Description Usage Arguments Value Examples
View source: R/RandomUtilityModels.R
Performs parameter estimation for the Plackett-Luce model using an Minorize Maximize algorithm
1 | Estimation.PL.MLE(Data, iter = 10)
|
Data |
data in either partial or full rankings (Partial rank case works for settings like car racing) |
iter |
number of MM iterations to run |
list of estimated means (Gamma) and the log likelihoods
1 2 |
[1] "Finished 1/10"
[1] "Finished 2/10"
[1] "Finished 3/10"
[1] "Finished 4/10"
[1] "Finished 5/10"
[1] "Finished 6/10"
[1] "Finished 7/10"
[1] "Finished 8/10"
[1] "Finished 9/10"
[1] "Finished 10/10"
$m
[1] 5
$order
[1] 1 2 3 4 5
$Mean
[1] 0.09125506 0.14783081 0.20515827 0.23404831 0.32170757
$SD
[1] 0.09125506 0.14783081 0.20515827 0.23404831 0.32170757
$LL
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,] -22.6349 -22.55422 -22.54277 -22.5405 -22.53989 -22.53968 -22.53958
[,8] [,9] [,10]
[1,] -22.53952 -22.53948 -22.53945
$Time
user system elapsed
0.010 0.000 0.012
$AverageLogLikelihood
[,1]
[1,] -4.50789
$Parameters
$Parameters[[1]]
$Parameters[[1]]$Mean
[1] 0.09125506
$Parameters[[2]]
$Parameters[[2]]$Mean
[1] 0.1478308
$Parameters[[3]]
$Parameters[[3]]$Mean
[1] 0.2051583
$Parameters[[4]]
$Parameters[[4]]$Mean
[1] 0.2340483
$Parameters[[5]]
$Parameters[[5]]$Mean
[1] 0.3217076
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