Projection pursuit regression for compositional data
Projection pursuit regression for compositional data.
comp.ppr(y, x, nterms = 3, type = "alr", xnew = NULL, yb = NULL )
y |
A matrix with the compositional data. |
x |
A matrix with the continuous predictor variables or a data frame including categorical predictor variables. |
nterms |
The number of terms to include in the final model. |
type |
Either "alr" or "ilr" corresponding to the additive or the isometric log-ratio transformation respectively. |
xnew |
If you have new data use it, otherwise leave it NULL. |
yb |
If you have already transformed the data using a log-ratio transformation put it here. Othewrise leave it NULL. |
This is the standard projection pursuit. See the built-in funciton "ppr" for more details.
A list includign:
runtime |
The runtime of the regression. |
mod |
The produced model as returned by the function "ppr". |
est |
The fitted values of xnew if xnew is not NULL. |
Michail Tsagris
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
Friedman, J. H. and Stuetzle, W. (1981). Projection pursuit regression. Journal of the American Statistical Association, 76, 817-823. doi: 10.2307/2287576.
x <- as.matrix( iris[, 1:4] ) x <- x/ rowSums(x) ina <- iris[, 5] mod <- comp.knn(x, x, ina, a = 1, k = 5) table(ina, mod) mod2 <- alfa.knn(x, x, ina, a = 1, k = 5) table(ina, mod2)
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