为什么使用 lme4 的线性混合模型的输出显示一个因子水平而不是另一个水平？

Question

为什么使用 lme4 的线性混合模型的输出显示一个因子水平而不是另一个水平？

我正在使用该lme4包并运行线性混合模型，但我很困惑，但输出并期望我遇到错误，即使我没有收到错误消息。基本问题是，当我拟合这样的模型lmer(Values ~ stimuli + timeperiod + scale(poly(distance.code,3,raw=FALSE))*habitat + wind.speed + (1|location.code), data=df, REML=FALSE) ，然后使用类似的东西查看结果时，summary我看到了所有模型固定（和随机）效应，正如我所期望的那样，但栖息地效应始终显示为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 ***
---

Run Code Online (Sandbox Code Playgroud)

即使一开始有两个层次的栖息地（森林和草原），这种情况也会发生，我认为这可能是因为我的模型有一个交互项，但当我尝试一个更简单的模型时，就会发生这种情况lmer(Values ~ stimuli + timeperiod + distance.code + habitat + wind.speed + (1|location.code), data=ex.df, REML=FALSE)

为什么它会说“habitatForest”而不仅仅是“habitat”，或者如果它要包含一个名称因素，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或此处： https: //ase.tufts.edu/bugs/guide/assets/mixed_model_guide.html（等等））表明我得到的输出不是预期的或正常的。我见过的其他输出只是有两个级别的因素，就像我的一样，作为一条线（例如栖息地）。

这是我正在使用的部分数据。我使用dputand subseting 来生成这个。我无法弄清楚如何使数据集更小并仍然重现错误，因此如果数据集太大，我深表歉意。它来自的数据集要大得多！（如果我使用dput不正确，请告诉我。（对于 R 和 stackoverflow 来说还是新手）

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, 
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, 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, 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), timeperiod = c(6L, 6L, 6L, 
6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 
9L, 9L, 9L, 9L, 9L, 11L, 11L, 11L, 11L, 11L, 11L, 13L, 13L, 13L, 
13L, 13L, 13L, 15L, 15L, 15L, 15L, 15L, 15L, 17L, 17L, 17L, 17L, 
17L, 17L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 
20L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L, 
23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 11L, 
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
13L, 13L, 13L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
15L, 15L, 15L, 15L, 15L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 
17L, 17L, 17L, 17L, 17L, 17L, 17L, 19L, 19L, 19L, 19L, 19L, 19L, 
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 
22L, 22L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 
23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 
24L, 24L, 24L, 24L, 24L, 24L), Values = c(910.721895276374, 922.652711611841, 
926.219785713456, 1030.28919690464, 1121.98321368732, 992.741416151102, 
910.878353926705, 920.201901019659, 922.134996121665, 992.059286431433, 
1042.05240231832, 1018.99804250179, 911.976009884021, 918.215389274037, 
931.037495260958, 981.032280455129, 983.700699744073, 989.716307418049, 
911.476759038955, 918.554393750162, 920.391856289719, 994.583211567691, 
1006.58290843226, 1005.52479816571, 908.665064025178, 917.940176257067, 
922.746174825048, 986.419049170517, 1042.41789735969, 1082.89658057517, 
916.02310296116, 918.254868924698, 931.01648294424, 982.154409713674, 
1008.54477137219, 996.577798511801, 912.914857937818, 916.937508116615, 
920.933077377339, 997.669828575817, 1007.44452218386, 1151.25894192961, 
909.463528658898, 915.293665875472, 921.917039784441, 983.866984633392, 
1002.04551764872, 986.791628665069, 907.695668282537, 917.845214744473, 
932.330755620455, 972.609449456089, 1155.55960936774, 1083.40557091613, 
909.903267624225, 914.846316952797, 921.279328283221, 1000.3672969178, 
1021.78461788922, 1011.40975353271, 915.037273600535, 914.099859036178, 
924.116937361394, 994.428182266452, 1123.09745015276, 1004.1485272116, 
914.431649376896, 915.27037594587, 929.411251949862, 974.273124973661, 
1145.99211507205, 1013.58184367388, 913.467056616881, 920.213007520924, 
919.794369158301, 983.816025282468, 1103.11322201674, 974.792027063404, 
910.532609655114, 917.616832229923, 923.462599912213, 1015.24811721269, 
1070.61183211249, 1016.57332551186, 910.196695694198, 923.403802532832, 
905.400995326023, 1036.98011238981, 963.147077473505, 916.899569521736, 
931.240844862156, 919.11781354823, 995.408916523572, 960.825305234446, 
1026.22960551445, 1000.13773127026, 962.347584090332, 904.090295814044, 
908.836747102913, 928.867625382891, 918.100799763641, 906.282906701285, 
913.146312873635, 977.094140033575, 972.599778534534, 964.658406857446, 
921.91272768213, 910.507770576621, 942.269786765654, 1014.34022271036, 
1128.29327664605, 1043.1365958913, 919.185972424773, 925.486310755197, 
908.769520270226, 1030.20866627018, 956.104935565803, 922.01947330213, 
934.451182538208, 928.626906337293, 986.326936258622, 1003.40797963907, 
1021.91264348048, 995.68658929192, 993.102343807935, 901.633626404701, 
908.255562868123, 922.840049924103, 917.012733437446, 907.541530752433, 
915.050696506642, 983.542956895186, 972.236377246083, 965.082329354352, 
918.337944633569, 910.137012141557, 952.89462134025, 977.420371016686, 
1154.17994731565, 1022.82998099991, 927.061613377597, 926.745527716988, 
908.284054932259, 966.157586219165, 974.986841619676, 916.559494755925, 
935.817296050643, 918.835719171662, 1023.62078549133, 1009.23121097376, 
1005.81651905991, 981.715747809821, 953.127134375762, 902.809201411559, 
907.462229880533, 921.595454423298, 919.198277947855, 904.969515265664, 
913.438353334218, 974.889830301362, 970.58615968713, 963.029605541189, 
915.889893279581, 908.147726780027, 942.742415528349, 979.939535179807, 
1153.51966568673, 1020.93502990084, 916.246150801212, 936.016759720656, 
914.4488779132, 962.397352323664, 986.957848140285, 985.364195731404, 
932.548910038465, 917.363220594089, 1085.89850605988, 1031.66330597084, 
1005.64983154588, 991.988118229379, 975.384741587994, 902.60240793926, 
907.989086075871, 923.287310593779, 912.878571722023, 904.107623756648, 
905.563259817979, 991.530368160932, 975.190212414434, 965.951810135591, 
915.334621878897, 910.857441830446, 936.093336975328, 972.074491630181, 
1106.77459226532, 993.45400883741, 951.911391767329, 927.688604859773, 
915.194279622847, 971.414103170297, 956.138106650696, 965.458656222347, 
944.097918792458, 947.157460200658, 1029.14870726558, 992.151638322899, 
954.129642526236, 981.48182339388, 968.10870393618, 906.941701681267, 
917.956716926981, 923.05649603805, 934.459432014683, 922.801034508827, 
920.724850575215, 981.478432929603, 1012.67364507927, 966.471299899978, 
912.640460101352, 906.34455384334, 923.738349342148, 970.987788560016, 
1210.42940542072, 975.753397539076, 911.747488522664, 928.34872697947, 
910.852487444859, 982.304620375747, 1028.52794775628, 913.408967803895, 
934.334726415048, 916.354017093653, 1036.08727658415, 974.408618327141, 
1004.71633485176, 995.142763465394, 987.00017276687, 906.86826042139, 
915.355833226192, 930.395950341189, 911.742114273539, 905.725754800821, 
912.194776217353, 979.488696998854, 998.766511802223, 968.436523426865, 
916.299279627464, 907.645161223541, 925.300567936

Answer 1

qdr*_*ead 6

注意：虽然您的问题是关于lmer()函数的，但此答案也适用于lm()和其他适合线性模型的 R 函数。

R 中线性模型的系数估计的呈现方式可能会令人困惑。要了解发生了什么，您需要了解当预测变量是因子变量时 R 如何拟合线性模型。

R 线性模型中因子变量的系数

在研究因子变量之前，让我们看一下预测变量连续的更简单的情况。在您的示例数据集中，预测变量之一是风速（连续变量）。估计系数约为-0.35。这很容易解释：对其他预测变量进行平均，风速每增加 1 km/h，您的响应值预计会减少 0.35。

但如果预测变量是一个因素呢？分类变量不能增加或减少 1。相反，它可以采用多个离散值。因此，lmer()orlm()函数默认执行的操作是自动将因子变量编码为一组所谓的“虚拟变量”。虚拟变量是二进制的（它们可以取值 0 或 1）。如果因子变量有n水平，则需要n-1虚拟变量对其进行编码。参考水平或对照组的作用类似于截距。

就栖息地变量而言，只有 2 个级别，因此只有 1 个虚拟变量，如果栖息地不存在，则为 0；如果栖息地为Forest，则为 1 Forest。现在我们可以解释 -68.8 的系数估计值：相对于草原栖息地的参考水平，森林栖息地的响应平均值预计要低 68.8。您不需要草地的第二个虚拟变量，因为您只需要估计一个系数即可比较两个栖息地。

如果你有第三个栖息地，比如说湿地，就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。该系数估计湿地栖息地与草原栖息地的响应变量值之间存在预期差异。草地将成为所有系数的参考水平。

参考电平默认设置

现在直接解决您的问题：为什么habitatForest是系数名称。

由于默认情况下未指定参考水平或对照组，因此因子水平排序中的第一个水平将成为与所有其他水平进行比较的参考水平。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。您的因素排序为草地第一，森林第二。因此，系数是森林栖息地与参考水平（本例中为草原）相比的影响。如果您切换栖息地因子水平排序，Forest则将是参考水平，您将得到habitatGrassland系数。（请注意，默认因子水平排序是按字母顺序排列的，因此，如果没有像您似乎所做的那样专门对因子水平进行排序，Forest则默认情况下将是参考水平）。

顺便说一句，您在问题中给出的两个链接（菲利普·阿尔戴和塔夫茨的混合模型指南）实际上确实具有与您获得的相同类型的输出。例如，在 Alday 的教程中，该因子recipe有 3 个级别：A、B 和 C。固定效应摘要中有两个系数recipeB和recipeC，正如您对使用 A 作为参考级别的虚拟编码所期望的那样。您可能会将固定效应摘要与他的文章中其他地方提供的方差分析表混淆。方差分析表只有一行，其中给出了由于（在其所有recipe水平上）引起的方差比率和总方差。所以无论有多少级，这都只是一个比率reciperecipe。

进一步阅读

这里不是对 R 线性模型中的对比编码进行全面讨论的地方。我在这里描述的虚拟编码（您也可能会看到称为 one-hot 编码）只是实现此目的的一种方法。这些资源可能会有所帮助：

基本 R 函数的文档contrasts()

来自 UCLA IDRE Stats 的 R 中不同类型分类变量编码的描述

Marissa Barlaz 的 R 对比编码教程

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