Error messages using LMER
Thanks for including all of that output. I believe that in this version the parameters are the relative variances. This would indicate that somehow you are getting fits with very low residual sums of squares in the weighted least squares problem. It could be that you have too many fixed effects terms in the model and are getting complete separation.
On 8/18/05, Shige Song <shigesong at gmail.com> wrote:
Here is what happened using R-devel: ------------------------------------------------------------- EM iterations 0 85289.766 ( 5407.13: 0.0815) ( 26134.4: 0.00387) 1 84544.322 ( 333.732: 0.137) ( 1462.32: 0.00934) 2 84515.108 ( 129.506: 0.0270) ( 446.306: 0.00481) 3 84514.519 ( 115.592: 0.00355) ( 328.637: 0.00103) 4 84514.505 ( 113.981:0.000505) ( 311.160:0.000165) 5 84514.505 ( 113.755:7.45e-005) ( 308.524:2.50e-005) 6 84514.505 ( 113.722:1.11e-005) ( 308.128:3.77e-006) 7 84514.505 ( 113.717:1.66e-006) ( 308.068:5.66e-007) 8 84514.505 ( 113.716:2.50e-007) ( 308.059:8.49e-008) 9 84514.505 ( 113.716:3.74e-008) ( 308.058:1.27e-008) 10 84514.505 ( 113.716:5.62e-009) ( 308.058:1.91e-009) 11 84514.505 ( 113.716:8.43e-010) ( 308.058:2.87e-010) 12 84514.505 ( 113.716:1.27e-010) ( 308.058:4.31e-011) 13 84514.505 ( 113.716:1.90e-011) ( 308.057:6.47e-012) 14 84514.505 ( 113.716:2.86e-012) ( 308.057:9.73e-013) 15 84514.505 ( 113.716:4.25e-013) ( 308.057:1.44e-013) EM iterations 0 83740.342 ( 113.716: -0.0164) ( 308.057:0.000596) 1 83740.273 ( 121.512:-0.00121) ( 298.914:-3.24e-005) 2 83740.272 ( 122.131:-0.000111) ( 299.397:-1.24e-005) EM iterations 0 84011.546 ( 122.131:-0.00474) ( 299.397:-0.000265) 1 84011.539 ( 124.616:-0.000472) ( 303.415:-6.62e-005) 2 84011.539 ( 124.869:-5.58e-005) ( 304.433:-1.16e-005) EM iterations 0 84018.589 ( 124.869:-0.000139) ( 304.433:-1.81e-005) 1 84018.589 ( 124.944:-1.62e-005) ( 304.713:-3.26e-006) 2 84018.589 ( 124.953:-2.12e-006) ( 304.764:-5.23e-007) EM iterations 0 84018.611 ( 124.953:-2.38e-006) ( 304.764:-5.44e-007) 1 84018.611 ( 124.954:-3.25e-007) ( 304.772:-8.50e-008) 2 84018.611 ( 124.955:-4.66e-008) ( 304.773:-1.29e-008) EM iterations 0 84018.611 ( 124.955:-4.75e-008) ( 304.773:-1.30e-008) 1 84018.611 ( 124.955:-6.93e-009) ( 304.774:-1.97e-009) 2 84018.611 ( 124.955:-1.03e-009) ( 304.774:-2.96e-010) Warning messages: 1: optim or nlminb returned message See PORT documentation. Code (27) in: LMEopt(x = mer, value = cv) 2: optim or nlminb returned message See PORT documentation. Code (27) in: LMEopt(x = mer, value = cv) 3: optim or nlminb returned message See PORT documentation. Code (27) in: LMEopt(x = mer, value = cv) 4: optim or nlminb returned message See PORT documentation. Code (27) in: LMEopt(x = mer, value = cv) 5: optim or nlminb returned message See PORT documentation. Code (27) in: LMEopt(x = mer, value = cv) 6: optim or nlminb returned message See PORT documentation. Code (27) in: LMEopt(x = mer, value = cv) 7: nlminb failed to converge in: lmer(.D ~ offset(log(.Y)) + (1 | provn) + (1 | bcohort) + agri + ------------------------------------------------------------- Shige On 8/19/05, Shige Song <shigesong at gmail.com> wrote:
Dear Professor Bates, Here is output R 2.1.1 produced with "control = list(EMverbose = TRUE, msVerbose = TRUE)". I am getting the new devel version and see what will hapen there: -------------------------------------------------------------------------------- EM iterations 0 85289.766 ( 5407.13: 0.0815) ( 26134.4: 0.00387) 1 84544.322 ( 333.732: 0.137) ( 1462.32: 0.00934) 2 84515.108 ( 129.506: 0.0270) ( 446.306: 0.00481) 3 84514.519 ( 115.592: 0.00355) ( 328.637: 0.00103) 4 84514.505 ( 113.981:0.000505) ( 311.160:0.000165) 5 84514.505 ( 113.755:7.45e-005) ( 308.524:2.50e-005) 6 84514.505 ( 113.722:1.11e-005) ( 308.128:3.77e-006) 7 84514.505 ( 113.717:1.66e-006) ( 308.068:5.66e-007) 8 84514.505 ( 113.716:2.50e-007) ( 308.059:8.49e-008) 9 84514.505 ( 113.716:3.74e-008) ( 308.058:1.27e-008) 10 84514.505 ( 113.716:5.62e-009) ( 308.058:1.91e-009) 11 84514.505 ( 113.716:8.43e-010) ( 308.058:2.87e-010) 12 84514.505 ( 113.716:1.27e-010) ( 308.058:4.31e-011) 13 84514.505 ( 113.716:1.90e-011) ( 308.057:6.47e-012) 14 84514.505 ( 113.716:2.86e-012) ( 308.057:9.73e-013) 15 84514.505 ( 113.716:4.25e-013) ( 308.057:1.44e-013) iter 0 value 84514.505044 final value 84514.505044 converged EM iterations 0 83740.342 ( 113.716: -0.0164) ( 308.057:0.000596) 1 83740.273 ( 121.512:-0.00121) ( 298.914:-3.24e-005) 2 83740.272 ( 122.131:-0.000111) ( 299.397:-1.24e-005) iter 0 value 83740.272232 final value 83740.272232 converged EM iterations 0 84011.550 ( 122.204:-0.00461) ( 299.576:-0.000256) 1 84011.543 ( 124.624:-0.000459) ( 303.453:-6.41e-005) 2 84011.543 ( 124.870:-5.42e-005) ( 304.440:-1.13e-005) iter 0 value 84011.543350 final value 84011.543350 converged EM iterations 0 84018.592 ( 124.915:-6.44e-005) ( 304.548:-1.22e-005) 1 84018.592 ( 124.949:-8.29e-006) ( 304.737:-1.99e-006) 2 84018.592 ( 124.954:-1.15e-006) ( 304.768:-3.08e-007) iter 0 value 84018.591624 final value 84018.591624 converged EM iterations 0 84018.612 ( 124.955:3.40e-007) ( 304.770:-1.98e-007) 1 84018.612 ( 124.955:-9.98e-009) ( 304.773:-2.23e-008) 2 84018.612 ( 124.955:-5.47e-009) ( 304.774:-2.86e-009) iter 0 value 84018.611512 final value 84018.611512 converged Error in fn(par, ...) : Unable to invert singular factor of downdated X'X In addition: Warning message: Leading minor of size 8 of downdated X'X is indefinite -------------------------------------------------------------------------------- Thanks! Shige On 8/18/05, Douglas Bates <dmbates at gmail.com> wrote:
On 8/18/05, Shige Song <shigesong at gmail.com> wrote:
Dear All, After playing with lmer for couple of days, I have to say that I am amazed! I've been using quite some multilevel/mixed modeling packages, lme4 is a strong candidate for the overall winner, especially for multilevel generzlized linear models. Now go back to my two-level poisson model with cross-classified model. I've been testing various different model specificatios for the past couple of days. Here are the models I tried: 1) Two level random intercept model with level-1 covariates only m1 <- lmer(.D ~ offset(log(.Y)) + (1|provn) +(1|bcohort) + x1 + x2 , data, poisson, method="Laplace") 2) Two-level random intercept model with both level-1 and level-2 covariates, but no cross-level interactions: m2 <- lmer(.D ~ offset(log(.Y)) + (1|provn) +(1|bcohort) + x1 + x2 + z1 + z2, data, poisson, method="Laplace") 3) Two-level random intercept with cross-level interaction m3 <- lmer(.D ~ offset(log(.Y)) + (1|provn) +(1|bcohort) + x1 + x2 + z1 + z2 + x1:z1 + x2:z2, data, poisson, method="Laplace") Both model 1 and 2 run fine. For model 3, I got error message: ---------------------------------- Error in fn(par, ...) : Unable to invert singular factor of downdated X'X In addition: Warning messages: 1: optim or nlminb returned message ERROR: ABNORMAL_TERMINATION_IN_LNSRCH in: LMEopt(x = mer, value = cv) 2: Leading minor of size 1 of downdated X'X is indefinite ---------------------------------- What is going on here? Any workarounds? Thanks!
The first thing I would try is set the EMverbose and msVerbose flags in the control list to see what occurs within the optimization. That is append the argument control = list(EMverbose = TRUE, msVerbose = TRUE) to your call to lmer(). You may also want to try the call in a recently compiled R-devel, which will be released as R-2.2.0 in October. You will notice that the first warning message reads "optim or nlminb". In R-2.1.1 lmer uses optim for the optimization. Starting with R-2.2.0 the default is to use nlminb. Test compilations of R-devel for Windows are available from CRAN.
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