Re: FW: mixed effect model: compare seed families To get around the problem of 'lmer()' not returning p values, Bates recommends using a Markov Chain Monte Carlo sample to evaluate the properties of individual coefficients. This is much easier than it may sound! If you download and install the library 'languageR', there is a function called 'pvals.fnc()', which computes p-values and MCMC confidence intervals for mixed models. It is very simple to use. 'pvalues.fnc' internally calls the functions 'mcmcsamp()' and 'HPDinterval()', which are what Bates recommends, to generate 95% MCMC confidence intervals and p values. It also supplies p values based on the t statistic (using the number of observations minus the number of fixed-effects coefficients as degrees of freedom), but warns that this p-value is anti-conservative. So, the MCMC intervals and p values are probably the best to use. However, and this may be a problem for your particular design, the 'pvalues.fnc' help file warns: "Currently, MCMC sampling for generalized linear mixed models may not work off the shelf when there is more than one random intercept in the model." But these functions are constantly being updated, so hopefully by now it can handle more than 1 random effect. Also, for information about why lmer does not supply p-values, see these sites: https://stat.ethz.ch/pipermail/r-help/2006-May/094765.html http://tolstoy.newcastle.edu.au/R/help/06/08/32409.html Hope that helps! Liza Comita
mixed effect model: compare seed families
1 message · Liza Comita