PPO {ODRF}

Projection Pursuit Optimization

Description

Find the optimal projection using various projectin pursuit models.

Usage

PPO(X, y, model = "PPR", split = "gini", weights = NULL, ...)

Arguments

X

An n by d numeric matrix (preferable) or data frame.

y

A response vector of length n.

model

Model for projection pursuit.

• "PPR"(default): projection projection regression from ppr. When y is a category label, it is expanded to K binary features.

• "Log": logistic based on nnet.

• "Rand": The random projection generated from \{-1, 1\}. The following models can only be used for classification, i.e. the split must be ”entropy” or 'gini'.

• "LDA", "PDA", "Lr", "GINI", and "ENTROPY" from library PPtreeViz.

• The following models based on Pursuit.

  • "holes": Holes index

  • "cm": Central Mass index

  • "holes": Holes index

  • "friedmantukey": Friedman Tukey index

  • "legendre": Legendre index

  • "laguerrefourier": Laguerre Fourier index

  • "hermite": Hermite index

  • "naturalhermite": Natural Hermite index

  • "kurtosismax": Maximum kurtosis index

  • "kurtosismin": Minimum kurtosis index

  • "moment": Moment index

  • "mf": MF index

  • "chi": Chi-square index

split

The criterion used for splitting the variable. 'gini': gini impurity index (classification, default), 'entropy': information gain (classification) or 'mse': mean square error (regression).

weights

Vector of non-negative observational weights; fractional weights are allowed (default NULL).

...

optional parameters to be passed to the low level function.

Value

Optimal projection direction.

References

Friedman, J. H., & Stuetzle, W. (1981). Projection pursuit regression. Journal of the American statistical Association, 76(376), 817-823.

Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.

Lee, YD, Cook, D., Park JW, and Lee, EK(2013) PPtree: Projection Pursuit Classification Tree, Electronic Journal of Statistics, 7:1369-1386.

Cook, D., Buja, A., Lee, E. K., & Wickham, H. (2008). Grand tours, projection pursuit guided tours, and manual controls. In Handbook of data visualization (pp. 295-314). Springer, Berlin, Heidelberg.

See Also

RotMatPPO

Examples

# classification
data(seeds)
(PP <- PPO(seeds[, 1:7], seeds[, 8], model = "Log", split = "entropy"))
(PP <- PPO(seeds[, 1:7], seeds[, 8], model = "PPR", split = "entropy"))
(PP <- PPO(seeds[, 1:7], seeds[, 8], model = "LDA", split = "entropy"))

# regression
data(body_fat)
(PP <- PPO(body_fat[, 2:15], body_fat[, 1], model = "Log", split = "mse"))
(PP <- PPO(body_fat[, 2:15], body_fat[, 1], model = "Rand", split = "mse"))
(PP <- PPO(body_fat[, 2:15], body_fat[, 1], model = "PPR", split = "mse"))