Description Usage Arguments Details Value References See Also Examples
View source: R/BTdecayLassoC.R
Model selection via AIC or BIC criteria. For Lasso estimators, the degree of freedom is the number of distinct groups of estimated abilities.
1 2 3 | BTdecayLassoC(dataframe, ability, weight = NULL, criteria = "AIC",
type = "HYBRID", model = NULL, decay.rate = 0, fixed = 1,
thersh = 1e-05, iter = 100, max = 100)
|
dataframe |
Generated using |
ability |
A column vector of teams ability, the last row is the home parameter.
The row number is consistent with the team's index shown in dataframe. It can be generated using |
weight |
Weight for Lasso penalty on different abilities |
criteria |
"AIC" or "BIC" |
type |
"HYBRID" or "LASSO" |
model |
An Lasso path object with class wlasso or swlasso. If NULL, the whole lasso path will be run. |
decay.rate |
The exponential decay rate. Usually ranging from (0, 0.01), A larger decay rate weights more importance to most recent matches and the estimated parameters reflect more on recent behaviour. |
fixed |
A teams index whose ability will be fixed as 0. The worstTeam's index
can be generated using |
thersh |
Threshold for convergence |
iter |
Number of iterations used in L-BFGS-B algorithm. |
max |
Maximum weight for w_ij (weight used for Adaptive Lasso) |
This function is usually run after the run of whole Lasso path. "model" parameter is obtained by whole
Lasso pass's run using BTdecayLasso
. If no model is provided, this function will run Lasso path first (time-consuming).
Users can select the information score added to HYBRID Lasso's likelihood or original Lasso's likelihood. ("HYBRID" is recommended)
summary() function can be applied to view the outputs.
Score |
Lowest AIC or BIC score |
Optimal.degree |
The degree of freedom where lowest AIC or BIC score is achieved |
Optimal.ability |
The ability where lowest AIC or BIC score is achieved |
ability |
Matrix contains all abilities computed in this algorithm |
Optimal.lambda |
The lambda where lowest score is attained |
Optimal.penalty |
The penalty (1- s/\max(s)) where lowest score is attained |
type |
Type of model selection method |
decay.rate |
Decay rate of this model |
Masarotto, G. and Varin, C.(2012) The Ranking Lasso and its Application to Sport Tournaments. *The Annals of Applied Statistics* **6** 1949–1970.
Zou, H. (2006) The adaptive lasso and its oracle properties. *J.Amer.Statist.Assoc* **101** 1418–1429.
BTdataframe
for dataframe initialization,
BTdecayLasso
for obtaining a whole Lasso path
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ##Initializing Dataframe
x <- BTdataframe(NFL2010)
##The following code runs the main results
##Model selection through AIC
z <- BTdecayLassoC(x$dataframe, x$ability, weight = NULL, fixed = x$worstTeam,
criteria = "AIC", type = "LASSO")
summary(z)
##If the whole Lasso path is run, we use it's result for model selection (recommended)
##Note that the decay.rate used in model selection should be consistent with
##the one which is used in whole Lasso path's run (keep the same model)
y1 <- BTdecayLasso(x$dataframe, x$ability, lambda = 0.1,
decay.rate = 0.005, fixed = x$worstTeam)
z1 <- BTdecayLassoC(x$dataframe, x$ability, weight = NULL, model = z1,
decay.rate = 0.005,
fixed = x$worstTeam, criteria = "BIC", type = "HYBRID")
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