我正在使用 glht 函数 (multcomp) 来计算回归后系数线性组合的点估计值和标准误差。回归包括一个四向交互项，并非所有组合都有数据点，因此它们的系数缺失。当我使用 glht 函数时，我收到以下错误消息：
modelparm.default(model, ...) 中的错误：
系数和协方差矩阵的维度不匹配
当我对不包括 NA 系数的回归结果（例如没有 4 路交互）做同样的事情时， glht 命令有效，这让我认为 NA 行是问题所在。我已经在 Stata 中做到了这一点并且它有效，但我不确定 Stata 对这些 NA 行做了什么。
回归
reg4 <- lm(choice_n ~
sex+e*c*r*r1+brit_par+occupation+residency, data = newdata,
na.action=na.omit, weights = Weight)
summary(reg4)
多压缩包
install.packages("multcomp")
library(multcomp)
爱尔兰的线性组合*优秀的英语
summary(glht(reg4, linfct =c("cIreland+eExcellent:cIreland=0")))
这些是回归结果的一部分
Call:
lm(formula = choice_n ~ sex + e * c * r * r1 + brit_par + occupation +
residency, data = innovation_panel, weights = Weight, na.action =
na.exclude)
Weighted Residuals:
Min 1Q Median 3Q Max
-2.0214 -0.3679 0.1630 0.2728 1.1978
Coefficients: (159 not defined because of singularities)
Estimate Std. Error t value
(Intercept) 1.727242 0.028168 61.318
sexWoman -0.002559 0.006775 -0.378
eGood 0.123681 0.036599 3.379
eExcellent 0.073905 0.035809 2.064
cPoland 0.068136 0.040527 1.681
cItaly 0.010660 0.038552 0.277
cIndia 0.083874 0.040283 2.082
cPakistan 0.008349 0.052466 0.159
cNigeria -0.063009 0.051064 -1.234
cIreland 0.080269 0.031548 2.544
cAustralia 0.069785 0.031285 2.231
cSyria -0.025254 0.056821 -0.444
cSomalia 0.018274 0.051287 0.356
rMuslim -0.094982 0.064429 -1.474
rNo religion 0.016348 0.035872 0.456
r1Yes -0.002653 0.066710 -0.040
r1NA NA NA NA
brit_parBritish grandparent -0.034635 0.008292 -4.177
brit_parNeither -0.096933 0.008268 -11.724
occupationDoctor 0.045576 0.014274 3.193
occupationIT professional 0.020612 0.014246 1.447
occupationLanguage teacher -0.008675 0.014467 -0.600
occupationAdmin worker -0.049867 0.014283 -3.491
occupationFarmer -0.066845 0.014500 -4.610
occupationCleaner -0.093354 0.014352 -6.505
occupationUnemployed -0.359872 0.014304 -25.159
occupationStay at home parent -0.170595 0.014394 -11.851
residency6 years 0.023541 0.009569 2.460
residency10 years 0.085133 0.009553 8.912
residency20 years 0.115200 0.009600 12.000
eGood:cPoland -0.104022 0.060583 -1.717
eExcellent:cPoland -0.074505 0.058459 -1.274
eGood:cItaly -0.021625 0.056038 -0.386
eExcellent:cItaly 0.014001 0.056616 0.247
eGood:cIndia -0.085940 0.057982 -1.482
eExcellent:cIndia -0.022007 0.058273 -0.378
eGood:cPakistan -0.063008 0.074928 -0.841
eExcellent:cPakistan 0.024541 0.073136 0.336
eGood:cNigeria -0.016136 0.069657 -0.232
eExcellent:cNigeria 0.071779 0.070881 1.013
eGood:cIreland NA NA NA
eExcellent:cIreland NA NA NA
eGood:cAustralia NA NA NA
eExcellent:cAustralia NA NA NA