計量方法Logit Model R軟體Package的選擇 - 經濟
By Adele
at 2015-05-25T16:24
at 2015-05-25T16:24
Table of Contents
已經PO在R語言板詢問
但考慮到經濟板的神人或許做過類似的研究
希望可以詢問到有相關經驗的神人
以下是問題:
大家好,目前小弟要複製某篇論文的結果,(只有改變樣本期間)
所使用的方法是用Maximum Likelihood Estimates 來估計Logit Model
但是上CRAN 找 Package的時候發現有很多可能的Packages
以下是我找到的:
mlogit: multinomial logit model
mlogitBMA: Bayesian Model Averaging for Multinomial Logit Models
mnlogit: Multinomial Logit Model
pln: Polytomous logit-normit (graded logistic) model estimation
gmnl: Multinomial Logit Models with Random Parameters
logitnorm: Functions for the logitnormal distribution
目前我是覺得mnlogit: Multinomial Logit Model
比較有可能是我需要的工具,以下是他的說明:
(Time and memory efficient estimation of multinomial logit models using
maximum likelihood method. Numerical optimization performed by Newton-Raphson
method using an optimized, parallel C++ library to achieve fast computation
to achieve fast computation of Hessian matrices. Motivated by large scale
multiclass classification problems in econometrics and machine learning.)
不知道有沒有人使用過類似的Package?
他會是我所需要的工具嗎?
(目前打算載來先用用看)
--
但考慮到經濟板的神人或許做過類似的研究
希望可以詢問到有相關經驗的神人
以下是問題:
大家好,目前小弟要複製某篇論文的結果,(只有改變樣本期間)
所使用的方法是用Maximum Likelihood Estimates 來估計Logit Model
但是上CRAN 找 Package的時候發現有很多可能的Packages
以下是我找到的:
mlogit: multinomial logit model
mlogitBMA: Bayesian Model Averaging for Multinomial Logit Models
mnlogit: Multinomial Logit Model
pln: Polytomous logit-normit (graded logistic) model estimation
gmnl: Multinomial Logit Models with Random Parameters
logitnorm: Functions for the logitnormal distribution
目前我是覺得mnlogit: Multinomial Logit Model
比較有可能是我需要的工具,以下是他的說明:
(Time and memory efficient estimation of multinomial logit models using
maximum likelihood method. Numerical optimization performed by Newton-Raphson
method using an optimized, parallel C++ library to achieve fast computation
to achieve fast computation of Hessian matrices. Motivated by large scale
multiclass classification problems in econometrics and machine learning.)
不知道有沒有人使用過類似的Package?
他會是我所需要的工具嗎?
(目前打算載來先用用看)
--
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經濟
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at 2015-05-30T16:21
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at 2015-06-03T13:51
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