R語言特徵選擇

原文連接：http://tecdat.cn/?p=5453

變量選擇方法

全部可能的迴歸

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

該plot方法顯示了全部可能的迴歸方法的擬合  。

model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)k <- ols_all_subset(model)plot(k)

最佳子集迴歸

選擇在知足一些明確的客觀標準時作得最好的預測變量的子集，例如具備最大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 Criteria

plot

model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)k <- ols_best_subset(model)plot(k)

逐步前進迴歸

從一組候選預測變量中創建迴歸模型，方法是逐步輸入基於p值的預測變量，直到沒有變量進入變量。該模型應該包括全部的候選預測變量。若是細節設置爲TRUE，則顯示每一個步驟。

變量選擇

# stepwise forward regressionmodel <- 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: 0.3plot(k)