R 向逻辑回归（glm）模型中添加多个解释变量会产生错误吗？_R_Glm

R 向逻辑回归（glm）模型中添加多个解释变量会产生错误吗？

R 向逻辑回归（glm）模型中添加多个解释变量会产生错误吗？,r,glm,R,Glm,我已尝试拟合以下线性模型： ad.glm.all <- glm(WinLoss ~ Score + Margin + Opposition + Venue + Disposals + Marks + Goals + Behinds + Hitouts + Tackles + Rebound50s + Inside50s + Clearances + Clangers + FreesFor + ContendedPossessions + ContestedMarks + MarksIns

我已尝试拟合以下线性模型：

ad.glm.all <- glm(WinLoss ~  Score + Margin + Opposition + Venue + Disposals + Marks + Goals + Behinds + Hitouts + Tackles + Rebound50s + Inside50s + Clearances + Clangers + FreesFor + ContendedPossessions + ContestedMarks + MarksInside50 + OnePercenters + Bounces+GoalAssists, 
                  data = ad.train, family = binomial)

当我看到这个回归模型的总结时，我得到：

Call:
glm(formula = WinLoss ~ Score + Margin + Disposals + Marks + 
    Goals + Behinds + Hitouts + Tackles + Rebound50s + Inside50s + 
    Clearances + Clangers + FreesFor + ContendedPossessions + 
    ContestedMarks + MarksInside50 + OnePercenters + Bounces + 
    GoalAssists, family = binomial, data = ad.train)

Deviance Residuals: 
       Min          1Q      Median          3Q         Max  
-2.980e-05  -2.100e-08   2.100e-08   2.100e-08   3.569e-05  

Coefficients:
                       Estimate Std. Error z value Pr(>|z|)
(Intercept)          -8.578e+00  2.502e+06   0.000        1
Score                 4.194e+00  5.165e+04   0.000        1
Margin                2.187e+00  3.742e+03   0.001        1
Disposals             8.946e-02  3.549e+03   0.000        1
Marks                 1.427e-01  1.938e+03   0.000        1
Goals                -2.288e+01  3.082e+05   0.000        1
Behinds              -7.034e+00  5.482e+04   0.000        1
Hitouts               3.640e-02  5.167e+03   0.000        1
Tackles               8.939e-01  7.075e+03   0.000        1
Rebound50s           -2.064e-01  8.497e+03   0.000        1
Inside50s             5.645e-01  8.133e+03   0.000        1
Clearances           -1.930e-01  1.525e+04   0.000        1
Clangers             -2.040e-01  1.056e+04   0.000        1
FreesFor             -7.699e-01  1.762e+04   0.000        1
ContendedPossessions -5.752e-01  7.424e+03   0.000        1
ContestedMarks       -1.869e+00  1.069e+04   0.000        1
MarksInside50         6.742e-01  1.676e+04   0.000        1
OnePercenters         1.616e-01  6.888e+03   0.000        1
Bounces              -8.763e-01  7.669e+03   0.000        1
GoalAssists           7.570e-01  3.299e+04   0.000        1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1.2540e+02  on 91  degrees of freedom
Residual deviance: 7.1154e-09  on 72  degrees of freedom
AIC: 40

Number of Fisher Scoring iterations: 25

显然这里出了严重的问题，对吗？每个变量的P值不能都是1，Z值都是0；对吧?

我给了它一个谷歌，我能找到的最好的结果是有人建议错误可能是因为变量太多（考虑到我有多少变量，这是有道理的）。因此，我开始一个接一个地删除它们，并且每次尝试都会得到错误，直到我只有一个变量（x~y）；只有这样我才不会出错
有人能给我解释一下这个错误是什么意思吗？为什么我所有的P值都是1，z值都是0
提前谢谢

-特洛伊城在我看来就像是一个过度装修的粗鲁例子。你可能想试试套索/弹性网。还有，你有没有检查过你的一个预测因素是否会导致已经完美的分离？@Roland我会看看套索/弹性网，猜测它们只是你可以添加到R中处理这类事情的包？我猜，因为这是体育数据，我在看赢/输，“利润”变量可能是一个完美的分隔符；有什么建议可以确定吗？那么分数是多少？把你的依赖者和预测者列成一个列联表。更新：我想我明白了为什么glm如此怪异；没有足够的数据。当我将数据分成不同的团队时，我的训练集中有20行数据，测试集中有5行数据（每个团队）。将所有团队合并到一个大数据框架中，得出的数字是这些数字的18倍。我很确定这就是我不再犯奇怪错误的原因。谢谢你们的帮助，小伙子们。
Call: glm(formula = WinLoss ~ Score + Margin + Disposals + Marks + Goals + Behinds + Hitouts + Tackles + Rebound50s + Inside50s + Clearances + Clangers + FreesFor + ContendedPossessions + ContestedMarks + MarksInside50 + OnePercenters + Bounces + GoalAssists, family = binomial, data = ad.train) Deviance Residuals: Min 1Q Median 3Q Max -2.980e-05 -2.100e-08 2.100e-08 2.100e-08 3.569e-05 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -8.578e+00 2.502e+06 0.000 1 Score 4.194e+00 5.165e+04 0.000 1 Margin 2.187e+00 3.742e+03 0.001 1 Disposals 8.946e-02 3.549e+03 0.000 1 Marks 1.427e-01 1.938e+03 0.000 1 Goals -2.288e+01 3.082e+05 0.000 1 Behinds -7.034e+00 5.482e+04 0.000 1 Hitouts 3.640e-02 5.167e+03 0.000 1 Tackles 8.939e-01 7.075e+03 0.000 1 Rebound50s -2.064e-01 8.497e+03 0.000 1 Inside50s 5.645e-01 8.133e+03 0.000 1 Clearances -1.930e-01 1.525e+04 0.000 1 Clangers -2.040e-01 1.056e+04 0.000 1 FreesFor -7.699e-01 1.762e+04 0.000 1 ContendedPossessions -5.752e-01 7.424e+03 0.000 1 ContestedMarks -1.869e+00 1.069e+04 0.000 1 MarksInside50 6.742e-01 1.676e+04 0.000 1 OnePercenters 1.616e-01 6.888e+03 0.000 1 Bounces -8.763e-01 7.669e+03 0.000 1 GoalAssists 7.570e-01 3.299e+04 0.000 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 1.2540e+02 on 91 degrees of freedom Residual deviance: 7.1154e-09 on 72 degrees of freedom AIC: 40 Number of Fisher Scoring iterations: 25