问题描述
(更新) 嘿堆栈溢出者,
我正在尝试使用 R 的 clogit
函数运行一系列 MLM 固定效应逻辑回归。当我向我的模型添加额外的协变量时,摘要输出显示 NA。但是,当我使用 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),method = 'exact')
summary(model2)
> summary(model2)
Call:
coxph(formula = Surv(rep(1,chldwork) ~ lag_aspgrade_binned +
age + sex + chldeth + typesite + selfwlth + enroll + strata(childid),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 函数将所有模型并排显示时会发生什么。请注意,通过 chldeth99 的系数性不在模型一中。
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
非常感谢您提供的任何见解。祝你在新的一年结束时一切顺利——特别感谢那些目前在学校仍在努力学习的人。
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