予測は予測されません

Question

私は2データフレームを持っています。 1つはトレーニングデータ（pubs1）、もう1つは（pubs2）のテストデータです。私は線形回帰オブジェクトを作成することはできますが、予測を作成することはできません。これは初めてのことではなく、何がうまくいかないのか分からない。

> head(pubs1) 
    id pred37 actual  weight  diff1 weightDiff1 pred1 pred2 pred3 pred4 
1 11 128.3257 128.3990 6.43482732 -0.07333650 -0.4719076922 126.3149 126.1024 126.9057 126.2718 
2 31 100.8822 100.9777 3.55520287 -0.09553741 -0.3396548680 100.7820 100.8589 100.9179 100.8903 
3 33 100.7204 100.9630 7.46413438 -0.24262409 -1.8109787866 100.8576 100.8434 100.8521 100.8914 
4 52 100.8564 100.9350 0.01299138 -0.07855588 -0.0010205495 100.8700 100.8925 100.8344 100.8714 
5 56 100.8410 100.9160 0.01299138 -0.07502125 -0.0009746298 100.8695 100.8889 100.8775 100.8871 
6 71 100.8889 100.8591 1.19266269 0.02979818 0.0355391800 100.8357 100.9205 100.8107 100.8316 
> head(pubs2) 
     id pred37  pred1 pred2  pred3  pred4 
1 762679 98.32212 97.84181 98.0776 98.03222 97.90022 
2 762680 115.79698 114.91411 115.1470 115.27129 115.45027 
3 762681 104.56418 104.81372 104.8537 104.66239 104.55240 
4 762682 106.65768 106.71011 106.6722 106.68662 106.60757 
5 762683 102.15662 103.14207 103.2035 103.31190 103.40397 
6 762684 101.96057 102.25939 102.1031 102.20659 102.04557 

> lm1 <- lm(pubs1$actual ~ pubs1$pred37 + pubs1$pred1 + pubs1$pred2 
+ + pubs1$pred3 + pubs1$pred4) 
> summary(lm1) 

Call: 
lm(formula = pubs1$actual ~ pubs1$pred37 + pubs1$pred1 + pubs1$pred2 + 
    pubs1$pred3 + pubs1$pred4) 

Residuals: 
    Min  1Q Median  3Q  Max 
-18.3415 -0.2309 0.0016 0.2236 17.8639 

Coefficients: 
       Estimate Std. Error t value Pr(>|t|)  
(Intercept) -0.122478 0.027227 -4.498 6.85e-06 *** 
pubs1$pred37 0.543270 0.005086 106.823 < 2e-16 *** 
pubs1$pred1 0.063680 0.007151 8.905 < 2e-16 *** 
pubs1$pred2 0.317768 0.010977 28.950 < 2e-16 *** 
pubs1$pred3 0.024302 0.008321 2.921 0.00349 ** 
pubs1$pred4 0.052183 0.010879 4.797 1.61e-06 *** 
--- 
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 0.7298 on 99994 degrees of freedom 
Multiple R-squared: 0.9932,  Adjusted R-squared: 0.9932 
F-statistic: 2.926e+06 on 5 and 99994 DF, p-value: < 2.2e-16 

> 

> pred2 <- predict(lm1, pubs2) 
Warning message: 
'newdata' had 50000 rows but variable(s) found have 100000 rows 

> str(pubs1) 
'data.frame': 100000 obs. of 10 variables: 
$ id   : num 11 31 33 52 56 71 85 87 92 95 ... 
$ pred37  : num 128 101 101 101 101 ... 
$ actual  : num 128 101 101 101 101 ... 
$ weight  : num 6.435 3.555 7.464 0.013 0.013 ... 
$ diff1  : num -0.0733 -0.0955 -0.2426 -0.0786 -0.075 ... 
$ weightDiff1: num -0.471908 -0.339655 -1.810979 -0.001021 -0.000975 ... 
$ pred1  : num 126 101 101 101 101 ... 
$ pred2  : num 126 101 101 101 101 ... 
$ pred3  : num 127 101 101 101 101 ... 
$ pred4  : num 126 101 101 101 101 ... 
> str(pubs2) 
'data.frame': 50000 obs. of 6 variables: 
$ id : num 762679 762680 762681 762682 762683 ... 
$ pred37: num 98.3 115.8 104.6 106.7 102.2 ... 
$ pred1 : num 97.8 114.9 104.8 106.7 103.1 ... 
$ pred2 : num 98.1 115.1 104.9 106.7 103.2 ... 
$ pred3 : num 98 115 105 107 103 ... 
$ pred4 : num 97.9 115.5 104.6 106.6 103.4 ... 
> colnames(pubs1) 
[1] "id"   "pred37"  "actual"  "weight"  "diff1"  "weightDiff1" "pred1"  "pred2"  "pred3"  "pred4"  
> colnames(pubs2) 
[1] "id"  "pred37" "pred1" "pred2" "pred3" "pred4" 

ここには何かがありますか？

Answer 1

の代わりに、

lm1 <- lm(pubs1$actual ~ pubs1$pred37 + pubs1$pred1 + pubs1$pred2 
      pubs1$pred3 + pubs1$pred4) 

試して、

lm1 <- lm(actual ~ pred37 + pred1 + pred2 
      pred3 + pred4, data = pubs1) 

そうでない場合predict.lmは、新しいデータフレームにpubs1$pred37と呼ばれる変数を探しています。