Hello: I have an two factorial random block design. It's a ecology experiment. My two factors are, guild removal and enfa removal. Both are two levels, 0 (no removal), 1 (removal). I have 5 blocks. But within each block, it's unbalanced at plot level because I have 5 plots instead of 4 in each block. Within each block, I have 1 plot with only guild removal, 1 plot with only enfa removal, 1 plot for control with no removal, 2 plots for both guild and enfa removal. I am looking at how these treatment affect the enfa mortality rate. I decide to use mixed model to treat block as random effect. So I try both nlme and lme4. But I don't know whether they take the unbalanced data properly. So my question is, does lme in nlme and lmer in lme4 take unbalanced data? How do I know it's analysis in a proper way? Here is my code and the result for each method. I first try nlme library(nlme) m=lme(enfa_mortality~guild_removal*enfa_removal,random=~1|block,data=com_summer) It gave me the result as following Linear mixed-effects model fit by REML Data: com_summer AIC BIC logLik 8.552254 14.81939 1.723873
Random effects: Formula: ~1 | block (Intercept) Residual StdDev: 9.722548e-07 0.1880945 Fixed effects: enfa_mortality ~ guild_removal * enfa_removal Value Std.Error DF t-value p-value (Intercept) 0.450 0.0841184 17 5.349603 0.0001 guild_removal -0.100 0.1189614 17 -0.840609 0.4122 enfa_removal -0.368 0.1189614 17 -3.093441 0.0066 guild_removal:enfa_removal 0.197 0.1573711 17 1.251818 0.2276 Correlation: (Intr) gld_rm enf_rm guild_removal -0.707 enfa_removal -0.707 0.500 guild_removal:enfa_removal 0.535 -0.756 -0.756 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -1.7650706 -0.7017751 0.1594943 0.7974717 1.9139320 Number of Observations: 25 Number of Groups: 5 I kind of heard the P value does not matter that much in the mixed model. Is there any other way I can tell whether the treatment has a significant effect or not? I then try lme4, it give similar result, but won't tell me the p value. library(lme4) m<-lmer(enfa_mortality ~ guild_removal*enfa_removal +(1|block), data=com_summer) here is the result Linear mixed model fit by REML Formula: enfa_mortality ~ guild_removal * enfa_removal + (1 | block) Data: com_summer AIC BIC logLik deviance REMLdev 8.552 15.87 1.724 -16.95 -3.448 Random effects: Groups Name Variance Std.Dev. block (Intercept) 0.000000 0.00000 Residual 0.035380 0.18809 Number of obs: 25, groups: block, 5 Fixed effects: Estimate Std. Error t value (Intercept) 0.45000 0.08412 5.350 guild_removal -0.10000 0.11896 -0.841 enfa_removal -0.36800 0.11896 -3.093 guild_removal:enfa_removal 0.19700 0.15737 1.252 Correlation of Fixed Effects: (Intr) gld_rm enf_rm guild_remvl -0.707 enfa_removl -0.707 0.500 gld_rmvl:n_ 0.535 -0.756 -0.756 I really appreciate any suggestion! Thank you! -- Chi Yuan Graduate Student Department of Ecology and Evolutionary Biology University of Arizona Room 106 Bioscience West lab phone: 520-621-1889 Email:cy...@email.arizona.edu Website: http://www.u.arizona.edu/~cyuan/ ______________________________________________ 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.