Package clogitboost. R topics documented: December 21, 2015
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1 Package December 21, 2015 Type Package Title Boosting Conditional Logit Model Version 1.1 Date Author Haolun Shi and Guosheng Yin Maintainer A set of functions to fit a boosting conditional logit model. License GPL (>= 2) Imports Rcpp (>= ) LinkingTo Rcpp LazyData True NeedsCompilation yes Repository CRAN Date/Publication :54:58 R topics documented: marginal plot predict summary travel Index 8 1
2 2 Boosting conditional logit model Fit a boosting conditional logit model using componentwise smoothing spline. (y, x, strata, iter, rho) y x strata iter rho vector of binary outcomes. matrix or data frame with each column being a covariate. vector of group membership, i.e., items in the same group have the same value. number of iterations. learning rate parameter in the boosting algorithm. Value The function returns the following list of values: call func index theta loglike infscore original function call. list of fitted spline functions. list of indices indicating which covariate is used as input for the smoothing spline. list of fitted coefficients in the conditional logit models. sequence of fitted values of log-likelihood. relative influence score for each covariate. rho learning rate parameter, which typically takes a value of 0.05 or 0.1. xmax xmin maximal element of each covariate. minimal element of each covariate. plot. predict.
3 marginal 3 marginal Marginal utility for objects marginal function for the objects, which produces the marginal utility values of a covariate. marginal(x, grid, d) x d grid output object from the function. integer indicating which covariate is used. grid of values for predicting the marginal utilities. Value The method marginal returns a vector of predicted marginal utilities based on the grid input. marginal(fit, grid = seq(0, 10, by = 1), d = 1)
4 4 plot. plot. Plotting after fitting a boosting conditional logit model plot methods for the objects, which produce marginal plots of the covariate effects. ## S3 method for class plot(x, d, grid = NULL,...) x d grid output object from the function. integer indicating which covariate is used. grid of values for plotting. If it is not specified, the minimal and maximal elements of the covariate are used as the two endpoints of the grid.... other options for plotting. plot(fit, d = 1, xlab = "x", ylab = "f(x)", main = "TTIME", type = "l")
5 predict. 5 predict. Predicting after fitting a boosting conditional logit model predict methods for the objects, which produce marginal predictions of the covariate effects. ## S3 method for class predict(object, x, strata,...) object x Value strata output object from the function. new matrix or data frame with each column being a covariate. new vector of group memberships, i.e., items in the same group have the same value.... not currently used. The method predict returns the following list of values: prob probability of the outcome equal to 1. utility prediction predicted utility. 0-1 prediction of the outcome variable. predict(fit, x = travel[-train, 3:6], strata = travel$group[-train])
6 6 summary. summary. Summary after fitting a boosting conditional logit model summary methods for the objects. ## S3 method for class summary(object,...) object output object from the function.... not currently used. Value The function () returns the following list of values: call infscore loglike original function call. relative influence score for each covariate. sequence of the fitted values of log-likelihood. summary(fit)
7 travel 7 travel Australian travel mode choice data Format Source The dataset is a survey result of 210 individuals choices of travel mode between Sydney, Melbourne and New South Wales. There are four alternative choices, along with four choice-specific covaraites for each choice. data("travel") A data frame with 840 observations on the following 6 variables. Group index of the group membership. MODE binary outcome of whether the item is chosen. TTME terminal time. INVC in-vehicle cost. INVT amount of time spent traveling. GC genearlized cost of travel. Greene W (2008). Econometric Analysis, 6th edition. Prentice Hall.
8 Index Topic datasets travel, 7, 2, 3 6 marginal, 3 plot., 2, 4 predict., 2, 5 summary., 6 travel, 7 8
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