Ord.logreg: Ordinal Logic Regression In OrdLogReg: Ordinal Logic Regression

Description

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

Usage

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

Arguments

 `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=FALSE`, nleaf can be either a single numeric value or vector (length is the number of categories -1). A single value means that the maximum possible number of leaves in all trees will be `"nleaf"`. The default value is 8 when `use.cv=FALSE`. If `use.cv=TRUE`, nleaf is a vector of the minimum and maximum values to be cosidered in the trees. The default value is `c(1,8)`. `use.cv` logical. If `use.cv=TRUE`, cross-validation will be used to determine the best choice of model size for each tree inteh ordinal logic regression model. `kfold` If `use.cv=TRUE`, `kfold` is the number of times the data are to be split during cross-validation to determine appropriate tree size. Note, if `use.cv=FALSE`, this arguement will be ignored. `anneal.params` a list containing the parameters for simulated annealing. See the help file for the function `logreg.anneal.control` in the `LogicReg` package. If missing, default annealing parameters are set at `start`=1, `end`=-2, and `iter`=50000.

Value

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.

Author(s)

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) ```