blocks|key|47987|text|这是告诉您，某些预测器集是/are完全(多个)共线；如果您查看coef(reg1)，您将看到至少一个NA值，如果运行summary(lm)，您将看到消息。|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|CODE|entityRanges|data|47988|47989|+(n因奇点而未定义)|blockquote|47990|47991|47992|(对于一些n>=1)。仅仅检查预测变量的两两相关性是不够的，因为如果你有(例如)预测因子A，B，C，其中(绝对值)的两两相关不完全是1，它们仍然可以是_multi_collinear。(可能最常见的情况是，A，B，C是描述一组相互排斥和完整的可能性的虚拟变量，也就是说，对于每个观测结果，A、B、C中的一个是1，另外两个是0。我强烈怀疑这就是最近16个左右的变量所发生的情况，这些变量似乎是奥斯陆的行政区.)|47993|检查回归的哪些系数是NA+(如@Axeman所建议的)可以提示问题所在；this+answer解释了如何使用model.matrix()和caret::findLinearCombos准确地确定哪些预测器是导致问题的。(如果所有的预测变量都是简单的数值变量，则可以跳过model.matrix()。)|47994|如果您的问题确实是由为每个可能的地理区域包含一个虚拟变量引起的，那么最简单/最好的解决方案就是将地理区域(borough)作为一个因素包含在模型中:如果这样做，R将自动生成一组虚拟/对比，但它将自动离开一个虚拟变量以避免此类问题。如果您以后想要返回并获得每个区的预测值，您可以使用emmeans或effects包中的工具。|47995|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/71270248/error-warning-on-running-glm-in-r-coefficients-1-not-defined-because-of-sing/71270467#71270467^0|H|2|V|A|1E|2|1M|B|0|0|0|0|0|0|A|2|1I|E|1X|N|3Q|E|10|B|0|0|3W|7|44|7|0^^$0|@$1|2|3|4|5|6|7|10|8|@$9|11|A|12|B|C]|$9|13|A|14|B|D]|$9|15|A|16|B|D]|$9|17|A|18|B|D]]|E|@]|F|$]]|$1|G|3|-4|5|6|7|19|8|@]|E|@]|F|$]]|$1|H|3|I|5|J|7|1A|8|@]|E|@]|F|$]]|$1|K|3|-4|5|6|7|1B|8|@]|E|@]|F|$]]|$1|L|3|-4|5|6|7|1C|8|@]|E|@]|F|$]]|$1|M|3|N|5|6|7|1D|8|@]|E|@]|F|$]]|$1|O|3|P|5|6|7|1E|8|@$9|1F|A|1G|B|D]|$9|1H|A|1I|B|D]|$9|1J|A|1K|B|D]|$9|1L|A|1M|B|D]]|E|@$9|1N|A|1O|1|1P]]|F|$]]|$1|Q|3|R|5|6|7|1Q|8|@$9|1R|A|1S|B|D]|$9|1T|A|1U|B|D]]|E|@]|F|$]]|$1|S|3|-4|5|6|7|1V|8|@]|E|@]|F|$]]]|T|$U|$5|V|W|X|F|$Y|Z]]]]

This is telling you that some set(s) of predictors is/are perfectly (multi)collinear; if you looked at <code>coef(reg1)</code> you would see at least one <code>NA</code> value, and if you ran <code>summary(lm)</code> you would see the message
<blockquote>
([n] not defined because of singularities)
</blockquote>
(for some n&gt;=1). Examining the pairwise correlations of the predictor variables is not enough, because if you have (e.g.) predictors A, B, C where (the absolute values of) none of the pairwise correlations are exactly 1, they can still be multicollinear. (Probably the most common case is where A, B, C are dummy variables that describe a mutually exclusive and complete set of possibilities [i.e. for each observation exactly one of A, B, C is 1 and the other two are 0]. I strongly suspect that this is what's going on with your last 16 or so variables, which seem to be boroughs of Oslo ...)
Checking to see which coefficients of the regression are <code>NA</code> (as suggested by @Axeman) can suggest where the problem is;
<a href="https://stackoverflow.com/questions/71270248/error-warning-on-running-glm-in-r-coefficients-1-not-defined-because-of-sing/71270467#71270467">this answer</a> explains how you can use <code>model.matrix()</code> and <code>caret::findLinearCombos</code> to figure out exactly which sets of predictors are causing the problem. (If all of your predictors are simple numeric variables you can skip <code>model.matrix()</code>.)
If your problem is indeed caused by including a dummy variable for every possible geographic region, the simplest/best solution is to include geographic region (borough) in the model as a factor: if you do this, R will automatically generate a set of dummies/contrasts, but it will leave one dummy out automatically to avoid this kind of problem. If you later want to go back and get predicted values for every borough, you can use tools from the <code>emmeans</code> or <code>effects</code> packages.

blocks|key|48076|text|我四处寻找解决方案，因为我无法根据答案来解决它们。然而，这些答案帮助我更好地理解了我的问题。我的问题的解决办法很简单，就是对一个虚拟变量加一个减号，而不是加号。这最初是我的代码，正如我早些时候发布的：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|48077|reg.model1+<-+log(Price2)+~+Detached.house+%2B+Semi.detached.house+%2B+
+++++++++++++++Attached.houses+%2B+Apartment+%2B
++++++Stock.apartment+%2B+Housing.cooperative+%2B+Sole.owner+%2B+Age+%2B
++++++BRA+%2B+Bedrooms+%2B+Balcony+%2B+Lotsize+%2B+Sentrum+%2B+Alna+%2B+Vestre.Aker+%2B
++++++Nordstrand+%2B+Marka+%2B+Ullern+%2B+Østensjø+%2B+Søndre.Nordstrand+%2B+Stovner+%2B+
++++++Nordre.Aker+%2B+Bjerke+%2B+Grorud+%2B+Gamle.Oslo+%2B+St..Hanshaugen+%2B+
++++++Grünerløkka+%2B+Sagene+%2B+Frogner+
reg1+<-+lm(formula+=+reg.model1,+data+=+Data)
vif(reg1)|code-block|syntax|javascript|48078|为了解决我的问题，我只需将代码更改为：|48079|reg.model1+<-+log(Price2)+~+Detached.house+%2B+Semi.detached.house+%2B+
+++++++++++++++Attached.houses+-+Apartment+%2B
++++++Stock.apartment+%2B+Housing.cooperative+-+Sole.owner+%2B+Age+%2B
++++++BRA+%2B+Bedrooms+%2B+Balcony+%2B+Lotsize+%2B+Sentrum+%2B+Alna+%2B+Vestre.Aker+%2B
++++++Nordstrand+%2B+Marka+%2B+Ullern+%2B+Østensjø+%2B+Søndre.Nordstrand+%2B+Stovner+%2B+
++++++Nordre.Aker+%2B+Bjerke+%2B+Grorud+%2B+Gamle.Oslo+%2B+St..Hanshaugen+%2B+
++++++Grünerløkka+%2B+Sagene+-+Frogner+
reg1+<-+lm(formula+=+reg.model1,+data+=+Data)
vif(reg1)|48080|正如你所看到的，我有3个系列的假人，为了确保不发生多重共线性，我必须从每个假人中删除一个假人。我已经拆除了房屋类型的公寓，拥有一种所有权的唯一业主，以及地区的弗罗格纳。这个网站解释了这个问题和解决方案比我(https://www.learndatasci.com/glossary/dummy-variable-trap/)更好和更简单！|offset|length|48081|entityMap|0|LINK|mutability|MUTABLE|url|https://www.learndatasci.com/glossary/dummy-variable-trap/^0|0|0|0|0|2V|1M|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|X|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Y|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|Z|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|10|8|@]|9|@$M|11|N|12|1|13]]|A|$]]|$1|O|3|-4|5|6|7|14|8|@]|9|@]|A|$]]]|P|$Q|$5|R|S|T|A|$U|V]]]]

I searched around for solutions since I couldn't solve them based on the answers. The answers, however, helped me understand my problem better. The solution to my problem was as simple as to put a minus instead of plus for one of the dummy variables. This was originally my code as I posted earlier:
<pre><code>reg.model1 &lt;- log(Price2) ~ Detached.house + Semi.detached.house + 
 Attached.houses + Apartment +
 Stock.apartment + Housing.cooperative + Sole.owner + Age +
 BRA + Bedrooms + Balcony + Lotsize + Sentrum + Alna + Vestre.Aker +
 Nordstrand + Marka + Ullern + Østensjø + Søndre.Nordstrand + Stovner + 
 Nordre.Aker + Bjerke + Grorud + Gamle.Oslo + St..Hanshaugen + 
 Grünerløkka + Sagene + Frogner 
reg1 &lt;- lm(formula = reg.model1, data = Data)
vif(reg1)
</code></pre>
To solve my issue i had to simply change my code to:
<pre><code>reg.model1 &lt;- log(Price2) ~ Detached.house + Semi.detached.house + 
 Attached.houses - Apartment +
 Stock.apartment + Housing.cooperative - Sole.owner + Age +
 BRA + Bedrooms + Balcony + Lotsize + Sentrum + Alna + Vestre.Aker +
 Nordstrand + Marka + Ullern + Østensjø + Søndre.Nordstrand + Stovner + 
 Nordre.Aker + Bjerke + Grorud + Gamle.Oslo + St..Hanshaugen + 
 Grünerløkka + Sagene - Frogner 
reg1 &lt;- lm(formula = reg.model1, data = Data)
vif(reg1)
</code></pre>
As you can see I have 3 series of dummies, and to make sure multicollinearity doesn't occur I have to remove one dummy from each one. I have removed apartments for the type of home, sole owner for a type of ownership, and Frogner for the district. This website explained this problem and solution much better and simpler than I (<a href="https://www.learndatasci.com/glossary/dummy-variable-trap/" rel="nofollow noreferrer">https://www.learndatasci.com/glossary/dummy-variable-trap/</a>)!

I am trying to run a <code>car::vif()</code> test in R, to test for multicollinearity. However, when I run the code
<pre class="lang-r prettyprint-override"><code>reg.model1 &lt;- log(Price2) ~ Detached.house + Semi.detached.house + 
 Attached.houses + Apartment +
 Stock.apartment + Housing.cooperative + Sole.owner + Age +
 BRA + Bedrooms + Balcony + Lotsize + Sentrum + Alna + Vestre.Aker +
 Nordstrand + Marka + Ullern + Østensjø + Søndre.Nordstrand + Stovner + 
 Nordre.Aker + Bjerke + Grorud + Gamle.Oslo + St..Hanshaugen + 
 Grünerløkka + Sagene + Frogner 
reg1 &lt;- lm(formula = reg.model1, data = Data)
vif(reg1)
</code></pre>
I receive this error in the console:
<blockquote>
Error in vif.default(reg1) : there are aliased coefficients in the model.
</blockquote>
What I have read is that this means that there is something in the model that is highly correlated. When I look at the correlation matrix the only thing that is highly correlated is the dependent variable <code>Price</code>. But I also read somewhere that the dependent variable is okay even if it's highly correlated. I also found out that <code>BRA</code> is 0.8 in correlation so I tried to run it again without this, and still get the same error. Does anyone know what the problem could be, or what I could try to do differently?

Multicollinearity test with car::vif

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我试图在R中运行一个car::vif()测试，以测试多重共线性。但是，当我运行代码时reg.model1 <- log(Price2) ~ Detached.house + Semi.detached.house + Attached.houses + Apartment + S...

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