# Getting Started with NNS: Clustering and Regression" In NNS: Nonlinear Nonparametric Statistics

### Classification

For a classification problem, we simply set NNS.reg(x, y, type = "CLASS", ...).

NNS.reg(iris[ , 1 : 4], iris[ , 5], type = "CLASS", point.est = iris[141:150, 1 : 4], location = "topleft")$Point.est  ### NNS Dimension Reduction Regression NNS.reg also provides a dimension reduction regression by including a parameter NNS.reg(x, y, dim.red.method = "cor", ...). Reducing all regressors to a single dimension using the returned equation $equation.

NNS.reg(iris[ , 1 : 4], iris[ , 5], dim.red.method = "cor", location = "topleft")$equation  Thus, our model for this regression would be: $$Species = \frac{0.7825612Sepal.Length -0.4266576Sepal.Width + 0.9490347Petal.Length + 0.9565473Petal.Width}{4}$$ #### Threshold NNS.reg(x, y, dim.red.method = "cor", threshold = ...) offers a method of reducing regressors further by controlling the absolute value of required correlation. NNS.reg(iris[ , 1 : 4], iris[ , 5], dim.red.method = "cor", threshold = .75, location = "topleft")$equation


Thus, our model for this further reduced dimension regression would be: $$Species = \frac{0.7825612Sepal.Length -0Sepal.Width + 0.9490347Petal.Length + 0.9565473Petal.Width}{3}$$

and the point.est = (...) operates in the same manner as the full regression above, again called with $Point.est. NNS.reg(iris[ , 1 : 4], iris[ , 5], dim.red.method = "cor", threshold = .75, point.est = iris[1 : 10, 1 : 4], location = "topleft")$Point.est


# References

If the user is so motivated, detailed arguments further examples are provided within the following:

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NNS documentation built on May 15, 2019, 1:05 a.m.