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在 MuMIn::dedge 的结果中获取“+”号

[英]Getting "+" sign in the results of MuMIn :: dredge

I am trying to MuMIn::dredge linear mixed-effect models lme4::lmer with categorical/continuous variables, the code is as follows:我正在尝试使用分类/连续变量 MuMIn::dredge 线性混合效应模型 lme4::lmer,代码如下:

# Selection of variables of interest
sig<-c("Age", "Sex", "BMI", "(1|HID)", "h_age", "h", "h_g", "smk_hs")
# Model formula
formula<-paste0("log10_PBA_N", "~", paste0(c(sig), collapse="+"))
# Global model
model<-lmer(formula, data=data)
# Dredging
DRG<-dredge(global.model=model)

The code runs fine (I guess), but in the results, I have this:代码运行良好(我猜),但在结果中,我有这个:

 Global model call: lmer(formula = formula, data = data)
---
Model selection table 
      (Int)      Age       BMI h h_age h_g Sex smk_hs df   logLik  AICc  delta weight
2   -0.2363 -0.01421                                   4 -332.476 673.0   0.00  0.847
66  -0.2461 -0.01420                                +  5 -333.689 677.5   4.47  0.090
34  -0.2406 -0.01417                         +         5 -334.508 679.2   6.11  0.040
4   -0.3348 -0.01598  0.007096                         5 -335.935 682.0   8.96  0.010
18  -0.1553 -0.01421                     +             7 -334.310 682.9   9.84  0.006
98  -0.2493 -0.01416                         +      +  6 -335.723 683.6  10.60  0.004
68  -0.3463 -0.01599  0.007206                      +  6 -337.140 686.5  13.43  0.001

Can someone please explain to me, what does the "+" sign mean in the results?有人可以向我解释一下,结果中的“+”号是什么意思?

I recently had the exact same question and was struggling to find an answer.我最近有完全相同的问题,正在努力寻找答案。 However, based on a response to a similar question asked on R Studio Community , I think the answer is simply that a '+' sign means that a given categorical variable term is included as significant in that particular model.但是,根据对R Studio Community 上提出的类似问题的回答,我认为答案很简单,“+”号表示给定的分类变量项在特定的 model 中包含为重要的。

So, looking at your table, the first model only includes the intercept, the second includes the intercept and the smk_hs categorical variable, the third includes the intercept and the Sex variable, etc.所以,看你的表,第一个 model 只包括截距,第二个包括截距和 smk_hs 分类变量,第三个包括截距和 Sex 变量等。

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