Lately I have been tackling a problem about estimating precision and trueness of dental measurements. I know that a mixed effects model is a best way to reveal these estimates, so I go that way.
I built some models with lm4::lmer and nlme::lme. Since lmer doesn't enable heterogenity in variances, I mainly focused on lme. I hadn't thought other packages like glmmTMB or similar although they might have best options. Since lme couldn't calculate 95% CIs, I wrote a simple script doing first nonparametric bootstrap, then parametric bootstrap.
I extracted variance components from the main by simulation, and calculated CIs for precision and trueness. Mixed models did a big job.
But, after all those serious work for simulations, I discovered that parameters package have this ability builtin its functions like simulate_model and model_parameters.
model_paramaters didn't work smoothly but simulate_model did the same job what my code of 100+ lines and many hours of work did immediately. I was surprised by the magical ease those functions supplied.
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