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what does this warnings mean? and what should I do?

2 messages · ronggui, Spencer Graves

ronggui
# 
I use lmer to fit a mixed effect model.It give some warnings.what does this warnings mean? and what should I do?
Generalized linear mixed model fit using PQL 
Formula: qd ~ edu + jiankang + peixun + hunyin + cadcj + age + age2 +      sex + dangyuan + Comp.1 + Comp.2 + trust.cz1 + (trust.cz1 |      commid) 
   Data: individual 
 Family: quasibinomial(logit link)
      AIC      BIC    logLik deviance
 736.7059 821.8267 -349.3529 698.7059
Random effects:
 Groups   Name        Variance Std.Dev. Corr  
 commid   (Intercept) 1.56413  1.25065        
          trust.cz1   0.17922  0.42334  1.000 
 Residual             0.89728  0.94725        
# of obs: 652, groups: commid, 39

Fixed effects:
                  Estimate  Std. Error  DF t value Pr(>|t|)  
(Intercept)    -1.6115e-01  6.7997e-01 637 -0.2370  0.81274  
edu            -5.2585e-02  4.1048e-02 637 -1.2810  0.20064  
jiankang       -9.8243e-01  4.4645e-01 637 -2.2005  0.02813 *
peixun         -4.6307e-01  2.6397e-01 637 -1.7542  0.07988 .
hunyin         -1.2255e-02  2.8151e-01 637 -0.0435  0.96529  
hunyin         -2.7726e-01  1.3846e+00 637 -0.2002  0.84136  
hunyin         -2.9759e-01  8.7180e-01 637 -0.3414  0.73295  
cadcj           2.2366e-01  7.6467e-01 637  0.2925  0.77000  
age             9.3626e-02  4.0390e-02 637  2.3180  0.02076 *
age2           -1.3095e-03  5.5104e-04 637 -2.3763  0.01778 *
sex             3.9188e-01  1.9759e-01 637  1.9833  0.04776 *
dangyuan       -5.2558e-01  5.9091e-01 637 -0.8894  0.37410  
Comp.1          5.2463e-02  1.0309e-01 637  0.5089  0.61100  
Comp.2         -1.5048e-01  1.1435e-01 637 -1.3160  0.18863  
trust.cz1      -8.0709e-01  4.4632e-01 637 -1.8083  0.07103 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
There were 11 warnings (use warnings() to see them)
Warning messages:
1: optim or nlminb returned message false convergence (8) 
 in: LMEopt(x = mer, value = cv) 
2: optim or nlminb returned message false convergence (8) 
 in: LMEopt(x = mer, value = cv) 
3: optim or nlminb returned message false convergence (8) 
 in: LMEopt(x = mer, value = cv) 
4: optim or nlminb returned message false convergence (8) 
 in: LMEopt(x = mer, value = cv) 
5: optim or nlminb returned message false convergence (8) 
 in: LMEopt(x = mer, value = cv) 
6: optim or nlminb returned message false convergence (8) 
 in: LMEopt(x = mer, value = cv) 
7: optim or nlminb returned message false convergence (8) 
 in: LMEopt(x = mer, value = cv) 
8: optim or nlminb returned message false convergence (8) 
 in: LMEopt(x = mer, value = cv) 
9: optim or nlminb returned message singular convergence (7) 
 in: LMEopt(x = mer, value = cv) 
10: optim or nlminb returned message singular convergence (7) 
 in: LMEopt(x = mer, value = cv) 
11: optim or nlminb returned message singular convergence (7) 
 in: LMEopt(x = mer, value = cv)
_              
platform i386-pc-mingw32
arch     i386           
os       mingw32        
system   i386, mingw32  
status                  
major    2              
minor    2.0            
year     2005           
month    10             
day      06             
svn rev  35749          
language R     	



 				


2005-12-14

------
Deparment of Sociology
Fudan University

My new mail addres is ronggui.huang at gmail.com
Blog:http://sociology.yculblog.com
3 days later
Spencer Graves
# 
You actually received two different warning messages.  The first 8
warnings read, "optim or nlminb returned message false convergence (8)",
and the other 3 say, "... returned message singular convergence".  The
function "lmer" uses a nonlinear optimizer (either "optim" or "nlminb")
to minimize an objective function.  The first message says that the
nonlinear optimizer was still reducing the objective function when it
reached an iteration limit.  If this were the  only problem, you might
consider increasing the iteration limits, maxIter, msMaxIter, niterEM
and PQLmaxIt.  However, the "singular convergence" message says that the
estimated variance-covariance matrix of the observations became singular.

	  Looking now at your output, I notice that "Corr" between
"(Intercept)" and "trust.cz1" for the "Random Effects" "commid" is
1.000.  This says that the structure of your data are not adequate to
allow you to distinguish between random effects for "(Intercept)" and
"trust.cz1" for "commid", while simultaneously estimating all the fixed
effects you have in the model.

	  If I were you, I'd start be deleting all the terms from the model
that don't have a "Signif. code" beside it in the table of "Fixed
effects" and then refit the smaller model, preferably also using
'method="AGQ"'.  If I still get the same message from trying to fit the
reduced model, I would conclude that the data are not adequate to
distinguish between "(Intercept)" and "trust.cz1" for "commid".  I would
then delete "trust.cz1" from the model and go from there.

	  hope this helps.
	  spencer graves
ronggui wrote:

            

      
      
      
    

        
age + age2 + sex + dangyuan + Comp.1 + Comp.2+trust.cz1 +
(trust.cz1|commid), data = individual,na.action =
"na.exclude",family="quasibinomial"))