Optim.DA: Discover the best Discriminant Analysis for your data
In OptimClassifier: Create the Best Train for Classification Models

Description Usage Arguments Details Value Examples

This function search the best Discriminant Analysis (DA) between LDA and QDA.

1 2	Optim.DA(formula, data, p, criteria = c("rmse", "success_rate", "ti_error", "tii_error"), includedata = FALSE, seed = NULL, ...)

`formula`	A formula of the form `y ~ x1 + x2 + ...`
`data`	Data frame from which variables specified in `formula` are preferentially to be taken.
`p`	A percentage of training elements
`criteria`	Select criterion to use.
`includedata`	logicals. If TRUE the training and testing datasets are returned.
`seed`	a single value, interpreted as an integer, or `NULL`. The default value is `NULL`, but for future checks of the model or models generated it is advisable to set a random seed to be able to reproduce it.
`...`	arguments passed to `lda` and `qda`

LDA and QDA are distribution-based classifiers with the assumption that data follows a multivariate normal distribution. LDA differs from QDA in the assumption about the class variability. LDA assumes that all classes share the same within-class covariance matrix whereas QDA allows for distinct within-class covariance matrices.

An object of class Optim. See Optim.object

if(interactive()){
## Load a Dataset
data(AustralianCredit)
## Generate a Model
modelFit <- Optim.DA(Y~., AustralianCredit, p = 0.7, seed=2018)
modelFit
}