Viewing posts tagged From old blog

New regression on discrete dataset

Continuing with the new regression model. Now it's the discrete dataset.

New regression on original dataset

It works for the original dataset pretty well. Here's what comes out:

New regression equation for LeapArticulator experiments

I have just discovered a better model for participants scores in the experiment. This used to be our prime model: ```prettyprint model <- lmer(score ~ nstates_amp_and_freq_n + (1 | id) + (1 |condition:phase) , data=all, REML=F) ``` I have discovered the following one outperforms this for both the original and the discrete datasets, and is much easier to interpret: ```prettyprint model <- lmer(score ~ nstates_amp_and_freq_n:phase_order:phase + (1|id), data=all, REML=F) ``` Of course, we are using MCMC samples, so the model declaration becomes:

Big poop - Journal paper is wrong!

I have just noticed that the first journal paper we are planning to send in with Hannah contains a huge error: I keep mentioning predictors, but I should be talking about random effects!

lme4 and P-values

There is a lot of stuff online about eliciting p-values for lme4 linear mixed-effects regression model coefficients (just google it). I opted for MCMCglmm because I like MCMC , and because lme4 has deprecated its own MCMC implementation.