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

The `pltr.glm`

function is designed to fit an hybrid glm model with an additive tree part on a glm scale.

1 2 3 |

`data` |
a data frame containing the variables in the model |

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

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

`G.names` |
the names of independent variables to consider in the tree part of the hybrid |

`family` |
the |

`args.rpart` |
a list of options that control details of the rpart algorithm. |

`epsi` |
a treshold value to check the convergence of the algorithm |

`iterMax` |
the maximal number of iteration to consider |

`iterMin` |
the minimum number of iteration to consider |

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

The `pltr.glm`

function use an itterative procedure to fit the linear part of the `glm`

and the tree part. The tree obtained at the convergence of the procedure is a maximal tree which overfits the data. It's then mandatory to prunned back this tree by using one of the proposed criteria (`BIC`

, `AIC`

and `CV`

).

A list with four elements:

`fit ` |
the glm fitted on the confounding factors at the end of the iterative algorithm |

`tree ` |
the maximal tree obtained at the end of the algorithm |

`nber_iter` |
the number of iterations used by the algorithm |

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

The tree obtained at the end of these itterative procedure usually overfits the data. It's therefore mendatory to use either `best.tree.BIC.AIC`

or `best.tree.CV`

to prunne back the tree.

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)

Terry M. Therneau, Elizabeth J. Atkinson (2013) An Introduction to Recursive Partitioning Using the `RPART`

Routines. Mayo Foundation.

Chen, J., Yu, K., Hsing, A., Therneau, T.M.: A partially linear tree-based regression model for assessing complex joint gene-gene and gene-environment effects. Genetic Epidemiology 31, 238-251 (2007)

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 | ```
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)
## 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)
plot(fit_pltr$tree, main = 'MAXIMAL TREE')
text(fit_pltr$tree, minlength = 0L, xpd = TRUE, cex = .6)
## End(Not run)
``` |

GPLTR documentation built on May 29, 2017, 8:01 p.m.

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