NNS.Feature.prob: NNS Feature Probability

Description Usage Arguments Value Author(s) Examples

Description

Classifies data based on feature probabilities.

Usage

1
NNS.Feature.prob(x, y, threshold = 0, point.est = NULL)

Arguments

x

Complete cleaned dataset in matrix form.

y

Column of data to be classified.

threshold

Sets the correlation threshold for independent variables. Defaults to 0.

point.est

IV data point(s) to be classified, in matrix form.

Value

Returns variables, "MSE" mean squared error, "Fitted" for only the fitted values of the DV, and "Point.est" for predicted values.

Author(s)

Fred Viole, OVVO Financial Systems

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
## Using 'iris' dataset where predictive attributes are columns 1:4, and the class is column 5.
NNS.Feature.prob(iris,5)

## To call mean squared error
NNS.Feature.prob(iris,5)$MSE

## To call fitted values
NNS.Feature.prob(iris,5)$Fitted

## To generate a single predicted value
NNS.Feature.prob(iris,5, point.est=cbind(5.1,3.5,1.4,0.2))$Point.est

## To generate multiple predicted values
NNS.Feature.prob(iris,5, point.est=(iris[1:10,1:4]))$Point.est

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.