PPO {ODRF} | R Documentation |
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.
|
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
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"))