Hi all, I recently upgraded versions of R and the package lme4 in a fit of something-or-other. My older version was old enough to still have p-values. I now have the current version (0.99875-7), and am learning to use mcmc to calculate p-values, but basically all my independent variables are factors, so p-values for each level are not really helpful, I need to estimate the impact of the whole factor. I can live without p-values on the model itself, but the lack of them in anova is killing me. I've been trying out mcmcpvalue from here:
http://wiki.r-project.org/rwiki/doku.php?id=guides:lmer-tests But it seems to only work for the linear models, not the glmms. Is there something out there for those, or a way to adapt this script for it? Also, I'm not really understanding the structure well enough to get how to get it to evaluate interactions (or if it will). If I'm looking at something like this: > HPDinterval(status_samp) lower upper (Intercept) 3.96106199 4.54810253 sregionivan -1.49569838 -0.32674386 sregionjason -1.08346050 0.06535123 sregionsouth -0.96097370 -0.13712609 ininging -0.15327601 0.19483823 factor(workingclass)1 -1.40345872 -0.53510599 sregionivan:ininging -0.28212347 0.48140623 sregionjason:ininging 0.00660252 0.72089386 sregionsouth:ininging -0.34948297 0.17119827 sregionivan:factor(workingclass)1 0.19306970 1.56657035 sregionjason:factor(workingclass)1 -0.13693835 2.36486351 sregionsouth:factor(workingclass)1 0.10013804 1.11524003 ininging:factor(workingclass)1 0.60875674 1.78589051 sregionivan:ininging:factor(workingclass)1 -2.36086950 -0.34788386 sregionjason:ininging:factor(workingclass)1 -2.16553468 0.84887041 sregionsouth:ininging:factor(workingclass)1 -1.65891153 -0.26497955 log(sigma^2) -0.56604768 -0.37239636 log(id.(In)) -2.44938452 -1.67803069 log(word.(In)) -2.24918742 -0.96898869 attr(,"Probability") [1] 0.95 which lines together give me the interaction of ining and factor(workingclass)? Is it just ininging:factor(workingclass)1 0.60875674 1.78589051 or is it ininging -0.15327601 0.19483823 factor(workingclass)1 -1.40345872 -0.53510599 ininging:factor(workingclass)1 0.60875674 1.78589051 One tempting option is to look at the p-values for anova comparing two models, one with and one without the term or interaction I'm interested in. But searching on the R-help list tells me that's not a good idea, as it is anti-conservative. Can someone explain why, or point me to a good explanation (where good=using small non-technical words)? Any help is much appreciated. Thanks, Kathryn _______________________________________________ R-lang mailing list [email protected] https://ling.ucsd.edu/mailman/listinfo.cgi/r-lang
