>>> El día 08/01/2013 a las 12:40, Silvina Velez <sve...@mendoza-conicet.gob.ar> escribió: > Hi All, > I have data about seed predation (SP) in fruits of three differents colors > (yellow, motted, dark) and in two fruiting seasons (2007, 2008). I performed > a GLMM (lmer function, lme4 package) and the outcome showed that the
> interaction term (color:season) was significant, and some combinations of > this interaction have significant Pr(>|z|), but I don't think they are the > right significant combinations, because when I look the bwplot, this > combinations seems to be very different from the other ones. So, I would like > to know if there is any test "a posteriori" to know the p-values for each > combination of color:season, and thereby be able to know what conbination/s > is/are really significant. > > m1=lmer(SP ~ color + season:color +(1|Site:tree), data=datosfl, > family="poisson") > AIC BIC logLik deviance > 178.3 196.6 -81.14 162.3 > Random effects: > Groups Name Variance Std.Dev. > obsBR (Intercept) 0.064324 0.25362 > Site:tree (Intercept) 0.266490 0.51623 > Number of obs: 73, groups: obsBR, 73; Site:tree, 37 > > Estimate Std. Error z value Pr(>|z|) > (Intercept) 2.5089 0.2750 9.125 <2e-16 *** > colorM -0.1140 0.3242 -0.352 0.7250 > colorD -0.6450 0.4178 -1.544 0.1227 > Season2008 -0.7343 0.3104 -2.365 0.0180 * > colorM:Season2008 0.2505 0.4352 0.576 0.5648 > colorD:Season2008 1.1445 0.5747 1.992 0.0464 * Hi Silvina, What do you exactly mean with "what combination(s) is/are significant"? If you mean "what combinations have significantly greater SP than the baseline combination (yellow:2007)", the table that you have copied may be what you actually want. If you want to test other contrasts between color:season combinations, perhaps you can use the function testInteractions() from package "phia". For instance: testInteractions(m1) will give you a test of all the pairwise contrasts between color and season. You can also test simple main effects, or other specific contrasts by adding further arguments (see the documentation and the package vignette). Anyway, the calculation of p-values in mixed models must always be taken with care. Helios De Rosario-Martinez Instituto de Biomecánica de Valencia INSTITUTO DE BIOMECÁNICA DE VALENCIA Universidad Politécnica de Valencia • Edificio 9C Camino de Vera s/n • 46022 VALENCIA (ESPAÑA) Tel. +34 96 387 91 60 • Fax +34 96 387 91 69 www.ibv.org Antes de imprimir este e-mail piense bien si es necesario hacerlo. En cumplimiento de la Ley Orgánica 15/1999 reguladora de la Protección de Datos de Carácter Personal, le informamos de que el presente mensaje contiene información confidencial, siendo para uso exclusivo del destinatario arriba indicado. En caso de no ser usted el destinatario del mismo le informamos que su recepción no le autoriza a su divulgación o reproducción por cualquier medio, debiendo destruirlo de inmediato, rogándole lo notifique al remitente. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
