如何解决“秩亏拟合可能是误导性错误"在我的线性模型上? [英] How to solve "rank-deficient fit may be misleading error" on my linear model?
问题描述
我在使用模型进行一些预测时遇到问题，R 显示此消息 Warning message prediction from a rank-deficient fit may be mislead，我该如何解决?我认为我的模型是正确的是预测失败，我不知道为什么.
I have a problem when I use my model to do some prediction, R shows this message Warning message prediction from a rank-deficient fit may be misleading, how can I solve it? I think my model is correct is the prediction that fails and I don't know why.
在这里你可以一步一步地看到我在做什么以及模型的总结:
Here you can see step by step what I am doing and the summary of model:
myModel <- lm(margin~.,data = dataClean[train,c(target,numeric,categoric)])
Call:
lm(formula = margin ~ ., data = dataClean[train, c(target, numeric, categoric)])
Residuals:
Min 1Q Median 3Q Max
-0.220407 -0.035272 -0.003415 0.028227 0.276727
Coefficients: (2 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.061e-01 2.260e-02 26.817 < 2e-16 ***
price 1.042e-05 8.970e-06 1.162 0.245610
shipping 1.355e-03 2.741e-04 4.943 9.25e-07 ***
categoryofficeSupplies -7.721e-02 2.295e-02 -3.364 0.000802 ***
categorytechnology -3.993e-02 2.325e-02 -1.717 0.086249 .
subCategorybindersAndAccessories -1.650e-01 1.421e-02 -11.612 < 2e-16 ***
subCategorybookcases 3.337e-04 2.328e-02 0.014 0.988565
subCategorychairsChairmats -3.104e-02 2.106e-02 -1.474 0.140831
subCategorycomputerPeripherals 1.356e-02 1.293e-02 1.049 0.294604
subCategorycopiersAndFax -1.943e-01 2.944e-02 -6.598 7.27e-11 ***
subCategoryenvelopes -1.648e-01 2.045e-02 -8.057 2.62e-15 ***
subCategorylabels -1.534e-01 1.984e-02 -7.730 3.00e-14 ***
subCategoryofficeFurnishings -8.827e-02 2.220e-02 -3.976 7.61e-05 ***
subCategoryofficeMachines -1.521e-01 1.639e-02 -9.281 < 2e-16 ***
subCategorypaper -1.624e-01 1.363e-02 -11.909 < 2e-16 ***
subCategorypensArtSupplies -8.484e-04 1.524e-02 -0.056 0.955623
subCategoryrubberBands 3.174e-02 2.245e-02 1.414 0.157854
subCategoryscissorsRulersTrimmers 1.092e-01 2.327e-02 4.693 3.13e-06 ***
subCategorystorageOrganization 1.219e-01 1.575e-02 7.739 2.82e-14 ***
subCategorytables NA NA NA NA
subCategorytelephoneAndComunication NA NA NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.08045 on 858 degrees of freedom
Multiple R-squared: 0.6512, Adjusted R-squared: 0.6439
F-statistic: 88.98 on 18 and 858 DF, p-value: < 2.2e-16
estimateModel <- predict(myModel, type="response", newdata=dataClean[test, c(numeric,categoric,target)])
Warning message:
In predict.lm(myModel, type = "response", newdata = dataClean[test, :
prediction from a rank-deficient fit may be misleading
推荐答案
您的两个子类别级别的相关系数被抑制.这意味着它们中的每一个都可以通过价格和运费以及其他类别和子类别级别的某种组合来 100% 预测.这在 R 文档中称为别名".警告可能重要也可能不重要，尽管同意@ZheyuanLi 的观点，这可能是良性的.我不认为这个特殊的警告可能是由于缺失值造成的，因为当任何一个变量有缺失值时，R 回归函数通常以删除整行的方式运行.两个变量之间存在 100% 相关性的理论也不太可能.如果您想查找可能导致此问题的显示组合，我建议从
Two of your SubCategory levels had their associated coefficients suppressed. That means that each of them can be 100% predicted by some combination of price and shipping and the other category and subCategory levels. This is known in the R documentation as being "aliased". The warning may or may not be important, although agree with @ZheyuanLi that it's probably benign. I don't think that this particular warning can be be due to missing values since R regression functions generally operate in a manner to remove entire rows when any one variable has a missing value. Also unlikely is the theory that there is 100% correlation between two variables. If you want to find display the combinations that might give rise to this I suggest starting with
with( dataClean , table( category, SubCategory) )
我预测您会在一个子类别中发现一个或多个类别行.
I predict you will find on one SubCategory is one or more of the category rows.
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