Caret的主成分分析

我正在使用Caret的PCI預處理。Caret的主成分分析

multinomFit <- train(LoanStatus~., train, method = "multinom", std=TRUE, family=binomial, metric = "ROC", thresh = 0.85, verbose = TRUE, pcaComp=7, preProcess=c("center", "scale", "pca"), 
    trControl = ctrl)

我指定，PCA組件的數量是7，爲什麼彙總顯示使用68個組件的配合？

summary(multinomFit) 

Call: 
multinom(formula = .outcome ~ ., data = dat, decay = param$decay, 
    std = TRUE, family = ..2, thresh = 0.85, verbose = TRUE, 
    pcaComp = 7) 

Coefficients: 
        Values Std. Err. 
(Intercept) 1.6650694329 0.03760419 
PC1   -0.1023790683 0.01474812 
PC2   0.0375344688 0.01554707 
PC3   -0.1012080589 0.01870754 
PC4   -0.1004020357 0.02418817 
PC5   0.0707421015 0.02403815 
PC6   0.0034671796 0.02535015 
PC7   0.1218028495 0.02852909 
PC8   0.2191031963 0.03291266 
PC9   0.1534144811 0.02986523 
PC10  -0.0665337138 0.02999863 
PC11  -0.1313662645 0.03032963 
PC12   0.0668422208 0.03397493 
PC13   0.0002770594 0.03282500 
PC14  -0.0883400819 0.03337427 
PC15   0.0221726084 0.03323058 
PC16  -0.0222984250 0.718 
PC17  -0.0394014147 0.03282160 
PC18   0.0280583827 0.03459664 
PC19  -0.0295243295 0.03430506 
PC20  -0.0149573710 0.03358775 
PC21   0.0653722886 0.03388418 
PC22  -0.0114810174 0.03583050 
PC23  -0.0594912738 0.03376091 
PC24   0.0117123190 0.03476835 
PC25  -0.0406770388 0.03507369 
PC26   0.0373200991 0.03440807 
PC27   0.0050323427 0.03366658 
PC28   0.0678087286 0.03516197 
PC29   0.0234294196 0.03459586 
PC30   0.0540846491 0.03464610 
PC31   0.1054946257 0.03459315 
PC32   0.0216292907 0.03485001 
PC33   0.0247627243 0.03488016 
PC34   0.0033126360 0.03402770 
PC35  -0.0434168834 0.03468038 
PC36  -0.0098687981 0.03497515 
PC37  -0.0193788562 0.03268054 
PC38   0.0572276670 0.03837009 
PC39   0.0535213906 0.03737078 
PC40   0.0007157334 0.03321343 
PC41  -0.0286461676 0.03546742 
PC42   0.0640903943 0.03378855 
PC43  -0.0111873647 0.03626063 
PC44  -0.0304589978 0.03448459 
PC45   0.0191817954 0.03690284 
PC46  -0.0330040383 0.03277895 
PC47   0.0328641857 0.03460263 
PC48   0.0204941541 0.03460759 
PC49   0.0345105736 0.04002168 
PC50   0.0076131373 0.03621336 
PC51   0.0082765068 0.03299395 
PC52  -0.0594596197 0.03633509 
PC53  -0.0276656822 0.03596515 
PC54   0.0411414647 0.03529887 
PC55  -0.0644394706 0.03490393 
PC56  -0.0266971243 0.03403656 
PC57  -0.1415322396 0.03681683 
PC58  -0.0332329932 0.03469459 
PC59  -0.0273683007 0.03524604 
PC60   0.0450430472 0.03586438 
PC61  -0.0708218651 0.03807458 
PC62   0.1523605734 0.03851722 
PC63  -0.0385759566 0.03920662 
PC64  -0.0602633030 0.03902837 
PC65   0.0547553856 0.03970764 
PC66   0.0727331180 0.04273518 
PC67   0.1142574406 0.04522347 
PC68  -0.1059928013 0.04077592 

Residual Deviance: 5273.035 
AIC: 5411.035

最後，有一種方法，其描述了變異的85％，在數據恢復到7輸入中的原始觀察屬性的7個PCA因素映射？

在此先感謝。

來源

2015-09-14 dbl001

根據該文件，則需要通過列表來傳遞參數'pcaComp'在'preProcess'。 – 2015-09-14 05:03:42

它顯示我在彙總中使用的參數（multinomFit）調用：多項式（公式= .outcome =。，data = dat，decay = param $ decay， std = TRUE，family =。2，thresh = 0.85 ，verbose = TRUE， pcaComp = 7） – dbl001

這不是我所建議的。 – 2015-09-14 06:22:55

您可以通過preProcOptions在trainControl()中通過預處理選項，看看?trainControl。這裏有一個例子，

ctrl <- trainControl(method = "repeatedcv", 
        repeats = 3, 
        classProbs = TRUE, 
        preProcOptions = list(thresh = 0.85), #or list(pcaComp = 7) 
        summaryFunction = twoClassSummary) 

multinomFit <- train(LoanStatus~., train, 
        method = "multinom", 
        family=binomial, 
        metric = "ROC", 
        verbose = TRUE, 
        preProcess=c("center", "scale", "pca"), 
        trControl = ctrl)

注意，如果你指定PCA分量pcaComp = 7的數量，這種情況會騎thresh（看看?preProcess）。所以使用其中之一。

您可以通過查看變量的貢獻，每個PCA組件：

multinomFit$preProcess$rotation

來源

2015-09-14 06:20:42 howaj

Caret的主成分分析

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