El d?a 08/01/2013 a las 12:40, Silvina Velez
<svelez at mendoza-conicet.gob.ar> escribi?:
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 Biomec?nica de Valencia INSTITUTO DE BIOMEC?NICA DE VALENCIA Universidad Polit?cnica de Valencia ? Edificio 9C Camino de Vera s/n ? 46022 VALENCIA (ESPA?A) 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 Org?nica 15/1999 reguladora de la Protecci?n de Datos de Car?cter Personal, le informamos de que el presente mensaje contiene informaci?n confidencial, siendo para uso exclusivo del destinatario arriba indicado. En caso de no ser usted el destinatario del mismo le informamos que su recepci?n no le autoriza a su divulgaci?n o reproducci?n por cualquier medio, debiendo destruirlo de inmediato, rog?ndole lo notifique al remitente.