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List:       r-sig-mixed-models
Subject:    [R-sig-ME] (no subject)
From:       DESPINA MICHAILIDOU <de.michailidou () gmail ! com>
Date:       2019-05-01 17:24:10
Message-ID: CAGP99JEbZ5=BPr6AGriWAMcqjdgw2L6mJt=2AO0d9_nKvjOUDw () mail ! gmail ! com
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Hi,

I run the following regression analysis with lme4 package and I get the
following message.

glmm_CA_intens<- glmer(Dizz_today ~ CA_intens  + (1 | ID/SCAN_DATE/Side),
data=TAK_data, family=binomial(link = "logit"))
summary(glmm_CA_intens)

Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: Dizz_today ~ CA_intens + (1 | ID/SCAN_DATE/Side)
   Data: TAK_data

     AIC      BIC   logLik deviance df.resid
    41.7     58.4    -15.8     31.7      205

Scaled residuals:
      Min        1Q    Median        3Q       Max
-0.003892 -0.000827 -0.000826  0.000000  0.054166

Random effects:
 Groups              Name        Variance Std.Dev.
 Side:(SCAN_DATE:ID) (Intercept)    0.0    0.00
 SCAN_DATE:ID        (Intercept) 3223.4   56.78
 ID                  (Intercept)  199.4   14.12
Number of obs: 210, groups:  Side:(SCAN_DATE:ID), 210; SCAN_DATE:ID, 105;
ID, 55

Fixed effects:
              Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.419e+01  1.901e+01  -0.747    0.455
CA_intens   -2.892e+03  1.103e+07   0.000    1.000

Correlation of Fixed Effects:
          (Intr)
CA_intens 0.000
convergence code: 0
boundary (singular) fit: see ?isSingular

Warning messages:
1: In vcov.merMod(object, use.hessian = use.hessian) :
  variance-covariance matrix computed from finite-difference Hessian is
not positive definite or contains NA values: falling back to var-cov
estimated from RX
2: In vcov.merMod(object, correlation = correlation, sigm = sig) :
  variance-covariance matrix computed from finite-difference Hessian is
not positive definite or contains NA values: falling back to var-cov
estimated from RX
>
What is wrong?

Thank you in advance for your help.

Sincerely,
Despina

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