Description Usage Arguments Value Author(s) References See Also Examples
View source: R/svmmajcrossval.R
This function performs a gridsearch of kfold crossvalidations using SVMMaj and returns the combination of input values which has the best forecasting performance.
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X 
A data frame (or object coercible by

y 
A factor (or object coercible by 
search.grid 
A list with for each factor the range of values to search for. 
... 
Other arguments to be passed through 
convergence 
Specifies the convergence criterion for 
weights.obs 
Weights for the classes. 
check.positive 
Specifies whether a check should be performed for
positive 
mc.cores 
the number of cores to be used (for parallel computing) 
options 
additional settings used in the 
verbose 

ngroup 
The number of groups to be divided into. 
groups 
A predetermined group division for performing the cross validation. 
return.model 

loss.opt 
The minimum (weighted) missclassification rate found in outofsample training along the search grid. 
param.opt 
The level of the factors which gives the minimum loss term value. 
loss.grp 
A list of missclassification rates per holdout sample 
groups 
A vector defining the crossvalidation groups which has been used. 
qhat 
The estimated outofsample predicted values in the crossvalidation. 
qhat.in 
The trained predicted values 
param.grid 
The matrix of all gridpoints which has been performed during the crossvalidation, with its corresponding weighted outofsample missclassification rate. 
model 
The 
Hok San Yip, Patrick J.F. Groenen, Georgi Nalbantov
P.J.F. Groenen, G. Nalbantov and J.C. Bioch (2008) SVMMaj: a majorization approach to linear support vector machines with different hinge errors.
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