Getting p-values for all included parameters using glmmLasso

问题

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)

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

(I used KNov$Subject <- factor(KNov$Subject) to have Subject read as a categorical variable)

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).

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

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.

回答1:

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) )

来源：https://stackoverflow.com/questions/38132830/getting-p-values-for-all-included-parameters-using-glmmlasso

标签

mixed-models