Revisions to Multiple Nested Random Effects affecting a Mixed Model

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edited Aug 14, 2019 at 10:03

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Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: electrode_response ~ TrialRep + (TrialRep | SubjectID) + (1 | CoupleID/SubjectID) + (1 | Block/Trial/TrialRep)
   Data: finaldb
Control: lmerControl(optimizer = "optimx", optCtrl = list(method = "nlminb"))

REML criterion at convergence: 80726

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-5.3489 -0.5861  0.0203  0.6204  6.7150 

Random effects:
 Groups                 Name        Variance  Std.Dev. Corr 
 TrialRep:(Trial:Block) (Intercept)   0.82127  0.9062       
 Trial:Block            (Intercept)   0.05282  0.2298       
 SubjectID:CoupleID     (Intercept)   9.21819  3.0361       
 SubjectID              (Intercept)  19.82985  4.4531       
 TrialRep                         TrialRep    3.16028  1.7777  -1.00
 CoupleID               (Intercept)   2.03290  1.4258       
 Block                  (Intercept)   0.08479  0.2912       
 Residual                           100.53917 10.0269       
Number of obs: 10800, groups:  TrialRep:(Trial:Block), 300; Trial:Block, 100; SubjectID:CoupleID, 36; SubjectID, 36; CoupleID, 18; Block, 4

Fixed effects:
            Estimate Std. Error      df t value Pr(>|t|)    
(Intercept)   8.5483     0.9857 24.1529   8.672 6.98e-09 ***
TrialRep     -1.8157     0.3254 37.6691  -5.581 2.20e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
         (Intr)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: electrode_response ~ TrialRep + (TrialRep | SubjectID) + (1 | CoupleID/SubjectID) + (1 | Block/Trial/TrialRep)
   Data: finaldb
Control: lmerControl(optimizer = "optimx", optCtrl = list(method = "nlminb"))

REML criterion at convergence: 80726

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-5.3489 -0.5861  0.0203  0.6204  6.7150 

Random effects:
 Groups                 Name        Variance  Std.Dev. Corr 
 TrialRep:(Trial:Block) (Intercept)   0.82127  0.9062       
 Trial:Block            (Intercept)   0.05282  0.2298       
 SubjectID:CoupleID     (Intercept)   9.21819  3.0361       
 SubjectID              (Intercept)  19.82985  4.4531       
 TrialRep                             3.16028  1.7777  -1.00
 CoupleID               (Intercept)   2.03290  1.4258       
 Block                  (Intercept)   0.08479  0.2912       
 Residual                           100.53917 10.0269       
Number of obs: 10800, groups:  TrialRep:(Trial:Block), 300; Trial:Block, 100; SubjectID:CoupleID, 36; SubjectID, 36; CoupleID, 18; Block, 4

Fixed effects:
            Estimate Std. Error      df t value Pr(>|t|)    
(Intercept)   8.5483     0.9857 24.1529   8.672 6.98e-09 ***
TrialRep     -1.8157     0.3254 37.6691  -5.581 2.20e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
         (Intr)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: electrode_response ~ TrialRep + (TrialRep | SubjectID) + (1 | CoupleID/SubjectID) + (1 | Block/Trial/TrialRep)
   Data: finaldb
Control: lmerControl(optimizer = "optimx", optCtrl = list(method = "nlminb"))

REML criterion at convergence: 80726

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-5.3489 -0.5861  0.0203  0.6204  6.7150 

Random effects:
 Groups                 Name        Variance  Std.Dev. Corr 
 TrialRep:(Trial:Block) (Intercept)   0.82127  0.9062       
 Trial:Block            (Intercept)   0.05282  0.2298       
 SubjectID:CoupleID     (Intercept)   9.21819  3.0361       
 SubjectID              (Intercept)  19.82985  4.4531       
                          TrialRep    3.16028  1.7777  -1.00
 CoupleID               (Intercept)   2.03290  1.4258       
 Block                  (Intercept)   0.08479  0.2912       
 Residual                           100.53917 10.0269       
Number of obs: 10800, groups:  TrialRep:(Trial:Block), 300; Trial:Block, 100; SubjectID:CoupleID, 36; SubjectID, 36; CoupleID, 18; Block, 4

Fixed effects:
            Estimate Std. Error      df t value Pr(>|t|)    
(Intercept)   8.5483     0.9857 24.1529   8.672 6.98e-09 ***
TrialRep     -1.8157     0.3254 37.6691  -5.581 2.20e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
         (Intr)

deleted 2 characters in body

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edited Aug 14, 2019 at 7:56

Unai Vicente

115
6

(1|CoupleID/SubjectID) <- Regarding the nesting of the subjects
(1|Block/Trial/TrialRep) <- Regarding the at what point in the experiment was the response data collected

As you can see I'm using an optimizer, but even with this I often, in the data collected in some electrodes, I can find the subsequent warnings, even though I use the same nesting structure in the data:

(1|CoupleID/SubjectID) <- Regarding the nesting of the subjects
(1|Block/Trial/TrialRep) <- Regarding the at what point in the experiment was the response data collected

As you can see I'm using an optimizer, but even with this I often, in the data collected in some electrodes, I can find the subsequent warnings, even though I use the same nesting structure in the data:

(1|CoupleID/SubjectID) <- Regarding the nesting of the subjects
(1|Block/Trial/TrialRep) <- Regarding at what point in the experiment was the response data collected

As you can see I'm using an optimizer, but even with this I often, in the data collected in some electrodes, can find the subsequent warnings, even though I use the same nesting structure in the data:

added 27 characters in body

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edited Aug 14, 2019 at 7:50

Unai Vicente

115
6

(1|CoupleID/SubjectID) <- Regarding the nesting of the subjects
(1|Block/Trial/TrialRepetitionTrialRep) <- Regarding the at what point in the experiment was the response data collected

lmer(electrode_response~TrialRep+(trial_rep|SubjectIDTrialRep |SubjectID)+(1|CoupleID/SubjectID)+(1|Block/Trial/TrialRep)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: electrode_response ~ TrialRep + (TrialRep | SubjectID) + (1 | CoupleID/SubjectID) + (1 | Block/Trial/TrialRep)
   Data: finaldb
Control: lmerControl(optimizer = "optimx", optCtrl = list(method = "nlminb"))

REML criterion at convergence: 80726

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-5.3489 -0.5861  0.0203  0.6204  6.7150 

Random effects:
 Groups                 Name        Variance  Std.Dev. Corr 
 TrialRep:(Trial:Block) (Intercept)   0.82127  0.9062       
 Trial:Block            (Intercept)   0.05282  0.2298       
 AbsSujSubjectID:ID         CoupleID     (Intercept)   9.21819  3.0361       
 AbsSuj   SubjectID              (Intercept)  19.82985  4.4531       
 TrialRep                       TrialRep      3.16028  1.7777  -1.00
 ID      CoupleID               (Intercept)   2.03290  1.4258       
 Block                  (Intercept)   0.08479  0.2912       
 Residual                           100.53917 10.0269       
Number of obs: 10800, groups:  TrialRep:(Trial:Block), 300; Trial:Block, 100; AbsSujSubjectID:IDCoupleID, 36; AbsSujSubjectID, 36; IDCoupleID, 18; Block, 4

Fixed effects:
            Estimate Std. Error      df t value Pr(>|t|)    
(Intercept)   8.5483     0.9857 24.1529   8.672 6.98e-09 ***
TrialRep     -1.8157     0.3254 37.6691  -5.581 2.20e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
         (Intr)

> r.squaredGLMM(lm500Pzelectrode_response)
            R2m       R2c
[1,] 0.01769641 0.1905829

(1|CoupleID/SubjectID) <- Regarding the nesting of the subjects
(1|Block/Trial/TrialRepetition) <- Regarding the at what point in the experiment was the response data collected

lmer(electrode_response~TrialRep+(trial_rep|SubjectID)+(1|CoupleID/SubjectID)+(1|Block/Trial/TrialRep)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: electrode_response ~ TrialRep + (TrialRep | SubjectID) + (1 | CoupleID/SubjectID) + (1 | Block/Trial/TrialRep)
   Data: finaldb
Control: lmerControl(optimizer = "optimx", optCtrl = list(method = "nlminb"))

REML criterion at convergence: 80726

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-5.3489 -0.5861  0.0203  0.6204  6.7150 

Random effects:
 Groups                 Name        Variance  Std.Dev. Corr 
 TrialRep:(Trial:Block) (Intercept)   0.82127  0.9062       
 Trial:Block            (Intercept)   0.05282  0.2298       
 AbsSuj:ID              (Intercept)   9.21819  3.0361       
 AbsSuj                 (Intercept)  19.82985  4.4531       
                        TrialRep      3.16028  1.7777  -1.00
 ID                     (Intercept)   2.03290  1.4258       
 Block                  (Intercept)   0.08479  0.2912       
 Residual                           100.53917 10.0269       
Number of obs: 10800, groups:  TrialRep:(Trial:Block), 300; Trial:Block, 100; AbsSuj:ID, 36; AbsSuj, 36; ID, 18; Block, 4

Fixed effects:
            Estimate Std. Error      df t value Pr(>|t|)    
(Intercept)   8.5483     0.9857 24.1529   8.672 6.98e-09 ***
TrialRep     -1.8157     0.3254 37.6691  -5.581 2.20e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
         (Intr)

> r.squaredGLMM(lm500Pz)
            R2m       R2c
[1,] 0.01769641 0.1905829

(1|CoupleID/SubjectID) <- Regarding the nesting of the subjects
(1|Block/Trial/TrialRep) <- Regarding the at what point in the experiment was the response data collected

lmer(electrode_response~TrialRep+(TrialRep |SubjectID)+(1|CoupleID/SubjectID)+(1|Block/Trial/TrialRep)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: electrode_response ~ TrialRep + (TrialRep | SubjectID) + (1 | CoupleID/SubjectID) + (1 | Block/Trial/TrialRep)
   Data: finaldb
Control: lmerControl(optimizer = "optimx", optCtrl = list(method = "nlminb"))

REML criterion at convergence: 80726

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-5.3489 -0.5861  0.0203  0.6204  6.7150 

Random effects:
 Groups                 Name        Variance  Std.Dev. Corr 
 TrialRep:(Trial:Block) (Intercept)   0.82127  0.9062       
 Trial:Block            (Intercept)   0.05282  0.2298       
 SubjectID:CoupleID     (Intercept)   9.21819  3.0361       
 SubjectID              (Intercept)  19.82985  4.4531       
 TrialRep                             3.16028  1.7777  -1.00
 CoupleID               (Intercept)   2.03290  1.4258       
 Block                  (Intercept)   0.08479  0.2912       
 Residual                           100.53917 10.0269       
Number of obs: 10800, groups:  TrialRep:(Trial:Block), 300; Trial:Block, 100; SubjectID:CoupleID, 36; SubjectID, 36; CoupleID, 18; Block, 4

Fixed effects:
            Estimate Std. Error      df t value Pr(>|t|)    
(Intercept)   8.5483     0.9857 24.1529   8.672 6.98e-09 ***
TrialRep     -1.8157     0.3254 37.6691  -5.581 2.20e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
         (Intr)

> r.squaredGLMM(electrode_response)
            R2m       R2c
[1,] 0.01769641 0.1905829

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edited Aug 13, 2019 at 12:03

Dimitris Rizopoulos

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asked Aug 13, 2019 at 11:30

Unai Vicente

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