r - clogit 输出产生 NA，但当我使用 cbind 时部分显示

Question

（更新）嘿堆栈溢出者，

clogit我正在尝试使用 R 的函数运行一系列 MLM 固定效应逻辑回归。当我向模型添加其他协变量时，摘要输出会显示 NA。但是，当我使用该cbind函数时，会出现一些缺失的协变量系数。

这是我的模型 1 方程和输出：

> model1 <- clogit(chldwork~lag_aspgrade_binned+age+strata(childid), data=finaletdtlag, method = 'exact')
> summary(model1)
Call:
coxph(formula = Surv(rep(1, 2686L), chldwork) ~ lag_aspgrade_binned + 
    age + strata(childid), data = finaletdtlag, method = "exact")

  n= 2686, number of events= 2287 

                                       coef exp(coef) se(coef)       z Pr(>|z|)    
lag_aspgrade_binnedhigh school      1.04156   2.83363  0.52572   1.981  0.04757 *  
lag_aspgrade_binnedno primary       1.31891   3.73935  0.89010   1.482  0.13841    
lag_aspgrade_binnedprimary some hs  0.85000   2.33964  0.56244   1.511  0.13072    
lag_aspgrade_binnedsome college     1.28607   3.61855  0.41733   3.082  0.00206 ** 
age                                -0.39600   0.67301  0.03105 -12.753  < 2e-16 ***

这是我的模型二方程：

model2
<- clogit(chldwork~lag_aspgrade_binned+age+sex+chldeth+typesite+selfwlth+enroll+strata(childid), data=finaletdtlag, method = 'exact')
summary(model2)

这是摘要输出：

> summary(model2)
Call:
coxph(formula = Surv(rep(1, 2686L), chldwork) ~ lag_aspgrade_binned + 
    age + sex + chldeth + typesite + selfwlth + enroll + strata(childid), 
    data = finaletdtlag, method = "efron")

  n= 2675, number of events= 2277 
   (11 observations deleted due to missingness)

                                       coef exp(coef) se(coef)      z Pr(>|z|)    
lag_aspgrade_binnedhigh school      0.32943   1.39018  0.13933  2.364   0.0181 *  
lag_aspgrade_binnedno primary       0.46553   1.59286  0.25154  1.851   0.0642 .  
lag_aspgrade_binnedprimary some hs  0.33477   1.39762  0.15728  2.128   0.0333 *  
lag_aspgrade_binnedsome college     0.36268   1.43718  0.11792  3.076   0.0021 ** 
age                                -0.07638   0.92647  0.01020 -7.486 7.11e-14 ***
sex1                                     NA        NA  0.00000     NA       NA    
chldeth2                                 NA        NA  0.00000     NA       NA    
chldeth3                                 NA        NA  0.00000     NA       NA    
chldeth4                                 NA        NA  0.00000     NA       NA    
chldeth6                                 NA        NA  0.00000     NA       NA    
chldeth7                                 NA        NA  0.00000     NA       NA    
chldeth8                                 NA        NA  0.00000     NA       NA    
chldeth9                                 NA        NA  0.00000     NA       NA    
chldeth99                                NA        NA  0.00000     NA       NA    
typesite1                                NA        NA  0.00000     NA       NA    
selfwlth1                           0.04031   1.04113  0.29201  0.138   0.8902    
selfwlth2                           0.11971   1.12717  0.28736  0.417   0.6770    
selfwlth3                           0.07928   1.08251  0.29189  0.272   0.7859    
selfwlth4                           0.05717   1.05884  0.30231  0.189   0.8500    
selfwlth5                           0.39709   1.48750  0.43653  0.910   0.3630    
selfwlth99                               NA        NA  0.00000     NA       NA    
enroll1                            -0.20443   0.81511  0.08890 -2.300   0.0215 *  
enroll88                                 NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

但是，当我使用 cbind 函数将我的所有模型彼此相邻显示时，会发生这种情况。请注意，通过 chldeth99 的系数 sex 不在模型一中。

cbind结果：

> cbind(coef(model1), (coef(model2)), coef(model3)) #creating side by side list of all model coefficients
                                         [,1]        [,2]        [,3]
lag_aspgrade_binnedhigh school      1.0415583  0.27198991  0.32827106
lag_aspgrade_binnedno primary       1.3189131  0.37986205  0.46103492
lag_aspgrade_binnedprimary some hs  0.8499958  0.27831739  0.33256493
lag_aspgrade_binnedsome college     1.2860726  0.30089261  0.36214068
age                                -0.3960015 -0.06233958 -0.07653464
sex1                                1.0415583          NA          NA
chldeth2                            1.3189131          NA          NA
chldeth3                            0.8499958          NA          NA
chldeth4                            1.2860726          NA          NA
chldeth6                           -0.3960015          NA          NA
chldeth7                            1.0415583          NA          NA
chldeth8                            1.3189131          NA          NA
chldeth9                            0.8499958          NA          NA
chldeth99                           1.2860726          NA          NA
typesite1                          -0.3960015          NA          NA
selfwlth1                           1.0415583  0.03245507  0.04424493
selfwlth2                           1.3189131  0.09775395  0.12743276
selfwlth3                           0.8499958  0.06499650  0.08854499
selfwlth4                           1.2860726  0.05038224  0.07092755
selfwlth5                          -0.3960015  0.32162830  0.38079232
selfwlth99                          1.0415583          NA          NA
enroll1                             1.3189131 -0.16966609 -0.30366842
enroll88                            0.8499958          NA          NA
sex1:enroll1                        1.2860726  0.27198991  0.24088361
sex1:enroll88                      -0.3960015  0.37986205          NA

非常感谢任何见解。祝你们在新的一年结束时一切顺利——特别向那些仍在努力学习的人致敬。

标签： rlogistic-regressioncbind