Hello, I ran a model as glmer(y ~ x, family = poisson, data, weights = w) and then tried to get confidence intervals with the following: confint(model, method = "boot", parallel = "multicore", ncpus = 4) However I'm getting the following warning message that I'm not receiving when using method "Wald" instead of "boot": Warning message: In sfun(object, nsim = 1, ftd = rep_len(musim, n * nsim), wts = weights) : ignoring prior weights What's the meaning of this message? Thanks for your help, Denis
confint.merMod, bootstrap and weights
2 messages · Denis Haine, Ben Bolker
The simulation function (sfun()) that's at the core of the parametric bootstrap algorithm is ignoring your specified prior weights. Poisson models with weights are somewhat unusual; what are the weights in your model supposed to signify? If you were simulating the data, how would you incorporate the weights in the simulation procedure? Ben Bolker
On 16-03-13 02:30 PM, Denis Haine wrote:
Hello, I ran a model as glmer(y ~ x, family = poisson, data, weights = w) and then tried to get confidence intervals with the following: confint(model, method = "boot", parallel = "multicore", ncpus = 4) However I'm getting the following warning message that I'm not receiving when using method "Wald" instead of "boot": Warning message: In sfun(object, nsim = 1, ftd = rep_len(musim, n * nsim), wts = weights) : ignoring prior weights What's the meaning of this message? Thanks for your help, Denis [[alternative HTML version deleted]]
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