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？

发布于 2025-01-10 20:48:23 字数 27614 浏览 0 评论 0原文

我正在使用 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像 lmer(Values ~ stimuli + timeperiod + scale(poly(distance.code,3,raw=FALSE))*habitat + Wind.speed + (1|location.code) 这样的模型时，数据=df，REML=FALSE） 然后使用类似于 summary 的内容查看结果，我看到了所有模型固定（和随机）效应，正如我所期望的那样，但是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 ***
---

即使有两个层次的栖息地（森林和草原），这种情况也会发生起初，我认为这可能是因为我的模型有一个交互项，但当我尝试像 lmer(Values ~ stimuli + timeperiod + distance.code +habitat + Wind.speed + (1|location .code), data=ex.df, REML=FALSE)

为什么会说“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”并且“栖息草原”？

快速查看此函数的预期输出：https://rpubs.com/palday/mixed-交互或此处：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 dput 和 subset 来生成它。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！（如果我错误地使用了dput，请告诉我。（对 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, 
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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))

这是适合模型所需的代码（我认为），并在上面的数据已加载：

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，但如果它实际上与统计数据有关，我很乐意将这篇文章删除并在统计堆栈交换中重新发布，

感谢您的帮助。帮助！

原文

I am using the lme4 package and running a linear mixed model but I am confused but the output and expect that I am encountering an error even though I do not get an error message.
The basic issue is when I fit a model like lmer(Values ~ stimuli + timeperiod + scale(poly(distance.code,3,raw=FALSE))*habitat + wind.speed + (1|location.code), data=df, REML=FALSE)
and then look at the results using something like summary I see all the model fixed (and random) effects as I would expect however the habitat effect is always displayed as habitatForest. Like this:

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 ***
---

This is happening even though there are two levels of habitat (Forest and Grassland)
at first, I thought this might be because my model had an interaction term but it happens when I try a simpler model like lmer(Values ~ stimuli + timeperiod + distance.code + habitat + wind.speed + (1|location.code), data=ex.df, REML=FALSE)

Why would it say "habitatForest" and not just "habitat" or if it were going to include a factor by name why not say "habitatForest" and "habitatGrassland"?

A quick look at the expected output from this function here: https://rpubs.com/palday/mixed-interactions or here: https://ase.tufts.edu/bugs/guide/assets/mixed_model_guide.html (among others)
shows that the out put that I am getting is not what is expected or normal.
Other output I have seen simply have factors with two levels, like mine, as a single line (eg habitat).

Here is a portion of the data I am using. I used dput and subseting to produce this. I couldn't figure out how to make the data set smaller and still reproduce the error so I apologize if this is too large. The data set that it comes from is MUCH bigger! (also please let me know if I have used dput incorrectly.(Still new to R and stackoverflow)

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, 
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992L, 993L, 994L, 995L, 996L, 997L, 999L, 1000L, 1001L, 1003L, 
1004L, 1005L, 1007L, 1008L, 1009L, 1011L, 1012L, 1013L, 1015L, 
1016L, 1018L, 1019L, 1020L, 1022L, 1023L, 1025L, 1026L, 1027L, 
1036L, 1037L, 1038L, 1039L, 1040L, 1041L, 1043L, 1044L, 1045L, 
1047L, 1048L, 1049L, 1051L, 1052L, 1053L, 1055L, 1056L, 1057L, 
1059L, 1060L, 1062L, 1063L, 1064L, 1066L, 1067L, 1069L, 1070L, 
1071L, 1080L, 1081L, 1082L, 1083L, 1084L, 1085L, 1087L, 1088L, 
1089L, 1091L, 1092L, 1093L, 1095L, 1096L, 1097L, 1099L, 1100L, 
1101L, 1103L, 1104L, 1106L, 1107L, 1108L, 1110L, 1111L, 1113L, 
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))

Here is the code (I think) that would be needed to fit the model and see the summary after the above data is loaded:

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)

I think this has to do with my data structure and the programming but if it is actually something to do with the stats I am happy to take this post down and re-post over at the stats stackexchange.

Thanks for any help!

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无声无音无过去 2025-01-17 20:48:23

注意：虽然您的问题是关于 lmer() 函数，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，让我们先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() 或 lm() 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 个级别，则需要 n-1 个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，如果栖息地不是森林，则为 0；如果是森林，则为 1。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，那么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。

参考水平的默认设置

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，该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 将成为参考水平，而您将得到 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，如果没有像您似乎所做的那样专门对因子级别进行排序，则默认情况下，Forest 将是参考级别）。

顺便说一句，您在问题中给出的两个链接（来自 Phillip Alday 和 Tufts 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，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recipe，它为您提供了由于recipe（跨所有其级别）和总方差。因此，无论 recipe 有多少级别，这都只是一个比率。

进一步阅读

本文不是对 R 中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：

note: although your question is about the lmer() function, this answer also applies to lm() and other R functions that fit linear models.

The way that coefficient estimates from linear models in R are presented can be confusing. To understand what's going on, you need to understand how R fits linear models when the predictor is a factor variable.

Coefficients on factor variables in R linear models

Before we look at factor variables, let's look at the more straightforward situation where the predictor is continuous. In your example dataset, one of the predictors is wind speed (continuous variable). The estimated coefficient is about -0.35. It's easy to interpret this: averaged across the other predictors, for every increase of 1 km/h in wind speed, your response value is predicted to decrease by 0.35.

But what about if the predictor is a factor? A categorical variable cannot increase or decrease by 1. Instead it can take several discrete values. So what the lmer() or lm() function does by default is automatically code your factor variable as a set of so-called "dummy variables." Dummy variables are binary (they can take values of 0 or 1). If the factor variable has n levels, you need n-1 dummy variables to encode it. The reference level or control group acts like an intercept.

In the case of your habitat variable, there are only 2 levels so you have only 1 dummy variable which will be 0 if habitat is not Forest and 1 if it is Forest. Now we can interpret the coefficient estimate of -68.8: the average value of your response is expected to be 68.8 less in forest habitat relative to the reference level of grassland habitat. You don't need a second dummy variable for grassland because you only need to estimate the one coefficient to compare the two habitats.

If you had a third habitat, let's say wetland, there would be a second dummy variable that would be 0 if not wetland and 1 if wetland. The coefficient estimate there would be the expected difference between the value of the response variable in wetland habitat compared to grassland habitat. Grassland will be the reference level for all the coefficients.

Default setting of reference level

Now to directly address your question of why habitatForest is the coefficient name.

Because by default no reference level or control group is specified, the first one in the factor level ordering becomes the reference level to which all other levels are compared. Then the coefficients are named by appending the variable's name to the name of the level being compared to the reference level. Your factor is ordered with grassland first and forest second. So the coefficient is the effect of the habitat being forest habitat, compared to the reference level, which is grassland in this case. If you switched the habitat factor level ordering, Forest would be the reference level and you would get habitatGrassland as the coefficient instead. (Note that default factor level ordering is alphabetical, so without specifically ordering the factor levels as you seem to have done, Forest would be the reference level by default).

Incidentally, the two links you give in your question (guides to mixed models from Phillip Alday and Tufts) do in fact have the same kind of output as you are getting. For example in Alday's tutorial, the factor recipe has 3 levels: A, B, and C. There are two coefficients in the fixed effects summary, recipeB and recipeC, just as you would expect from dummy coding using A as reference level. You may be confusing the fixed effects summary with the ANOVA table presented elsewhere in his post. The ANOVA table does only have a single line for recipe which gives you the ratio of variance due to recipe (across all its levels) and the total variance. So that would only be one ratio regardless of how many levels recipe has.

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？

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R 线性模型中因子变量的系数

参考水平的默认设置

进一步阅读

Coefficients on factor variables in R linear models

Default setting of reference level

Further reading

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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？

如果你对这篇内容有疑问，欢迎到本站社区发帖提问 参与讨论，获取更多帮助，或者扫码二维码加入 Web 技术交流群。

发布评论

评论（1）

R 线性模型中因子变量的系数

参考水平的默认设置

进一步阅读

Coefficients on factor variables in R linear models

Default setting of reference level

Further reading

关于作者

相关话题

热门标签

推荐作者

佚名

羁客

天天爱笑的徐老师

星

夏日落

隐诗

友情链接

如果你对这篇内容有疑问，欢迎到本站社区发帖提问参与讨论，获取更多帮助，或者扫码二维码加入 Web 技术交流群。