Description Usage Arguments Value Author(s) See Also Examples

Constructs an ordinal logic regression model for identification of binary predictors and predictor interactions for an ordinal response

1 | ```
Ord.logreg(resp, Xs, nleaf, use.cv = TRUE, kfold = 5, anneal.params)
``` |

`resp` |
vector of ordinal response values. Note the reference cateogry should be assigned a value of 0. |

`Xs` |
matrix or data frame of zeros and ones for all predictor variables. |

`nleaf` |
numeric value or vector. If |

`use.cv` |
logical. If |

`kfold` |
If |

`anneal.params` |
a list containing the parameters for simulated annealing. See the help file for the function |

An object of class `"Ord.logreg"`

which is a list including values

`mod.dat` |
For data with K response categories, a list of the K-1 predictor datasets used to fit each logic regression tree in the model. |

`model` |
A list of K-1 logic regression trees associated with the largest K-1 response categories. |

`Ys` |
A list of the K-1 binary response vectors (based on the original ordinal response) generated to fit each of the K-1 logic regression trees. |

`mod.preds` |
A vector containing the names of the predictors used in each of teh K-1 logic regression trees. |

`pos` |
A vector of indicators of whether or not a predictor in an individial tree represents a predictor or its compliment. A value of 1 indicates that the predictor occurs as the compliment. |

`leaves ` |
A vector of the maximum number of leaves used for each of the K-1 logic regression trees. |

`CV ` |
A statement describing if cross-validation was used. |

Bethany Wolf wolfb@musc.edu

`print.Ord.logreg`

, `predict.Ord.logreg`

, `plot.Ord.logreg`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
data(OLRdata)
#typically >25000 would be used for the annealing algorithm.
#Number of iterations here is set to 2500 for faster run time
#Fitting model without cross-validation
cont<-logreg.anneal.control(start=1, end=-2, iter=2500)
Xs<-OLRdata[,c(1:50)]
Ys<-OLRdata$Y
OLRmod1<-Ord.logreg(resp=Ys, Xs=Xs, use.cv=FALSE, anneal.params=cont)
print(OLRmod1)
#Fitting a model without cross-validation but setting the maximum number of leaves per tree
OLRmod2<-Ord.logreg(resp=Ys, Xs=Xs, nleaf=c(3,4,3), use.cv=FALSE, anneal.params=cont)
print(OLRmod2)
#Fitting model with cross-validation
OLRmod3<-Ord.logreg(resp=Ys, Xs=Xs, use.cv=TRUE, anneal.params=cont)
print(OLRmod3)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.