I'm using mvabund uni.test to select species that are changing according to a treatment gradient. I've pulled out species that have a significantly different intercept from the null but I'd also like to indicate if they are positive or negative with treatment and extract the "sum-of-LR" as Warton does in the paper showing the usefulness of this approach (http://onlinelibrary.wiley.com/doi/10.1111/j.2041-210X.2011.00127.x/abstract). I've run manyglm followed by anova on the spider data to try to match the mvabund documentation but I can't figure out where the results beyond the p-values are stored, here are the str for the manyglm and anova output. (the dput are huge)
thanks Kendra > str(glm.spid) List of 41 $ coefficients : num [1:3, 1:12] 2.2353 0.0582 -1.3425 2.1552 -0.4229 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:3] "(Intercept)" "bare.sand" "fallen.leaves" .. ..$ : chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... $ var.coefficients : num [1:3, 1:12] 0.1068 0.0185 0.1351 0.252 0.0466 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:3] "(Intercept)" "bare.sand" "fallen.leaves" .. ..$ : chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... $ fitted.values : num [1:28, 1:12] 9.349 0.843 9.349 9.349 9.349 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:28] "1" "2" "3" "4" ... .. ..$ : chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... $ linear.predictor : num [1:28, 1:12] 2.24 -0.17 2.24 2.24 2.24 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:28] "1" "2" "3" "4" ... .. ..$ : chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... $ residuals : num [1:28, 1:12] 1.767 -0.778 0.638 -0.83 -0.943 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:28] "1" "2" "3" "4" ... .. ..$ : chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... $ PIT.residuals : num [1:28, 1:12] 0.934 0.534 0.79 0.238 0.101 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:28] "1" "2" "3" "4" ... .. ..$ : chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... $ sqrt.1_Hii : num [1:28, 1:12] 0.945 0.89 0.945 0.945 0.945 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:28] "1" "2" "3" "4" ... .. ..$ : chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... $ var.estimator : num [1:28, 1:12] 87.79 1.48 87.79 87.79 87.79 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:28] "1" "2" "3" "4" ... .. ..$ : chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... $ sqrt.weight : num [1:28, 1:12] 0.998 0.693 0.998 0.998 0.998 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:28] "1" "2" "3" "4" ... .. ..$ : chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... $ theta : Named num [1:12] 1.114 0.428 1.301 0.156 0.715 ... ..- attr(*, "names")= chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... $ two.loglike : Named num [1:12] -121.5 -142.1 -78.9 -60.6 -44.5 ... ..- attr(*, "names")= chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... $ deviance : Named num [1:12] 22.2 29.4 18.7 16 10.5 ... ..- attr(*, "names")= chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... $ aic : Named num [1:12] 129.5 150.1 86.9 68.6 52.5 ... ..- attr(*, "names")= chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... $ iter : Named num [1:12] 3 3 6 4 5 4 4 5 4 4 ... ..- attr(*, "names")= chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... $ data :'data.frame': 28 obs. of 3 variables: ..$ spiddat : int [1:28, 1:12] 25 0 15 2 1 0 2 0 1 3 ... .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:28] "1" "2" "3" "4" ... .. .. ..$ : chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... .. ..- attr(*, "class")= chr [1:2] "mvabund" "matrix" ..$ bare.sand : num [1:28] 0 0 0 0 0 ... ..$ fallen.leaves: num [1:28] 0 1.79 0 0 0 ... ..- attr(*, "terms")=Classes 'terms', 'formula' length 3 spiddat ~ bare.sand + fallen.leaves .. .. ..- attr(*, "variables")= language list(spiddat, bare.sand, fallen.leaves) .. .. ..- attr(*, "factors")= int [1:3, 1:2] 0 1 0 0 0 1 .. .. .. ..- attr(*, "dimnames")=List of 2 .. .. .. .. ..$ : chr [1:3] "spiddat" "bare.sand" "fallen.leaves" .. .. .. .. ..$ : chr [1:2] "bare.sand" "fallen.leaves" .. .. ..- attr(*, "term.labels")= chr [1:2] "bare.sand" "fallen.leaves" .. .. ..- attr(*, "order")= int [1:2] 1 1 .. .. ..- attr(*, "intercept")= int 1 .. .. ..- attr(*, "response")= int 1 .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> .. .. ..- attr(*, "predvars")= language list(spiddat, bare.sand, fallen.leaves) .. .. ..- attr(*, "dataClasses")= Named chr [1:3] "nmatrix.12" "numeric" "numeric" .. .. .. ..- attr(*, "names")= chr [1:3] "spiddat" "bare.sand" "fallen.leaves" $ stderr.coefficients: num [1:3, 1:12] 0.327 0.136 0.368 0.502 0.216 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:3] "(Intercept)" "bare.sand" "fallen.leaves" .. ..$ : chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... $ phi : Named num [1:12] 0.897 2.338 0.768 6.43 1.399 ... ..- attr(*, "names")= chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... $ tol : num 1.49e-08 $ maxiter : num 25 $ maxiter2 : num 10 $ AIC : Named num [1:12] 129.5 150.1 86.9 68.6 52.5 ... ..- attr(*, "names")= chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... $ AICsum : num 1658 $ family : chr "negative.binomial" $ K : num 1 $ theta.method : chr "PHI" $ cor.type : chr "I" $ shrink.param : num 0 $ call : language manyglm(formula = spiddat ~ bare.sand + fallen.leaves, family = "negative.binomial", data = X) $ terms :Classes 'terms', 'formula' length 3 spiddat ~ bare.sand + fallen.leaves .. ..- attr(*, "variables")= language list(spiddat, bare.sand, fallen.leaves) .. ..- attr(*, "factors")= int [1:3, 1:2] 0 1 0 0 0 1 .. .. ..- attr(*, "dimnames")=List of 2 .. .. .. ..$ : chr [1:3] "spiddat" "bare.sand" "fallen.leaves" .. .. .. ..$ : chr [1:2] "bare.sand" "fallen.leaves" .. ..- attr(*, "term.labels")= chr [1:2] "bare.sand" "fallen.leaves" .. ..- attr(*, "order")= int [1:2] 1 1 .. ..- attr(*, "intercept")= int 1 .. ..- attr(*, "response")= int 1 .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> .. ..- attr(*, "predvars")= language list(spiddat, bare.sand, fallen.leaves) .. ..- attr(*, "dataClasses")= Named chr [1:3] "nmatrix.12" "numeric" "numeric" .. .. ..- attr(*, "names")= chr [1:3] "spiddat" "bare.sand" "fallen.leaves" $ assign : int [1:3] 0 1 2 $ formula :Class 'formula' length 3 spiddat ~ bare.sand + fallen.leaves .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> $ rank : int 3 $ xlevels : Named list() $ df.residual : int 25 $ x : num [1:28, 1:3] 1 1 1 1 1 1 1 1 1 1 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:28] "1" "2" "3" "4" ... .. ..$ : chr [1:3] "(Intercept)" "bare.sand" "fallen.leaves" $ y : num [1:28, 1:12] 25 0 15 2 1 0 2 0 1 3 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:28] "1" "2" "3" "4" ... .. ..$ : chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... $ qr :List of 4 ..$ qr : num [1:28, 1:3] -5.292 0.189 0.189 0.189 0.189 ... .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:28] "1" "2" "3" "4" ... .. .. ..$ : chr [1:3] "(Intercept)" "bare.sand" "fallen.leaves" ..$ rank : int 3 ..$ qraux: num [1:3] 1.19 1.11 1.18 ..$ pivot: int [1:3] 1 2 3 ..- attr(*, "class")= chr "qr" $ show.coef : logi FALSE $ show.fitted : logi FALSE $ show.residuals : logi FALSE $ offset : num [1:28, 1:12] 0 0 0 0 0 0 0 0 0 0 ... - attr(*, "class")= chr [1:2] "manyglm" "mglm" > str(an.spid) List of 11 $ family : chr "negative.binomial" $ p.uni : chr "adjusted" $ test : chr "Dev" $ cor.type : chr "I" $ resamp : chr "pit.trap" $ nBoot : num 1000 $ shrink.param: num [1:3] 0 0 0 $ n.bootsdone : num 999 $ table :'data.frame': 3 obs. of 4 variables: ..$ Res.Df : int [1:3] 27 26 25 ..$ Df.diff : num [1:3] NA 1 1 ..$ Dev : num [1:3] NA 70.4 70.3 ..$ Pr(>Dev): num [1:3] NA 0.002 0.007 ..- attr(*, "heading")= chr [1:3] "Analysis of Deviance Table\n" "Model: manyglm(formula = spiddat ~ bare.sand + fallen.leaves, family = \"negative.binomial\", " "Model: data = X)" ..- attr(*, "title")= chr "\nMultivariate test:\n" $ uni.p : num [1:3, 1:12] NA 0.568 0.001 NA 0.485 0.988 NA 0.001 0.074 NA ... ..- attr(*, "title")= chr "\nUnivariate Tests:\n" ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:3] "(Intercept)" "bare.sand" "fallen.leaves" .. ..$ : chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... $ uni.test : num [1:3, 1:12] NA 1.22 27.64 NA 2.42 ... ..- attr(*, "title")= chr "\nUnivariate Tests:\n" ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:3] "(Intercept)" "bare.sand" "fallen.leaves" .. ..$ : chr [1:12] "Alopacce" "Alopcune" "Alopfabr" "Arctlute" ... - attr(*, "class")= chr "anova.manyglm" -- Kendra Maas, Ph.D. Post Doctoral Research Fellow University of British Columbia 604-822-5646 _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
