k-fold cross validation for the selection of the number of components for partial robust M regression.
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formula |
an object of class formula. |
data |
a data frame or list which contains the variables given in formula. |
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 |
fun |
an internal weighting function for case weights. Choices are |
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 |
hampelp3 |
the 1-alpha values for third cutoff. Only applies to |
center |
type of centering of the data in form of a string that matches an R function, e.g. |
scale |
type of scaling for the data in form of a string that matches an R function, e.g. |
usesvd |
logical, default is |
plot |
logical, default is |
numit |
the number of maximal iterations for the convergence of the coefficient estimates. |
prec |
a value for the precision of estimation of the coefficients. |
alpha |
value used for alpha trimmed mean squared error, which is the cross validation criterion (see Details). |
The alpha
- trimmed mean squared error of the predicted response over all observations is used as robust decision criterion to choose the optimal model. For plot=TRUE
a graphic visualizes the alpha
- trimmed mean squared error for each model.
opt.mod |
object of class prm. (see |
spe |
matrix with squared prediction error for each observation and each number of components. |
Irene Hoffmann
Hoffmann, I., Serneels, S., Filzmoser, P., Croux, C. (2015). Sparse partial robust M regression. Chemometrics and Intelligent Laboratory Systems, 149, 50-59.
Serneels, S., Croux, C., Filzmoser, P., Van Espen, P.J. (2005). Partial Robust M-Regression. Chemometrics and Intelligent Laboratory Systems, 79, 55-64.
prms
, plot.prm
, predict.prm
, sprmsCV
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Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
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