使用 glmmLasso 获取所有包含参数的 p 值 [英] Getting p-values for all included parameters using glmmLasso
问题描述
我正在使用以下命令在 R 中使用 glmmLasso 拟合混合模型:
I am fitting a mixed model using glmmLasso in R using the command:
glmmLasso(fix = Activity ~ Novelty + Valence + ROI + Novelty:Valence +
Novelty:ROI + Valence:ROI + Novelty:Valence:ROI, rnd = list(Subject = ~1),
data = KNov, lambda = 195, switch.NR = F, final.re = TRUE)
为了让您了解数据，head(KNov) 的输出是:
To give you a sense of the data, the output of head(KNov) is:
Subject Activity ROI Novelty Valence Side STAIt
1 202 -0.4312944 H N E L -0.2993321
2 202 -0.6742497 H N N L -0.2993321
3 202 -1.0914216 H R E L -0.2993321
4 202 -0.6296091 H R N L -0.2993321
5 202 -0.6023507 H N E R -0.2993321
6 202 -1.1554196 H N N R -0.2993321
(我使用 KNov$Subject <- factor(KNov$Subject) 将主题读作分类变量)
(I used KNov$Subject <- factor(KNov$Subject) to have Subject read as a categorical variable)
活动是大脑活动的衡量标准，新颖性和效价是分类变量，用于编码用于引发反应的刺激类型，而投资回报率是编码我们从中采样此活动的大脑三个区域的分类变量.主题是从中采样数据的个人的 ID 号 (n=94).
Activity is a measure of brain activity, Novelty and Valence are categorical variables coding the type of stimulus used to elicit the response and ROI is a categorical variable coding three regions of the brain that we have sampled this activity from. Subject is an ID number for the individuals the data was sampled from (n=94).
glmmLasso 的输出是:
The output for glmmLasso is:
Fixed Effects:
Coefficients:
Estimate StdErr z.value p.value
(Intercept) 0.232193 0.066398 3.4970 0.0004705 ***
NoveltyR -0.190878 0.042333 -4.5089 6.516e-06 ***
ValenceN -0.164214 NA NA NA
ROIB 0.000000 NA NA NA
ROIH 0.000000 NA NA NA
NoveltyR:ValenceN 0.064523 0.077290 0.8348 0.4038189
NoveltyR:ROIB 0.000000 NA NA NA
NoveltyR:ROIH 0.000000 NA NA NA
ValenceN:ROIB -0.424670 0.069561 -6.1050 1.028e-09 ***
ValenceN:ROIH 0.000000 NA NA NA
NoveltyR:ValenceN:ROIB 0.000000 NA NA NA
NoveltyR:ValenceN:ROIH 0.000000 NA NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Random Effects:
StdDev:
Subject
Subject 0.6069078
我想获得价效应的 p 值.我的第一个想法是不包括效价的 p 值，因为它不显着，只包括在模型中，因为它是重要的 ValenceR:ROIB 相互作用的一部分，但是 NoveltyR:ValenceN 也不显着，但是为此给出了 p 值.我想要化合价的 p 值，即使它不显着，因为此分析将用于论文，而且我更喜欢报告实际 p 值而不是 p>.05.
I would like to get a p-value for the effect of valence. My first thought was that the p-value for valence was not included because it was non-significant and only included in the model because it is part of the significant ValenceR:ROIB interaction, however NoveltyR:ValenceN was also non-significant, but a p-value is given for that. I would like a p-value for valence even if it is non-significant, as this analysis is going to be used for a paper, and I prefer to report actual p-values rather than p>.05.
推荐答案
这里的问题很可能是由于降低了预测变量的等级集"，即您有很多组合，其中要么没有条目，要么有些较小的条目子集足以明确地预测其余的值.我建议您运行此代码并注意您得到零个单元格.
The problem here is most likely due to a "reduced rank set of predictors", i.e you have a lot of combinations where there are either no entries or where some smaller subset of entries is sufficient to unambiguously precits the rest of the values,. I suggest you run this code and notice that you get zero cells.
with(KNov, table( Novelty ,
Valence,
ROI ,
interaction(Novelty, Valence) )
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