线性混合模型使用lme4输出为什么会显示一个因子水平而不显示另一个因子水平？

Question

线性混合模型使用lme4输出为什么会显示一个因子水平而不显示另一个因子水平？

3

我正在使用lme4软件包运行线性混合模型，但我对输出结果感到困惑，并且我认为遇到了错误，尽管没有收到错误消息。基本问题是，当我拟合一个类似于lmer(Values ~ stimuli + timeperiod + scale(poly(distance.code,3,raw=FALSE))*habitat + wind.speed + (1|location.code), data=df, REML=FALSE)的模型，然后使用summary之类的东西查看结果时，发现所有的模型固定效应（和随机效应）都按照我预期的显示，但是栖息地效应总是显示为habitatForest。像这样：

Fixed effects:
                                                            Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)                                                996.63179    8.16633   31.22730 122.042  < 2e-16 ***
stimuliBHCO                                                 -3.57541    1.28877 8750.89273  -2.774 0.005544 ** 
stimuliCOHA                                                -10.17037    1.29546 8754.17156  -7.851 4.62e-15 ***
timeperiod                                                   0.19900    0.05516 8744.95307   3.608 0.000310 ***
scale(poly(distance.code, 3, raw = FALSE))1                 -3.87613    0.71431 8745.70773  -5.426 5.90e-08 ***
scale(poly(distance.code, 3, raw = FALSE))2                  2.65854    0.71463 8745.19353   3.720 0.000200 ***
scale(poly(distance.code, 3, raw = FALSE))3                  4.66340    0.72262 8745.67948   6.453 1.15e-10 ***
habitatForest                                              -68.82430   11.83009   29.95226  -5.818 2.34e-06 ***
wind.speed                                                  -0.35853    0.07631 8403.15191  -4.698 2.66e-06 ***
scale(poly(distance.code, 3, raw = FALSE))1:habitatForest    2.89860    1.03891 8745.46534   2.790 0.005282 ** 
scale(poly(distance.code, 3, raw = FALSE))2:habitatForest   -3.49758    1.03829 8745.11371  -3.369 0.000759 ***
scale(poly(distance.code, 3, raw = FALSE))3:habitatForest   -4.67300    1.03913 8745.30579  -4.497 6.98e-06 ***
---

即使有两个生境层次（森林和草原），仍然会出现这种情况。一开始，我以为可能是因为我的模型有一个交互项，但是即使我尝试一个更简单的模型（例如lmer(Values ~ stimuli + timeperiod + distance.code + habitat + wind.speed + (1|location.code), data=ex.df, REML=FALSE)），它也会出现这种情况。

为什么它会说“habitatForest”而不只是“habitat”，或者如果要按名称包含因子，为什么不说“habitatForest”和“habitatGrassland”？

快速查看此函数的预期输出：https://rpubs.com/palday/mixed-interactions 或https://ase.tufts.edu/bugs/guide/assets/mixed_model_guide.html（等等），显示我得到的输出不是预期的或正常的。其他我看到的输出只将像我这样的具有两个水平的因子作为一行列出（例如habitat）。

以下是我使用的数据的一部分。我使用了dput和subset来生成这个数据集。我无法想出如何使数据集更小并且仍然能够重现错误，所以如果数据集太大了，请谅解。它来自的数据集要大得多！（还有请告诉我是否使用了dput不正确（对R和stackoverflow还是新手））。

structure(list(location.code = structure(c(1L, 1L, 1L, 2L, 2L, 
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 
1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 
3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 
1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 
4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 
2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 
4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 
1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 
3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 
1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 
4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 
2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 
4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 
1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 
3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 
1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 
4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 
2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 
4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 
1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 
3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 
1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 
4L, 3L, 3L, 3L, 4L, 4L, 4L), .Label = c("BSF1", "BSG1", "RLF3", 
"RLG3", "CCBSF1", "CCBSG1", "CPF1", "CPF2", "CPG1", "CPG2", "OSG1", 
"OSG2", "RLF4", "RLF5", "RLF1", "RLF2", "RLG1", "RLG2", "BNPF1", 
"BNPG1", "OSG3", "OSF1", "CMG3", "CMF1", "BSG2", "BSG3", "WSF1", 
"WSF2", "HPG1", "HPG2"), class = "factor"), habitat = structure(c(2L, 
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L), .Label = c("Grassland", 
"Forest"), class = "factor"), distance.code = c(0L, 30L, 60L, 
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 
60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 
60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 
60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 
0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 
30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 
60L), stimuli = structure(c(3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 
2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 
3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 
2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 
2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 
3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 
2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 
3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 
1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 
2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 
3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 
1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 
2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 
3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 
1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 
2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 
3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 
1L, 1L, 1L), .Label = c("FOSP", "BHCO", "COHA", "YEWA", "TUTI"
), class = "factor"), wind.speed = c(0.8, 0.8, 0.8, 0.2, 0.2, 
0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 
0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 
0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 
0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 
0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 
0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 
0.2, 65, 65, 65, 65, 65, 55, 55, 55, 50, 50, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 65, 65, 65, 65, 65, 
55, 55, 55, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0.9, 0.9, 0.9, 65, 65, 65, 65, 65, 55, 55, 55, 50, 50, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 65, 
65, 65, 65, 65, 55, 55, 55, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 65, 65, 65, 65, 65, 55, 55, 
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1114L, 1115L, 1124L, 1125L, 1126L, 1127L, 1128L, 1129L, 1131L, 
1132L, 1133L, 1135L, 1136L, 1137L, 1139L, 1140L, 1141L, 1143L, 
1144L, 1145L, 1147L, 1148L, 1150L, 1151L, 1152L, 1154L, 1155L, 
1157L, 1158L, 1159L, 1168L, 1169L, 1170L, 1171L, 1172L, 1173L, 
1175L, 1176L, 1177L, 1179L, 1180L, 1181L, 1183L, 1184L, 1185L, 
1187L, 1188L, 1189L, 1191L, 1192L, 1194L, 1195L, 1196L, 1198L, 
1199L, 1201L, 1202L, 1203L, 1212L, 1213L, 1214L, 1215L, 1216L, 
1217L, 1219L, 1220L, 1221L, 1223L, 1224L, 1225L, 1227L, 1228L, 
1229L))

这里是加载了上述数据后，可能需要用来拟合模型并查看摘要的代码（我认为）：

library(lme4)
library(lmerTest)

fit1 <- lmer(Values ~ stimuli + timeperiod + scale(poly(distance.code,3,raw=FALSE))*habitat + wind.speed + (1|location.code), data=df, REML=FALSE)
fit2 <- lmer(Values ~ stimuli + timeperiod + distance.code + habitat + wind.speed + (1|location.code), data=ex.df, REML=FALSE)

summary(fit1)
#or
summary(fit2)

我认为这与我的数据结构和编程有关，但如果实际上与统计有关，我很乐意将此帖子删除并重新发布到统计堆栈交换。

感谢任何帮助！

- parksnrec1

4

默认情况下，在R中线性模型（无论是lm还是lmer）中的对比使用n-1个“虚拟变量”进行编码。因为默认情况下没有指定控制组，所以因子水平排序中的第一个成为控制。您的因素按草原和森林的顺序排序。因此，系数是指栖息地为森林栖息地时与参考水平（在本例中为草原）相比的影响。如果您交换了栖息地因子级别排序，则会得到habitatGrassland作为系数。 - qdread

2

你只需要n-1个参数来区分n组——因此这里只有一个栖息地系数，因为n=2。 - qdread

4

这是一个可以用来调整模型对比设置的潜在教程，如果您希望尝试一下。链接为：https://marissabarlaz.github.io/portfolio/contrastcoding/。 - qdread

1

@qdread，我查找了重复内容，但似乎没有。您能否将您的评论发布为答案？（另请参见https://dev59.com/_m0NtIcB2Jgan1znOs6s#70459991） - Ben Bolker

@BenBolker 完成了。如果我没有使用正确的术语，欢迎任何编辑。 - qdread

1个回答

网页内容由stack overflow 提供, 点击上面的

可以查看英文原文，
原文链接

- qdread · Accepted Answer

注意：虽然您的问题涉及到lmer()函数，但是这个答案也适用于lm()和其他拟合线性模型的R函数。

R中呈现线性模型系数估计值的方式可能会让人困惑。要理解发生了什么，您需要了解当预测变量为因子变量时R如何拟合线性模型。

R中线性模型中因子变量的系数

在我们看因子变量之前，让我们先来看更简单明了的情况，即预测变量是连续的情况。在您的示例数据集中，其中一个预测变量是风速（连续变量）。估计系数约为-0.35。这很容易解释：在其他预测变量平均值下，每增加1公里/小时的风速，您的响应值预测将减少0.35。

但如果预测因素是一个因素呢？分类变量不能增加或减少1。相反，它可以取几个离散值。所以lmer()或lm()函数默认会自动将您的因素变量编码为一组所谓的“虚拟变量”。虚拟变量是二进制的（它们可以取0或1的值）。如果因素变量有n个级别，则需要n-1个虚拟变量来对其进行编码。参考水平或控制组就像截距。

在您的栖息地变量的情况下，只有2个级别，因此您只有1个虚拟变量，如果栖息地不是Forest则为0，如果是Forest则为1。现在我们可以解释系数估计为-68.8：相对于草原栖息地的参考水平，您的响应的平均值预计要少68.8。您不需要第二个草原虚拟变量，因为您只需要估计一个系数来比较这两个栖息地。

如果你有第三种栖息地，比如湿地，那么会有第二个虚拟变量，如果不是湿地则为0，如果是湿地则为1。在那里的系数估计值将是响应变量在湿地栖息地与草原栖息地之间预期差异的期望值。对于所有系数，草原都将是参考水平。

参考水平的默认设置

现在直接回答你的问题，为什么habitatForest是系数名称。

因为默认情况下没有指定参考水平或对照组，因此因子水平排序中的第一个成为参考水平，其他所有水平都与其进行比较。然后，通过将变量名称附加到与参考水平进行比较的水平的名称来命名系数。你的因子首先是草原，其次是森林。因此，在这种情况下，系数是森林栖息地与参考水平（即草原）相比的效应。如果您交换了栖息地因子级别排序，则Forest将成为参考水平，您将得到habitatGrassland作为系数。（请注意，默认因子级别排序是按字母顺序排列的，因此如果没有明确地对因子级别进行排序，Forest将成为默认的参考水平）。

顺便提一下，你在问题中提供的两个链接（来自Phillip Alday和Tufts的混合模型指南）实际上与你得到的输出具有相同类型。例如，在Alday的教程中，因子recipe有3个级别：A、B和C。在固定效应总结中有两个系数，recipeB和recipeC，正如你所期望的使用A作为参考水平的虚拟编码。你可能会将固定效应总结与他的帖子中其他地方呈现的ANOVA表混淆。ANOVA表仅对recipe有一行，其中给出由recipe（跨越所有其级别）和总方差引起的方差比率。因此，无论recipe有多少级别，这只会是一个比率。

进一步阅读

这不是讨论R中线性模型对比编码的完整场所。我在这里描述的虚拟编码（您也可以称之为one-hot编码）只是一种方法。这些资源可能会有所帮助：