Chi: I would say that these are not really R questions, but about statistical procedures in general. However, there are some important issues that you touch on, so I cannot resist commenting. Hopefully my comments might pique others to add theirs -- and perhaps to disagree with me.
1. Unbalanced data? Yes that's what they're for. However, you need to recognize that with unbalanced data, interpretability of coefficients may be lost (the estimates depend on what factors you include in your model). 2.Proper analysis? TRUST in DOUG. (Doug Bates, the author of the packages and an acknowledged expert in the field). 3. Without P values, how do I I tell what's significant? Well, first of all, P values/statistical significance are only meaningful when hypotheses are pre-specified; and even then, statistical significance is a function of effect magnitude, sample size, and experimental noise, so usually doesn't accord with scientific importance anyway unless the experiments have been powered appropriately, which is rarely the case outside clinical trials. So statistical significance is probably irrelevant to the scientific questions iin any case. I would say that you need to focus on effect (coefficient) sizes and perhaps do sensitivity analyses to see how much predicted results change as you change them. Good graphs of your data are also always a good idea. 4, With only 5 blocks, you have practically no information to estimate variance anyway. You're probably better off treating them as fixed effects and just estimating the model via lm. You might find a sqrt or log transformation of the enfa numbers to be useful, though... Cheers, Bert On Mon, Oct 25, 2010 at 11:44 AM, Chi Yuan <cy...@email.arizona.edu> wrote: > 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. > -- Bert Gunter Genentech Nonclinical Biostatistics ______________________________________________ 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.