Value

An object of class "rrlda" is returned which can be used for class prediction (see predict()).
prior=prior, counts=counts, means=means, cov=covm, covi=covi, lev=lev, n=n, h=h, bic=bic, loglik=loglik, nonnuls=nonnuls, subs=est$subset
 prior
 Vector of prior probabilities.
 counts
 Number of obervations for each group.
 means
 Estimated mean vectors for each group.
 covi
 Estimated (common) inverse covariance matrix.
 lev
 Levels. Corresponds to the groups.
 n
 Number of observations.
 h
 Number of observations included in the computations (see robustness parameter alpha).
 bic
 Adapted bic value. Can be used for optimal selection of lambda
 loglik
 The maximized (log)likelihood value.
 df
 Degrees of freedom of the estimated inverse covariance matrix.
 subs
 An index vector specifying the data subset used (see robustness parameter alpha).
Examples
data(iris)
x < iris[,1:4]
rr < rrlda(x, grouping=as.numeric(iris[,5]), lambda=0.2, hp=0.75) ## perform rrlda
pred < predict(rr, x) ## predict
table(as.numeric(pred$class), as.numeric(iris[,5])) ## show errors
Community examples
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