Description Usage Arguments Value Author(s) References See Also Examples

View source: R/best.tree.BIC.AIC.R

this function is set to prune back the maximal tree by using the `BIC`

or the `AIC`

criterion.

1 2 | ```
best.tree.BIC.AIC(xtree, xdata, Y.name, X.names,
family = "binomial", verbose = TRUE)
``` |

`xtree` |
a tree to prune |

`xdata` |
the dataset used to build the tree |

`Y.name` |
the name of the dependent variable |

`X.names` |
the names of independent confounding variables to consider in the linear part of the |

`family` |
the |

`verbose` |
Logical; TRUE for printing progress during the computation (helpful for debugging) |

a list of four elements:

`best_index ` |
The size of the selected trees by |

`tree ` |
The selected trees by |

`fit_glm` |
The fitted pltr models selected with |

`Timediff` |
The execution time of the selection procedure |

Cyprien Mbogning and Wilson Toussile

Mbogning, C., Perdry, H., Toussile, W., Broet, P.: A novel tree-based procedure for deciphering the genomic spectrum of clinical disease entities. Journal of Clinical Bioinformatics 4:6, (2014)

Akaike, H.: A new look at the statistical model identification. `IEEE Trans. Automat. Control AC-19`

, 716-723 (1974)

Schwarz, G.: Estimating the dimension of a model. `The Annals of Statistics`

6, 461-464 (1978)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | ```
data(burn)
args.rpart <- list(minbucket = 10, maxdepth = 4, cp = 0, maxcompete = 0,
maxsurrogate = 0)
family <- "binomial"
X.names = "Z2"
Y.name = "D2"
G.names = c('Z1','Z3','Z4','Z5','Z6','Z7','Z8','Z9','Z10','Z11')
pltr.burn <- pltr.glm(burn, Y.name, X.names, G.names, args.rpart = args.rpart,
family = family, iterMax = 4, iterMin = 3, verbose = FALSE)
## Prunned back the maximal tree using either the BIC or the AIC criterion
pltr.burn_prun <- best.tree.BIC.AIC(xtree = pltr.burn$tree, burn, Y.name,
X.names, family = family)
## plot the BIC selected tree
plot(pltr.burn_prun$tree$BIC, main = 'BIC selected tree')
text(pltr.burn_prun$tree$BIC, xpd = TRUE, cex = .6, col = 'blue')
## Not run:
##load the data set
data(data_pltr)
## Set the parameters
args.rpart <- list(minbucket = 40, maxdepth = 10, cp = 0)
family <- "binomial"
Y.name <- "Y"
X.names <- "G1"
G.names <- paste("G", 2:15, sep="")
## build a maximal tree
fit_pltr <- pltr.glm(data_pltr, Y.name, X.names, G.names, args.rpart = args.rpart,
family = family,iterMax = 5, iterMin = 3)
##prunned back the maximal tree by BIC or AIC criterion
tree_select <- best.tree.BIC.AIC(xtree = fit_pltr$tree,data_pltr,Y.name,
X.names, family = family)
plot(tree_select$tree$BIC, main = 'BIC TREE')
text(tree_select$tree$BIC, minlength = 0L, xpd = TRUE, cex = .6)
## End(Not run)
``` |

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