[R-sig-eco] envfit and NMDS
Hello list, I commonly see envfit used for NMDS, and am curious if envfit is considered a non-metric vector fitting tool. This question came up during a conversation with a colleague who only uses envfit with PCoA, because they are concerned that to do this would be problematic for the same reason you are not supposed to correlate environmental variables with NMDS axes (you can't correlate something that's non-metric with a metric variable). To me, it seems like by projecting the metric variable into non-metric space, you're essentially making it non-metric, and the correlation would be fine. If anyone could weigh in and clear up the confusion, that would be great. Thanks, Erin ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] envfit and NMDS
Hi Jari, Thanks for your response -- I will definitely be forwarding this on to my colleagues! Cheers, Erin On Apr 24, 2013, at 2:47 AM, Jari Oksanen wrote: Howdy folks, This is the second time this week we have this issue. There are two (or three) separate points: (1) You should not correlate environmental variables with axes in any ordination method. This applies to PCoA, PCA, CA or anything else just as well as to NMDS. You can see this by fitting the vectors: they are rarely parallel to the axes. Even in CCA/RDA, the vectors for constraints are rarely parallel to the axes. (2) The ordination space in NMDS is metric. The non-metric part is the monotonic (non-metric) regression from metric ordination space to observed dissimilarities. The observed dissimilarities between sampling units (plots, sites) need not be metric, but they can be semimetric or non-metric, but the ordination space derived from them is metric. (3) As a separate issue, it is often better to use fitted surfaces than fitted vectors. Fitted vectors are appropriate when the fitted surface is a plane (first degree linear trend surface). This is rarely the case, and this applies to all ordination methods: the fitted surfaces in CA, PCoA or PCA are usually just as non-planar as in NMDS. For point 2: Look at the stressplot(NMDS-result). Here horizontal axis gives Euclidean distances in NMDS space -- these are metric. The vertical axis gives the observed dissimilarities -- these can be anything. The fit lines gives the monotonic regression -- this is non-metric. With vegan::metaMDS() the ordination space is not only metric, but it is strictly Euclidean. We do and we can rotate the ordination space. As a historic note, the vector fitting code for vegan was based on a Bell Labs document that describes vector fitting for their NMDS (KYST software). The Bell folks invented NMDS, and they regarded vector fitting suitable for NMDS from the very beginning. That is, form 1960s. Cheers, Jari Oksanen From: r-sig-ecology-boun...@r-project.org [r-sig-ecology-boun...@r-project.org] on behalf of Erin Nuccio [enuc...@gmail.com] Sent: 24 April 2013 12:30 To: r-sig-ecology@r-project.org Subject: [R-sig-eco] envfit and NMDS Hello list, I commonly see envfit used for NMDS, and am curious if envfit is considered a non-metric vector fitting tool. This question came up during a conversation with a colleague who only uses envfit with PCoA, because they are concerned that to do this would be problematic for the same reason you are not supposed to correlate environmental variables with NMDS axes (you can't correlate something that's non-metric with a metric variable). To me, it seems like by projecting the metric variable into non-metric space, you're essentially making it non-metric, and the correlation would be fine. If anyone could weigh in and clear up the confusion, that would be great. Thanks, Erin ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Adonis and Random Effects
Hi Steve, You mentioned that nested.npmanova won't test GrasslandPlot correctly for a split-plot design. However, does adonis test GrasslandPlot correctly, since it's using the split-plot error term to test all effects? Here are the formulas again. adonis(community_dist ~ Grassland*Treatment + GrasslandPlot, strata = GrasslandPlot) nested.npmanova(community_dist ~ Grassland + GrasslandPlot) Thank you, Erin On Mar 10, 2013, at 8:17 AM, JOHN S BREWER wrote: Erin, Please check the February 25 post I made called Permanova with nested data. It explains how to test whole plot and split plot effects correctly in adonis. But to answer your question, even if you treat Grassland as a fixed-plot effect (which seems perfectly reasonable), Grassland is a whole-plot effect. Using the model formula given and strata, adonis uses the split-plot error term (i.e., the residual error term) to test all effects. That's wrong because Grassland needs to be tested with the whole-plot error term. In the post I referred to, I describe how you can do a separate test for the whole plot using the BiodiversityR package and the nested.npmanova function. In this case, you would only include Grassland and GrasslandPlot as terms in the model. It's just doing a two-way nested manova. The whole-plot effect of Grassland will be tested correctly using the GrasslandPlot term. GrasslandPlot will be tested with the residual error term, which will be wrong, but you can ! ignore that. I've tried it with my own data and it works. One cautionary note. See the posts by Jari Oksanen and others about the versions of BiodiversityR and R used. Hope this helps Steve From: Erin Nuccio [enuc...@gmail.com] Sent: Saturday, March 09, 2013 9:09 PM To: JOHN S BREWER Cc: r-sig-ecology@r-project.org Subject: Re: [R-sig-eco] Adonis and Random Effects Hi Steve and R list, I was hoping you could clarify something you mentioned in previous post. A quick recap... I have a split-plot design where I determined the microbial communities at 3 grasslands (see post script for design). I am trying quantify the how much of my community can be explained by Treatment or Grassland effect. After talking with a statistician, it seems like treating Grassland as a Fixed effect would be reasonable (because I have such a small number of grasslands). You mentioned that if I treat Grassland as a Fixed effect, and use the following formula, the Grassland effect would not be tested correctly: adonis(formula = community_distance_matrix ~ Treatment*Grassland + GrasslandPlot, strata = GrasslandPlot) Why is this? Is there any way to remedy this? Thanks for your feedback, Erin Experimental design: 4 split plots * 2 Treatments * 3 Grasslands = 24 observations Treatment: 2 levels (each within 1 split plot) Grassland: 3 levels GrasslandPlot: 12 levels (4 split plots nested in 3 Grasslands) On Feb 4, 2013, at 6:22 AM, Steve Brewer wrote: Erin, There have been a lot of similar queries (e.g., repeated measures, nested permanova). Jari can correct me if I am wrong, but as far as I know, no one has developed a way to define multiple error terms in adonis. You can use adonis, however, to get the split-plot effects. If you want to make a grassland a random effect, use the following statement adonis(formula = community_distance_matrix ~ Treatment + Grassland + GrasslandPlot, strata = GrasslandPlot) The treatment effect will be correct because the residual error term (which is equivalent to treatment x GrasslandPlot interaction nested within Grassland) is the correct error term. The Grassland effect, however, will not be tested correctly because it is using the residual error term when it should be using GrasslandPLot as the error term. You can determine what the F stat for Grassland should be, however, using the Ms Grassland and MS GrasslandPlot from the anova table to construct the F test. You just won't get a p-value for the test. If you want to treat Grassland as a fixed effect, the model is similar but defines the interaction adonis(formula = community_distance_matrix ~ Treatment*Grassland + GrasslandPlot, strata = GrasslandPlot) In this case, the treatment x grassland interaction will be tested correctly, as will the treatment effect, but not the Grassland effect. Unfortunately, you cannot just take averages of abundances across the treatment and control in each plot and then do a separate analysis of Grassland and GrasslandPLot (unless you're using Euclidean distances). I suspect you're not using Euclidean distances. Hope this helps some. Good luck, Steve J. Stephen Brewer Professor Department of Biology PO Box 1848 University of Mississippi University, Mississippi 38677-1848 Brewer web page - http://home.olemiss.edu/~jbrewer/ FAX - 662-915-5144 Phone - 662-915-1077
Re: [R-sig-eco] nested.npmanova -- distance matrices as input?
Hi Jari, Yes, my distance matrix is of class dist, so it sounds like my unifrac dissimilarities were handled correctly. Thank you! Erin On Mar 11, 2013, at 12:57 AM, Jari Oksanen wrote: Erin, R is open source: you can see the source code if in doubt. Looking at the source code, the situation is a bit unclear and depends on the details of your data that I don't know. It seems that nested.npmanova() accepst R distances structures of class dist. If your Unifrac distances inherit from dist class, nested.npmanova(), they will be handled correctly in nested.npmanova. If they are, like your write, distance matrices, then they will be handled incorrectly: they are accepted silently but treated like they were raw data matrices. You should get an error message from vegdist() in that case, as it knows no method = FALSE, so it may be that your dissimilarities were correctly handled. However, we don't know as we even do not know what software (R package, external software) you used in calculating unifrac dissimilarities. If 'd' are your distances, see what does class(d) say. If says dist (possibly with some other alternatives), you are safe. If your 'd' are not of class dist, you can try if as.dist(d) changes them to dist. Cheers, Jari Oksanen From: r-sig-ecology-boun...@r-project.org [r-sig-ecology-boun...@r-project.org] on behalf of Erin Nuccio [enuc...@gmail.com] Sent: 11 March 2013 07:08 To: r-sig-ecology@r-project.org Subject: [R-sig-eco] nested.npmanova -- distance matrices as input? Hello list, Does anyone know nested.npmanova can take distance matrices as input? When I read the helpfile, it specifies that the input for nested.npmanova is a data frame, and sounds like distance matrices can only be used for nested.anova.dbrda (see below). However, if I try inputting a Unifrac distance matrix, and make method = FALSE, it completes without any errors. Did this complete correctly? formulaFormula with a community data frame (with sites as rows, species as columns and species abundance as cell values) or (for nested.anova.dbrda only) distance matrix on the left-hand side and two categorical variables on the right-hand side (with the second variable assumed to be nested within the first). Thank you, Erin ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Adonis and Random Effects
Thanks Steve, that is helpful. However, I've run into a small problem with nested.npmanova. It appears that I cannot supply my own distance matrix, and need to supply the raw species data. I am using Unifrac distances, which is not an option for vegdist. Anyone know if there is a workaround here? I did compare nested.npmanova to adonis with bray distance using the same model (community_data ~ Grassland + GrasslandPlot), and it looks like the F values are similar for Grassland (F values: 3.6 vs. 3.3), and the same for GrasslandPlot. The R2 values seem to stay the same no matter what I do in adonis, and the p values are all ~ 0.001. So, in case there is no way to use Unifrac distances with nested.npmanova, my backup plan would be to perform two adonis functions, and use the second function to get the approximate F value for Grassland and correct F value for GrasslandPlot: adonis(community_data ~ Treatment*Grassland + GrasslandPlot, strata=GrasslandPlot) adonis(community_data ~ Grassland + GrasslandPlot, strata=GrasslandPlot) Does this seem reasonable? Of course, the best thing would be to use the Unifrac distances with nested.npmanova if it's possible. Thank you, Erin On Mar 10, 2013, at 8:17 AM, JOHN S BREWER wrote: Erin, Please check the February 25 post I made called Permanova with nested data. It explains how to test whole plot and split plot effects correctly in adonis. But to answer your question, even if you treat Grassland as a fixed-plot effect (which seems perfectly reasonable), Grassland is a whole-plot effect. Using the model formula given and strata, adonis uses the split-plot error term (i.e., the residual error term) to test all effects. That's wrong because Grassland needs to be tested with the whole-plot error term. In the post I referred to, I describe how you can do a separate test for the whole plot using the BiodiversityR package and the nested.npmanova function. In this case, you would only include Grassland and GrasslandPlot as terms in the model. It's just doing a two-way nested manova. The whole-plot effect of Grassland will be tested correctly using the GrasslandPlot term. GrasslandPlot will be tested with the residual error term, which will be wrong, but you can ! ignore that. I've tried it with my own data and it works. One cautionary note. See the posts by Jari Oksanen and others about the versions of BiodiversityR and R used. Hope this helps Steve From: Erin Nuccio [enuc...@gmail.com] Sent: Saturday, March 09, 2013 9:09 PM To: JOHN S BREWER Cc: r-sig-ecology@r-project.org Subject: Re: [R-sig-eco] Adonis and Random Effects Hi Steve and R list, I was hoping you could clarify something you mentioned in previous post. A quick recap... I have a split-plot design where I determined the microbial communities at 3 grasslands (see post script for design). I am trying quantify the how much of my community can be explained by Treatment or Grassland effect. After talking with a statistician, it seems like treating Grassland as a Fixed effect would be reasonable (because I have such a small number of grasslands). You mentioned that if I treat Grassland as a Fixed effect, and use the following formula, the Grassland effect would not be tested correctly: adonis(formula = community_distance_matrix ~ Treatment*Grassland + GrasslandPlot, strata = GrasslandPlot) Why is this? Is there any way to remedy this? Thanks for your feedback, Erin Experimental design: 4 split plots * 2 Treatments * 3 Grasslands = 24 observations Treatment: 2 levels (each within 1 split plot) Grassland: 3 levels GrasslandPlot: 12 levels (4 split plots nested in 3 Grasslands) On Feb 4, 2013, at 6:22 AM, Steve Brewer wrote: Erin, There have been a lot of similar queries (e.g., repeated measures, nested permanova). Jari can correct me if I am wrong, but as far as I know, no one has developed a way to define multiple error terms in adonis. You can use adonis, however, to get the split-plot effects. If you want to make a grassland a random effect, use the following statement adonis(formula = community_distance_matrix ~ Treatment + Grassland + GrasslandPlot, strata = GrasslandPlot) The treatment effect will be correct because the residual error term (which is equivalent to treatment x GrasslandPlot interaction nested within Grassland) is the correct error term. The Grassland effect, however, will not be tested correctly because it is using the residual error term when it should be using GrasslandPLot as the error term. You can determine what the F stat for Grassland should be, however, using the Ms Grassland and MS GrasslandPlot from the anova table to construct the F test. You just won't get a p-value for the test. If you want to treat Grassland as a fixed effect, the model is similar but defines the interaction adonis
Re: [R-sig-eco] Adonis and Random Effects
Hi again, OK, figuring out if it's possible to use Unifrac with nested.npmanova may be necessary I just realized my test comparing nested.npmanova and adonis on the same model had no strata for adonis. When I add the strata GrasslandPlot to adonis, my p values are equal to 1. So adonis with no strata gives me similar values to nested.npmanova for the following model: community_data ~ Grassland + GrasslandPlot. So, (community_data ~ Grassland + GrasslandPlot) approximates the correct statistics, but since this ignores all strata, I'm not sure if it's justified. Thoughts? Thanks, Erin On Mar 10, 2013, at 3:42 PM, Erin Nuccio wrote: Thanks Steve, that is helpful. However, I've run into a small problem with nested.npmanova. It appears that I cannot supply my own distance matrix, and need to supply the raw species data. I am using Unifrac distances, which is not an option for vegdist. Anyone know if there is a workaround here? I did compare nested.npmanova to adonis with bray distance using the same model (community_data ~ Grassland + GrasslandPlot), and it looks like the F values are similar for Grassland (F values: 3.6 vs. 3.3), and the same for GrasslandPlot. The R2 values seem to stay the same no matter what I do in adonis, and the p values are all ~ 0.001. So, in case there is no way to use Unifrac distances with nested.npmanova, my backup plan would be to perform two adonis functions, and use the second function to get the approximate F value for Grassland and correct F value for GrasslandPlot: adonis(community_data ~ Treatment*Grassland + GrasslandPlot, strata=GrasslandPlot) adonis(community_data ~ Grassland + GrasslandPlot, strata=GrasslandPlot) Does this seem reasonable? Of course, the best thing would be to use the Unifrac distances with nested.npmanova if it's possible. Thank you, Erin On Mar 10, 2013, at 8:17 AM, JOHN S BREWER wrote: Erin, Please check the February 25 post I made called Permanova with nested data. It explains how to test whole plot and split plot effects correctly in adonis. But to answer your question, even if you treat Grassland as a fixed-plot effect (which seems perfectly reasonable), Grassland is a whole-plot effect. Using the model formula given and strata, adonis uses the split-plot error term (i.e., the residual error term) to test all effects. That's wrong because Grassland needs to be tested with the whole-plot error term. In the post I referred to, I describe how you can do a separate test for the whole plot using the BiodiversityR package and the nested.npmanova function. In this case, you would only include Grassland and GrasslandPlot as terms in the model. It's just doing a two-way nested manova. The whole-plot effect of Grassland will be tested correctly using the GrasslandPlot term. GrasslandPlot will be tested with the residual error term, which will be wrong, but you can! ignore that. I've tried it with my own data and it works. One cautionary note. See the posts by Jari Oksanen and others about the versions of BiodiversityR and R used. Hope this helps Steve From: Erin Nuccio [enuc...@gmail.com] Sent: Saturday, March 09, 2013 9:09 PM To: JOHN S BREWER Cc: r-sig-ecology@r-project.org Subject: Re: [R-sig-eco] Adonis and Random Effects Hi Steve and R list, I was hoping you could clarify something you mentioned in previous post. A quick recap... I have a split-plot design where I determined the microbial communities at 3 grasslands (see post script for design). I am trying quantify the how much of my community can be explained by Treatment or Grassland effect. After talking with a statistician, it seems like treating Grassland as a Fixed effect would be reasonable (because I have such a small number of grasslands). You mentioned that if I treat Grassland as a Fixed effect, and use the following formula, the Grassland effect would not be tested correctly: adonis(formula = community_distance_matrix ~ Treatment*Grassland + GrasslandPlot, strata = GrasslandPlot) Why is this? Is there any way to remedy this? Thanks for your feedback, Erin Experimental design: 4 split plots * 2 Treatments * 3 Grasslands = 24 observations Treatment: 2 levels (each within 1 split plot) Grassland: 3 levels GrasslandPlot: 12 levels (4 split plots nested in 3 Grasslands) On Feb 4, 2013, at 6:22 AM, Steve Brewer wrote: Erin, There have been a lot of similar queries (e.g., repeated measures, nested permanova). Jari can correct me if I am wrong, but as far as I know, no one has developed a way to define multiple error terms in adonis. You can use adonis, however, to get the split-plot effects. If you want to make a grassland a random effect, use the following statement adonis(formula = community_distance_matrix ~ Treatment + Grassland + GrasslandPlot
[R-sig-eco] nested.npmanova -- distance matrices as input?
Hello list, Does anyone know nested.npmanova can take distance matrices as input? When I read the helpfile, it specifies that the input for nested.npmanova is a data frame, and sounds like distance matrices can only be used for nested.anova.dbrda (see below). However, if I try inputting a Unifrac distance matrix, and make method = FALSE, it completes without any errors. Did this complete correctly? formulaFormula with a community data frame (with sites as rows, species as columns and species abundance as cell values) or (for nested.anova.dbrda only) distance matrix on the left-hand side and two categorical variables on the right-hand side (with the second variable assumed to be nested within the first). Thank you, Erin ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Adonis and Random Effects
Hi Steve and R list, I was hoping you could clarify something you mentioned in previous post. A quick recap... I have a split-plot design where I determined the microbial communities at 3 grasslands (see post script for design). I am trying quantify the how much of my community can be explained by Treatment or Grassland effect. After talking with a statistician, it seems like treating Grassland as a Fixed effect would be reasonable (because I have such a small number of grasslands). You mentioned that if I treat Grassland as a Fixed effect, and use the following formula, the Grassland effect would not be tested correctly: adonis(formula = community_distance_matrix ~ Treatment*Grassland + GrasslandPlot, strata = GrasslandPlot) Why is this? Is there any way to remedy this? Thanks for your feedback, Erin Experimental design: 4 split plots * 2 Treatments * 3 Grasslands = 24 observations Treatment: 2 levels (each within 1 split plot) Grassland: 3 levels GrasslandPlot: 12 levels (4 split plots nested in 3 Grasslands) On Feb 4, 2013, at 6:22 AM, Steve Brewer wrote: Erin, There have been a lot of similar queries (e.g., repeated measures, nested permanova). Jari can correct me if I am wrong, but as far as I know, no one has developed a way to define multiple error terms in adonis. You can use adonis, however, to get the split-plot effects. If you want to make a grassland a random effect, use the following statement adonis(formula = community_distance_matrix ~ Treatment + Grassland + GrasslandPlot, strata = GrasslandPlot) The treatment effect will be correct because the residual error term (which is equivalent to treatment x GrasslandPlot interaction nested within Grassland) is the correct error term. The Grassland effect, however, will not be tested correctly because it is using the residual error term when it should be using GrasslandPLot as the error term. You can determine what the F stat for Grassland should be, however, using the Ms Grassland and MS GrasslandPlot from the anova table to construct the F test. You just won't get a p-value for the test. If you want to treat Grassland as a fixed effect, the model is similar but defines the interaction adonis(formula = community_distance_matrix ~ Treatment*Grassland + GrasslandPlot, strata = GrasslandPlot) In this case, the treatment x grassland interaction will be tested correctly, as will the treatment effect, but not the Grassland effect. Unfortunately, you cannot just take averages of abundances across the treatment and control in each plot and then do a separate analysis of Grassland and GrasslandPLot (unless you're using Euclidean distances). I suspect you're not using Euclidean distances. Hope this helps some. Good luck, Steve J. Stephen Brewer Professor Department of Biology PO Box 1848 University of Mississippi University, Mississippi 38677-1848 Brewer web page - http://home.olemiss.edu/~jbrewer/ FAX - 662-915-5144 Phone - 662-915-1077 On 2/4/13 1:14 AM, Erin Nuccio enuc...@gmail.com wrote: Hello List, Is adonis capable of modeling random effects? I'm analyzing the impact of a treatment on the microbial community in a split-plot design (2 treatments per plot, 4 plots per grassland, 3 grasslands total). I would like to quantify how much of the variance is due to the Treatment versus the Grassland. It seems like Grassland should be a random effect, since there are thousands of grasslands, and I'm only looking at 3. Thanks for your help, Erin Here are my factors: 'data.frame':24 obs. of 4 variables: $ Treatment: Factor w/ 2 levels T1,T2: 1 1 1 1 1 2 2 2 1 1 ... $ Grassland: Factor w/ 3 levels G1,G2,G3: 3 3 1 1 1 2 2 1 2 2 ... $ Plot : Factor w/ 4 levels P1,P2,P3,P4: 1 2 2 3 4 1 3 2 1 2 ... $ GrasslandPlot: Factor w/ 12 levels G1:P1,G1:P2,G1:P3..: 9 10 2 3 4 5 7 2 5 6 ... And here's the error message: Error in `contrasts-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels In addition: Warning messages: 1: In Ops.factor(1, Grassland) : | not meaningful for factors 2: In Ops.factor(1, GrasslandPlot) : | not meaningful for factors ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Adonis and Random Effects
Thanks Steve and Jari for your responses. I really do appreciate it -- trying to analyze this data has been a challenge! Based on both your responses, it seems like I won't be able to get adonis to do the appropriate tests for the Grassland effect (since I do not know how to design custom permutation matrices, as Jari mentioned). Steve you're correct that I'm not using Euclidean distances (I'm using Unifrac distances). Since I'm mainly interested in the amount of variance explained by each variable, would it be worthwhile to explore using varpart? I can convert Grassland into a distance matrix (pairwise distances between sites), and I would guess that I would have to turn Treatment into a dummy variable. I also have additional environmental data that I ignored with my attempt at adonis. Many thanks for your input, Erin On Feb 4, 2013, at 6:22 AM, Steve Brewer jbre...@olemiss.edu wrote: Erin, There have been a lot of similar queries (e.g., repeated measures, nested permanova). Jari can correct me if I am wrong, but as far as I know, no one has developed a way to define multiple error terms in adonis. You can use adonis, however, to get the split-plot effects. If you want to make a grassland a random effect, use the following statement adonis(formula = community_distance_matrix ~ Treatment + Grassland + GrasslandPlot, strata = GrasslandPlot) The treatment effect will be correct because the residual error term (which is equivalent to treatment x GrasslandPlot interaction nested within Grassland) is the correct error term. The Grassland effect, however, will not be tested correctly because it is using the residual error term when it should be using GrasslandPLot as the error term. You can determine what the F stat for Grassland should be, however, using the Ms Grassland and MS GrasslandPlot from the anova table to construct the F test. You just won't get a p-value for the test. If you want to treat Grassland as a fixed effect, the model is similar but defines the interaction adonis(formula = community_distance_matrix ~ Treatment*Grassland + GrasslandPlot, strata = GrasslandPlot) In this case, the treatment x grassland interaction will be tested correctly, as will the treatment effect, but not the Grassland effect. Unfortunately, you cannot just take averages of abundances across the treatment and control in each plot and then do a separate analysis of Grassland and GrasslandPLot (unless you're using Euclidean distances). I suspect you're not using Euclidean distances. Hope this helps some. Good luck, Steve J. Stephen Brewer Professor Department of Biology PO Box 1848 University of Mississippi University, Mississippi 38677-1848 Brewer web page - http://home.olemiss.edu/~jbrewer/ FAX - 662-915-5144 Phone - 662-915-1077 On 2/4/13 1:14 AM, Erin Nuccio enuc...@gmail.com wrote: Hello List, Is adonis capable of modeling random effects? I'm analyzing the impact of a treatment on the microbial community in a split-plot design (2 treatments per plot, 4 plots per grassland, 3 grasslands total). I would like to quantify how much of the variance is due to the Treatment versus the Grassland. It seems like Grassland should be a random effect, since there are thousands of grasslands, and I'm only looking at 3. I have tried to use the notation that works with lme4, and it's not working for me (see below for formula and error messages). If adonis can't do random effects, are there any alternatives? Or, considering my goal, are there any other programs I should look into? Any suggestions would be highly appreciated! Thanks for your help, Erin Here's what I think I should run: adonis(formula = community_distance_matrix ~ Treatment + (1|Grassland) + (1|GrasslandPlot), strata = GrasslandPlot) Here are my factors: 'data.frame':24 obs. of 4 variables: $ Treatment: Factor w/ 2 levels T1,T2: 1 1 1 1 1 2 2 2 1 1 ... $ Grassland: Factor w/ 3 levels G1,G2,G3: 3 3 1 1 1 2 2 1 2 2 ... $ Plot : Factor w/ 4 levels P1,P2,P3,P4: 1 2 2 3 4 1 3 2 1 2 ... $ GrasslandPlot: Factor w/ 12 levels G1:P1,G1:P2,G1:P3..: 9 10 2 3 4 5 7 2 5 6 ... And here's the error message: Error in `contrasts-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels In addition: Warning messages: 1: In Ops.factor(1, Grassland) : | not meaningful for factors 2: In Ops.factor(1, GrasslandPlot) : | not meaningful for factors ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
[R-sig-eco] Adonis and Random Effects
Hello List, Is adonis capable of modeling random effects? I'm analyzing the impact of a treatment on the microbial community in a split-plot design (2 treatments per plot, 4 plots per grassland, 3 grasslands total). I would like to quantify how much of the variance is due to the Treatment versus the Grassland. It seems like Grassland should be a random effect, since there are thousands of grasslands, and I'm only looking at 3. I have tried to use the notation that works with lme4, and it's not working for me (see below for formula and error messages). If adonis can't do random effects, are there any alternatives? Or, considering my goal, are there any other programs I should look into? Any suggestions would be highly appreciated! Thanks for your help, Erin Here's what I think I should run: adonis(formula = community_distance_matrix ~ Treatment + (1|Grassland) + (1|GrasslandPlot), strata = GrasslandPlot) Here are my factors: 'data.frame': 24 obs. of 4 variables: $ Treatment: Factor w/ 2 levels T1,T2: 1 1 1 1 1 2 2 2 1 1 ... $ Grassland: Factor w/ 3 levels G1,G2,G3: 3 3 1 1 1 2 2 1 2 2 ... $ Plot : Factor w/ 4 levels P1,P2,P3,P4: 1 2 2 3 4 1 3 2 1 2 ... $ GrasslandPlot: Factor w/ 12 levels G1:P1,G1:P2,G1:P3..: 9 10 2 3 4 5 7 2 5 6 ... And here's the error message: Error in `contrasts-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels In addition: Warning messages: 1: In Ops.factor(1, Grassland) : | not meaningful for factors 2: In Ops.factor(1, GrasslandPlot) : | not meaningful for factors ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
