我在R中有一个线性混合回归输出，它重复两个交互的虚拟变量两次。重复的变量是FranceDummy0:Dummy2008_2009。如果有人能阐明为什么会这样，我将不胜感激。
输出：
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: ExcessReturn ~ FranceDummy + FranceDummy:Dummy2008_2009 + FranceDummy:Dummy2010Onwards +
NetherlandsDummy + NetherlandsDummy:Dummy2008_2009 + NetherlandsDummy:Dummy2009Onwards +
OpenEnd + OpenEnd:Dummy2008_2009 + OpenEnd:Dummy2010Onwards +
ValueAddedDummy + ValueAddedDummy:Dummy2008_2009 + ValueAddedDummy:Dummy2010Onwards +
scale(BrentCrude) + scale(ExcessStock) + scale(RealGDP) +
scale(CPIGrowth) + scale(M1MoneySupply) + scale(InflationSurprise) +
scale(CPIGrowth):scale(InflationSurprise) + scale(RealGDP):scale(CPIGrowth) +
scale(CreditSpread) + scale(X10YearInterestRate) + scale(ExcessStock):NetherlandsDummy +
scale(GearingLag) + scale(I(GearingLag^2)) + scale(I(log(GAV +
1)^2)) + scale(log(GAV + 1)) + scale(Age) + scale(I(Age^2)) + (1 | FundID)
Data: panelsingle
REML criterion at convergence: -2853.8
Scaled residuals:
Min 1Q Median 3Q Max
-5.9453 -0.3983 -0.0174 0.3878 12.2966
Random effects:
Groups Name Variance Std.Dev.
FundID (Intercept) 0.002041 0.04517
Residual 0.015097 0.12287
Number of obs: 2427, groups: FundID, 302
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -8.483e-03 1.497e-02 8.990e+02 -0.567 0.571110
FranceDummy1 2.230e-02 2.707e-02 1.098e+03 0.824 0.410228
NetherlandsDummy1 6.473e-03 2.078e-02 7.544e+02 0.312 0.755456
OpenEnd1 -3.256e-03 1.576e-02 8.381e+02 -0.207 0.836425
ValueAddedDummy1 3.754e-02 1.722e-02 8.801e+02 2.180 0.029534 *
scale(BrentCrude) -1.694e-02 3.569e-03 2.324e+03 -4.746 2.21e-06 ***
scale(ExcessStock) 4.956e-03 3.906e-03 2.263e+03 1.269 0.204670
scale(RealGDP) 2.400e-02 5.379e-03 2.394e+03 4.462 8.51e-06 ***
scale(CPIGrowth) -3.309e-03 3.308e-03 2.366e+03 -1.000 0.317223
scale(M1MoneySupply) 4.952e-03 3.550e-03 2.267e+03 1.395 0.163162
scale(InflationSurprise) -2.312e-03 3.931e-03 2.199e+03 -0.588 0.556505
scale(CreditSpread) -2.743e-02 4.284e-03 2.156e+03 -6.402 1.88e-10 ***
scale(X10YearInterestRate) -2.692e-03 3.415e-03 2.191e+03 -0.788 0.430605
scale(GearingLag) 1.031e-01 1.081e-02 6.807e+02 9.534 < 2e-16 ***
scale(I(GearingLag^2)) -1.378e-01 1.017e-02 9.595e+02 -13.556 < 2e-16 ***
scale(I(log(GAV + 1)^2)) 4.450e-02 1.336e-02 6.915e+02 3.332 0.000908 ***
scale(log(GAV + 1)) -3.529e-02 1.269e-02 9.151e+02 -2.782 0.005518 **
scale(Age) -4.478e-02 9.516e-03 8.289e+02 -4.705 2.97e-06 ***
scale(I(Age^2)) 3.009e-02 9.518e-03 5.839e+02 3.161 0.001653 **
FranceDummy0:Dummy2008_20091 -7.214e-02 2.723e-02 2.313e+03 -2.649 0.008132 **
FranceDummy1:Dummy2008_20091 -1.028e-01 3.826e-02 2.295e+03 -2.687 0.007260 **
FranceDummy0:Dummy2010Onwards1 -7.420e-03 3.103e-02 2.377e+03 -0.239 0.811016
FranceDummy1:Dummy2010Onwards1 -2.861e-02 3.922e-02 2.393e+03 -0.729 0.465888
Dummy2008_20091:NetherlandsDummy1 -3.382e-02 2.788e-02 2.230e+03 -1.213 0.225193
NetherlandsDummy0:Dummy2009Onwards1 4.450e-02 2.783e-02 2.273e+03 1.599 0.109998
NetherlandsDummy1:Dummy2009Onwards1 3.336e-02 3.427e-02 2.367e+03 0.974 0.330361
Dummy2008_20091:OpenEnd1 5.475e-02 2.122e-02 2.261e+03 2.580 0.009935 **
Dummy2010Onwards1:OpenEnd1 2.171e-02 1.643e-02 2.233e+03 1.321 0.186532
Dummy2008_20091:ValueAddedDummy1 -5.917e-02 2.287e-02 2.253e+03 -2.587 0.009735 **
Dummy2010Onwards1:ValueAddedDummy1 -4.683e-02 1.787e-02 2.323e+03 -2.620 0.008839 **
scale(CPIGrowth):scale(InflationSurprise) -5.429e-05 3.242e-03 2.191e+03 -0.017 0.986639
scale(RealGDP):scale(CPIGrowth) 2.056e-02 4.768e-03 2.239e+03 4.313 1.68e-05 ***
NetherlandsDummy1:scale(ExcessStock) -1.955e-02 8.016e-03 2.195e+03 -2.438 0.014831 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
答案 0 :(得分：1)
如果我很好地理解了您的问题，那么以下示例可以重现您的情况：
# create a normaly distributed random variable and 3 factors with 2 levels : 0 and 1
a = rnorm(3, n = 100)
b = rbinom(100, 1, 0.2)
c = rbinom(100, 1, 0.7)
d = rbinom(100, 1, 0.5)
df <- data.frame(a, b=as.factor(b), c=as.factor(c), d=as.factor(d)) # create a dataframe
mod <- lm(a ~ b + b:c + b:d, data = df) # fit linear regression model
summary(mod)
# Call:
# lm(formula = a ~ b + b:c + b:d, data = df)
#
# Residuals:
# Min 1Q Median 3Q Max
# -2.3638 -0.7926 0.1332 0.7963 2.5876
#
# Coefficients:
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 2.69310 0.27043 9.959 2.25e-16 ***
# b1 -0.10971 0.54447 -0.202 0.841
# b0:c1 0.05097 0.28810 0.177 0.860
# b1:c1 0.69217 0.55119 1.256 0.212
# b0:d1 0.10487 0.27033 0.388 0.699
# b1:d1 0.06647 0.52216 0.127 0.899
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
# Residual standard error: 1.186 on 94 degrees of freedom
# Multiple R-squared: 0.03121, Adjusted R-squared: -0.02032
# F-statistic: 0.6057 on 5 and 94 DF, p-value: 0.6957
变量b具有两个级别：0和1。因此，在summary（）输出中，您可以看到相对于“ b0”级别定义的“ b1”估计值。交互是相同的：您可以估算“ b0：c1”交互，相对于“ b0：c0”定义，“ b1：c1”相对于“ b1：c0”定义，依此类推。如G5W所述，没有任何值在这里重复。
要查看所有交互级别的估算，例如，可以使用“效果”包中的allEffects（）函数：
allEffects(mod)
# model: a ~ b + b:c + b:d
#
# b*c effect
# c
# b 0 1
# 0 2.693097 2.744071
# 1 2.583384 3.275555
#
# b*d effect
# d
# b 0 1
# 0 2.693097 2.797964
# 1 2.583384 2.649854