trees_pred | R Documentation |

Obtain predicted class for new data from baggtree function or PPforest

trees_pred(object, xnew, parallel = FALSE, cores = 2, rule = 1)

`object` |
Projection pursuit classification forest structure from PPforest or baggtree |

`xnew` |
data frame with explicative variables used to get new predicted values. |

`parallel` |
logical condition, if it is TRUE then parallelize the function |

`cores` |
number of cores used in the parallelization |

`rule` |
split rule 1: mean of two group means 2: weighted mean of two group means - weight with group size 3: weighted mean of two group means - weight with group sd 4: weighted mean of two group means - weight with group se 5: mean of two group medians 6: weighted mean of two group medians - weight with group size 7: weighted mean of two group median - weight with group IQR 8: weighted mean of two group median - weight with group IQR and size |

predicted values from PPforest or baggtree

## Not run: crab.trees <- baggtree(data = crab, class = 'Type', m = 200, PPmethod = 'LDA', lambda = .1, size.p = 0.4 ) pr <- trees_pred( crab.trees,xnew = crab[, -1], parallel= FALSE, cores = 2) pprf.crab <- PPforest(data = crab, class = 'Type', std = FALSE, size.tr = 2/3, m = 100, size.p = .4, PPmethod = 'LDA', parallel = TRUE ) trees_pred(pprf.crab, xnew = pprf.crab$test ,parallel = TRUE) ## End(Not run)

PPforest documentation built on Sept. 10, 2022, 1:05 a.m.

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