Thanks for adding the cl parameter to mixed(), very nice! However, it seems to cause some problems when passing the ‘weights’ parameter. This causes an error, stopping immediately:
> mixed(F1 ~ cued + (1 + cued|pair),
+ family=binomial, weights=wt,
+ method='PB', args_test = list(nsim =10, cl = cl),
+ data = performance.expt2, cl = cl)
Fitting 2 (g)lmer() models.
Error in checkForRemoteErrors(val) :
2 nodes produced errors; first error: object 'wt' not found
Whereas this seems to run ok (albeit convergence warnings):
> mixed(F1 ~ cued + (1 + cued|pair),
+ family=binomial, weights=wt,
+ method='PB', args_test = list(nsim =10, cl = cl),
+ data = performance.expt2)
Fitting 2 (g)lmer() models:
[..]
Obtaining 1 p-values:
[.]
Warning messages:
1: In eval(family$initialize, rho) :
non-integer #successes in a binomial glm!
2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.0127221 (tol = 0.001, component 1)
3: In eval(family$initialize, rho) :
non-integer #successes in a binomial glm!
4: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.00420134 (tol = 0.001, component 1)
Any idea what’s going on? (note, nsim=10 is just for troubleshooting)
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This topic was modified 3 years, 5 months ago by
S McIntyre.