BTLboost: Boosting ordinal Bradley-Terry-Luce models

In ordBTL: Modelling comparison data with ordinal response

Description

Boosting procedure for ordinal BTL models

Usage

BTLboost(formula, data, objects=NULL, groupVars=NULL,
                selection=c("DEVIANCE","AIC","BIC"),
                mstop=500, nu=1, maxit=1, verbose=TRUE, ...)

Arguments

formula	a formula describing the full model.
data	a data frame containing the design matrix for the model (See also design to generate such an design matrix).
objects	(optional) a character vector specifying the objects that should always be part of the model.
groupVars	(optional) a character vector specifying the subject-specific covariates, whose subject-object interactions are considered simultaneously in each boosting step. By default (groupVars=NULL), all subject-object interactions are cosidered separately in each boosting step.
selection	a character specifying the criterion that is used in each boosting step to determine the best fitting covariate(s).
mstop	an integer giving the number of boosting iterations.
nu	a double between 0 and 1 defining the step size or shrinkage parameter.
maxit	an integer representing the maximum number of Fisher-scoring iterations (see also vglm.control).
verbose	logical indicating if output should be produced for each boosting iteration.
...	further arguments passed to ordBTL.

Value

A List of

• BEST contains estimated parameters of the last boosting iteration

• AIC a vector of AIC values for each boosting iteration

• BIC a vector of BIC values for each boosting iteration

• DEVIANCE a vector that reflects the deviance of each boosting iteration

• PATH a dataframe containing the coefficient build-up at the end of each boosting iteration

• UPDATED a vector of strings containing the selected components in each boosting iteration

Author(s)

Giuseppe Casalicchio

Examples

# Get the CEMS data and generate design matrix
example(wide2long, package="ordBTL", echo=FALSE)

des2 <- design(CEMSlong[-which(is.na(CEMSlong$Y)),], 
               var1="object1", var2="object2", 
               use.vars="ALL", reference="Stockholm")

# Formula for full model considering all subject-object interactions
form2 <- Y ~ 
  (GAMMA.London+GAMMA.Paris+GAMMA.Milano+GAMMA.StGallen+GAMMA.Barcelona)+
  (GAMMA.London+GAMMA.Paris+GAMMA.Milano+GAMMA.StGallen+GAMMA.Barcelona):
  (WOR+SEX+DEG+STUD+ENG+FRA+SPA+ITA)

## Not run: 
# Exemplatory boosting call with mstop=5
#BoostDev <- BTLboost(form2, data=des2, groupVars=c("WOR","DEG","SEX","STUD"), 
#                     selection="DEVIANCE", mstop=5)
## End(Not run)

Example output

Loading required package: caret
Loading required package: lattice
Loading required package: ggplot2
Loading required package: VGAM
Loading required package: stats4
Loading required package: splines

Attaching package: 'VGAM'

The following object is masked from 'package:caret':

    predictors

Loading required package: wikibooks
Loading required package: gtools

Attaching package: 'gtools'

The following object is masked from 'package:VGAM':

    logit


Attaching package: 'ordBTL'

The following object is masked from 'package:BiocGenerics':

    design

ordBTL documentation built on May 2, 2019, 7:59 a.m.