Description Arguments Details See Also
The following parameters can be used in the ...
argument in
function getap
, also within function gdmm
, to
override the values in the analysis procedure file and so to modify the
calculation of discriminant analysis models  see examples.
getap(...)
gdmm(dataset, ap=getap(...))
do.da 
Logical. If used in 
da.type 
Character vector. The type of discriminant analysis (DA) to
perform; possible values (one or more) are:

da.classOn 
Character vector. One or more class variables to define the grouping used for classification. 
da.testCV 
Logical, if the errors of the testdata should be crossvalidated. If set to true, CV and testing is repeated in alternating datasets. See below. 
da.percTest 
Numeric length one. The percentage of the dataset that should be set aside for testing the models; these data are never seen during training and crossvalidation. 
da.cvBootCutoff 
The minimum number of observations (W) that should be
in the smallest subgroup (as defined by the classification grouping variable)
*AFTER* the split into 
da.cvBootFactor 
The factor used to multiply the number of observations
within the smallest subgroup defined by the classification grouping variable
with, resulting in the number of iterations of a possible bootstrap
crossvalidation of the trainign data – see 
da.valid 
The number of segments the training data should be divided into in case of a "traditional" crossvalidation of the training data; see above. 
da.pcaRed 
Logical, if variable reduction via PCA should be applied; if
TRUE, the subsequent classifications are performed on the PCA scores, see

da.pcaNComp 
Character or integer vector. Provide the character "max" to use the maximum number of components (i.e. the number of observations minus 1), or an integer vector specifying the components resp. their scores to be used for DA. 
For a list of all parameters that can be used in the ...
argument in getap
and in the plot
functions
please see anproc_file
.
gdmm
, siWlg
for reducing the number of
wavelengths in a dataset
Other Calc. arguments: calc_NNET_args
,
calc_SVM_args
, calc_aqg_args
,
calc_pca_args
, calc_pls_args
,
calc_randomForest_args
,
calc_sim_args
, split_dataset
Other Classification functions: calc_NNET_args
,
calc_SVM_args
,
calc_randomForest_args
,
plot_classifX_indepPred
Other DA documentation: plot_da,aquap_cubemethod
,
plot_discrimAnalysis_args
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