[R-sig-eco] Bootstrapping with pseudo-replicates
Johannes, A very good question to ask, but you can't use a bootstrap, or boot(), to investigate it. You can define strata and then bootstrap observations within strata, but all bootstrap data sets will have the same structure as the original data. That's the point of the bootstrap. In your example, you have observations from 4 sites, 1 obs from site 1, 2 from site 2, 1 from site 3, and 2 from site 4. Every stratified bootstrap sample will have 1 from site 1, 2 from site 2, 1 from site 3 and 2 from site 4. I believe you have to construct your own code, probably along the lines of defining a vector for one obs per site, then for each site: extracting the set of pseudoreplicates for one site, using sample() to grab one value from that set, then storing in the vector. Best wishes, Philip Dixon ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] interpreting adonis results
On Wed, 2011-11-16 at 03:43 +0100, Gian Maria Niccolò Benucci wrote: Hi all, I had 84 samples collected in 7 different sites. In each sample were individuated the different fungal species and recorded. I would test if exist a real difference between the sites and if exist a sort of site effect that structure the fungal communities... Then, I did adonis test adonis(community.sq ~ location, data=env.table, permutations=999) Call: adonis(formula = community.sq ~ location, data = env.table, permutations = 999) Df SumsOfSqs MeanSqs F.Model R2 Pr(F) location 612.593 2.09886 6.8867 0.34922 0.001 *** Residuals 7723.467 0.30477 0.65078 Total 8336.060 1.0 --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 The significance is R2=0.349 at P=0.001 Can I assure that exist a strong site effect in structuring the communities in each site? Depends. The test is one of no effect of `location`. You have found evidence against this hypothesis and thus could reject this hypothesis, instead accepting the alternative hypothesis that there is an effect of `location`. As to the strength of this effect? ~35% of the sums of squares can be explained by `location`. Substantially more of the variance remains unexplained. As I know nothing about your subject area, I am unable to comment further on the strength of the relationship. Seeing as many ecologists whose work I read would say an effect is significant if the p-value was = 0.05. Not that I subscribe to this way or working, but by that criterion, you have identified a significant `location` effect. HTH G Thanks for helping, G. [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology -- %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% Dr. Gavin Simpson [t] +44 (0)20 7679 0522 ECRC, UCL Geography, [f] +44 (0)20 7679 0565 Pearson Building, [e] gavin.simpsonATNOSPAMucl.ac.uk Gower Street, London [w] http://www.ucl.ac.uk/~ucfagls/ UK. WC1E 6BT. [w] http://www.freshwaters.org.uk %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
[R-sig-eco] Marrying Tukey's HSD and ANOVA results
I've done a standard two way ANOVA using glm on the dependent variable clutchsize with the two factors treatment (which has 3 levels called 1, 2, and 3) and species (which has two levels called 1 and 2). Apparently there is no significant interaction term. Then I did Tukey's HSD and found that there were significant differences between species at only one of the three treatment levels, treatment level 1. Are these in fact conflicting results? ##ANOVA RESULTS summary(aov((clutchsize~treatment*species))) Df Sum Sq Mean Sq F value Pr(F) treatment 1 29.26 29.264 7.0230 0.00884 ** species 1 138.14 138.143 33.1526 4.13e-08 *** treatment:species 1 8.11 8.110 1.9464 0.16487 Residuals 163 679.20 4.167 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##TUKEY HSD RESULTS TukeyHSD(aov(clutchsize~treatment*species)) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = clutchsize ~ treatment * species) $treatment difflwr upr p adj 2-1 1.3245614 0.4184292 2.2306936 0.0020030 3-1 1.0416667 0.1316071 1.9517262 0.0204117 3-2 -0.2828947 -1.1806793 0.6148899 0.7368331 $species diff lwr upr p adj 2-1 -1.89988 -2.544747 -1.255013 0 $`treatment:species` diff lwrupr p adj 2:1-1:1 1.1791506 -0.19269072 2.5509919 0.1364846 3:1-1:1 0.6476190 -0.73345324 2.0286913 0.7550479 1:2-1:1 -2.4225564 -4.08045729 -0.7646555 0.0005858 2:2-1:1 -0.8357143 -2.46652980 0.7951012 0.6787094 3:2-1:1 -0.6357143 -2.26652980 0.9951012 0.8706501 3:1-2:1 -0.5315315 -1.89354633 0.8304833 0.8701101 1:2-2:1 -3.6017070 -5.24376636 -1.9596476 0.000 2:2-2:1 -2.0148649 -3.62957317 -0.4001566 0.0055886 3:2-2:1 -1.8148649 -3.42957317 -0.2001566 0.0177862 1:2-3:1 -3.0701754 -4.71995456 -1.4203963 0.041 2:2-3:1 -1.483 -3.10589150 0.1392248 0.0944158 3:2-3:1 -1.283 -2.90589150 0.3392248 0.2077935 2:2-1:2 1.5868421 -0.27701672 3.4507009 0.1438444 3:2-1:2 1.7868421 -0.07701672 3.6507009 0.0684872 3:2-2:2 0.200 -1.63980803 2.0398080 0.9995894 ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
