# identifies random outliers given a p-criterion, from an lmer/lmer2 object
# Kyle Gorman <kgorman@ling.upenn.edu>
# todo: make this print prettier

ranOutliers = function(model, p = 0.05) {   
    est = ranef(model)
    se = se.ranef(model)
    groupings = names(est)
    i = 1 # keep track of where we are grouping
    for(zest in est) { # over groupings
        j = 1 # keep track of where we are in se's x-given-group
        for(effect in names(zest)) { # over effects in grouping
            k = 1
            levels = row.names(zest[effect])    
            print(paste(effect, groupings[i], sep='|'))
            for(val in zest[effect][,]) {
                pval = pnorm(val, 0, se[[i]][k,j])
                if(pval < p) { # signif
                    print(paste('   ', levels[k], val, pval))
                }
                k = k + 1
            }
            j = j + 1
        }
        i = i + 1
    }
}