R语言特征选择方法——最佳子集回归、逐步回归|附代码数据
原创R语言特征选择方法——最佳子集回归、逐步回归|附代码数据
原创
拓端
发布于 2023-02-20 22:22:32
发布于 2023-02-20 22:22:32
原文链接：http://tecdat.cn/?p=5453
最近我们被客户要求撰写关于特征选择方法的研究报告，包括一些图形和统计输出。
变量选择方法
所有可能的回归
model?<-?lm(mpg?~?disp?+?hp?+?wt?+?qsec,?data?=?mtcars)
ols_all_subset(model)
##?#?A?tibble:?15?x?6
##????Index?????N??????Predictors?`R-Square`?`Adj.?R-Square`?`Mallow's?Cp`
##??????????????????????????????????????????
##??1?????1?????1??????????????wt????0.75283?????????0.74459??????12.48094
##??2?????2?????1????????????disp????0.71834?????????0.70895??????18.12961
##??3?????3?????1??????????????hp????0.60244?????????0.58919??????37.11264
##??4?????4?????1????????????qsec????0.17530?????????0.14781?????107.06962
##??5?????5?????2???????????hp?wt????0.82679?????????0.81484???????2.36900
##??6?????6?????2?????????wt?qsec????0.82642?????????0.81444???????2.42949
##??7?????7?????2?????????disp?wt????0.78093?????????0.76582???????9.87910
##??8?????8?????2?????????disp?hp????0.74824?????????0.73088??????15.23312
##??9?????9?????2???????disp?qsec????0.72156?????????0.70236??????19.60281
##?10????10?????2?????????hp?qsec????0.63688?????????0.61183??????33.47215
##?11????11?????3??????hp?wt?qsec????0.83477?????????0.81706???????3.06167
##?12????12?????3??????disp?hp?wt????0.82684?????????0.80828???????4.36070
##?13????13?????3????disp?wt?qsec????0.82642?????????0.80782???????4.42934
##?14????14?????3????disp?hp?qsec????0.75420?????????0.72786??????16.25779
##?15????15?????4?disp?hp?wt?qsec????0.83514?????????0.81072???????5.00000该plot方法显示了所有可能的回归方法的拟合 ?。
model?<-?lm(mpg?~?disp?+?hp?+?wt?+?qsec,?data?=?mtcars)
k?<-?ols_all_subset(model)
plot(k)
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最佳子集回归
选择在满足一些明确的客观标准时做得最好的预测变量的子集，例如具有最大R2值或最小MSE， Cp或AIC。
model?<-?lm(mpg?~?disp?+?hp?+?wt?+?qsec,?data?=?mtcars)
ols_best_subset(model)
##????Best?Subsets?Regression????
##?------------------------------
##?Model?Index????Predictors
##?------------------------------
##??????1?????????wt??????????????
##??????2?????????hp?wt???????????
##??????3?????????hp?wt?qsec??????
##??????4?????????disp?hp?wt?qsec?
##?------------------------------
##?
##???????????????????????????????????????????????????Subsets?Regression?Summary???????????????????????????????????????????????????
##?-------------------------------------------------------------------------------------------------------------------------------
##????????????????????????Adj.????????Pred?????????????????????????????????????????????????????????????????????????????????????????
##?Model????R-Square????R-Square????R-Square?????C(p)????????AIC????????SBIC????????SBC????????MSEP??????FPE???????HSP???????APC??
##?-------------------------------------------------------------------------------------------------------------------------------
##???1????????0.7528??????0.7446??????0.7087????12.4809????166.0294????74.2916????170.4266????9.8972????9.8572????0.3199????0.2801?
##???2????????0.8268??????0.8148??????0.7811?????2.3690????156.6523????66.5755????162.5153????7.4314????7.3563????0.2402????0.2091?
##???3????????0.8348??????0.8171???????0.782?????3.0617????157.1426????67.7238????164.4713????7.6140????7.4756????0.2461????0.2124?
##???4????????0.8351??????0.8107???????0.771?????5.0000????159.0696????70.0408????167.8640????8.1810????7.9497????0.2644????0.2259?
##?-------------------------------------------------------------------------------------------------------------------------------
##?AIC:?Akaike?Information?Criteria?
##??SBIC:?Sawa's?Bayesian?Information?Criteria?
##??SBC:?Schwarz?Bayesian?Criteria?
##??MSEP:?Estimated?error?of?prediction,?assuming?multivariate?normality?
##??FPE:?Final?Prediction?Error?
##??HSP:?Hocking's?Sp?
##??APC:?Amemiya?Prediction?Criteriaplot
model?<-?lm(mpg?~?disp?+?hp?+?wt?+?qsec,?data?=?mtcars)
k?<-?ols_best_subset(model)
plot(k)
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逐步前进回归
从一组候选预测变量中建立回归模型，方法是逐步输入基于p值的预测变量，直到没有变量进入变量。该模型应该包括所有的候选预测变量。如果细节设置为TRUE，则显示每个步骤。
点击标题查阅往期内容
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R语言多元逐步回归模型分析房价和葡萄酒价格：选择最合适的预测变量
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变量选择
#向前逐步回归
model?<-?lm(y?~?.,?data?=?surgical)
ols_step_forward(model)
##?We?are?selecting?variables?based?on?p?value...
##?1?variable(s)?added....
##?1?variable(s)?added...
##?1?variable(s)?added...
##?1?variable(s)?added...
##?1?variable(s)?added...
##?No?more?variables?satisfy?the?condition?of?penter:?0.3
##?Forward?Selection?Method???????????????????????????????????????????????????????
##?
##?Candidate?Terms:???????????????????????????????????????????????????????????????
##?
##?1?.?bcs????????????????????????????????????????????????????????????????????????
##?2?.?pindex?????????????????????????????????????????????????????????????????????
##?3?.?enzyme_test????????????????????????????????????????????????????????????????
##?4?.?liver_test?????????????????????????????????????????????????????????????????
##?5?.?age????????????????????????????????????????????????????????????????????????
##?6?.?gender?????????????????????????????????????????????????????????????????????
##?7?.?alc_mod????????????????????????????????????????????????????????????????????
##?8?.?alc_heavy??????????????????????????????????????????????????????????????????
##?
##?------------------------------------------------------------------------------
##???????????????????????????????Selection?Summary????????????????????????????????
##?------------------------------------------------------------------------------
##?????????Variable?????????????????????Adj.?????????????????????????????????????????
##?Step??????Entered??????R-Square????R-Square?????C(p)????????AIC?????????RMSE??????
##?------------------------------------------------------------------------------
##????1????liver_test???????0.4545??????0.4440????62.5119????771.8753????296.2992????
##????2????alc_heavy????????0.5667??????0.5498????41.3681????761.4394????266.6484????
##????3????enzyme_test??????0.6590??????0.6385????24.3379????750.5089????238.9145????
##????4????pindex???????????0.7501??????0.7297?????7.5373????735.7146????206.5835????
##????5????bcs??????????????0.7809??????0.7581?????3.1925????730.6204????195.4544????
##?------------------------------------------------------------------------------
?
model?<-?lm(y?~?.,?data?=?surgical)
k?<-?ols_step_forward(model)
##?We?are?selecting?variables?based?on?p?value...
##?1?variable(s)?added....
##?1?variable(s)?added...
##?1?variable(s)?added...
##?1?variable(s)?added...
##?1?variable(s)?added...
##?No?more?variables?satisfy?the?condition?of?penter:?0.3
plot(k)
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本文摘选 《 R语言特征选择——逐步回归 》 ，点击“阅读原文”获取全文完整资料。
点击标题查阅往期内容
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原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。
如有侵权，请联系 cloudcommunity@tencent.com 删除。
原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。
如有侵权，请联系 cloudcommunity@tencent.com 删除。
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