B_T_Weighted_Lasso_R_wij_f: (Important) Bradley-Terry Weighted Likelihood Estimation on...

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

Description

(Important) Bradley-Terry Weighted Likelihood Estimation on specifed Lasso Peanlty Level

Usage

B_T_Weighted_Lasso_R_wij_f(df, ability, i0, u, penalty, uk)

Arguments

ability	A column vector of teams ability, the last row is the home parameter whose rowname must be "at.home".
i0	A teams index whose ability will be fixed as 0 (usually the team loss most).
u	The exponential decay rate.
penalty	Penalty level = s/max(s)
uk	Exponetial rate between lambda and true penalty level (usually setting to be 0.642)
dataframe	Dataframe with 5 columns. First column is the index of the home teams (use numbers to denote teams). Second column is the index of the away teams. Third column is the number of wins of home teams (usually to be 0/1). Fourth column is the number of wins of away teams (usually to be 0/1). Fifth column is the scalar of time when the match is played until now (Time lag).
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.

Value

The estimated abilities

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