B_T_Lasso: Bradley-Terry Likelihood Function with Lasso Peanlty's...

In heilokchow/MWLE-Lasso: Bradley-Terry Model with Exponential Decayed Likelihood function and Lasso Penalty

Description

Bradley-Terry Likelihood Function with Lasso Peanlty's optimization

Usage

B_T_Lasso(dataframe, lambda, wij)

Arguments

dataframe	Dataframe with 4 columns. First column is the home teams Second column is the away teams. Third column is the number of wins of home teams. Fourth column is the number of wins of away teams.
lambda	Lasso penalty of no statistical mean, a larger choice of lambda means higher penalty. Usually the best penalty is chosen from Cross-Validation, where in-model's prediction power is maximized.
wij	The weights added to the Lasso Penalty. Can be manually setted or determined using function B_T_Wij

heilokchow/MWLE-Lasso documentation built on May 23, 2019, 4:03 a.m.