[R-sig-eco] measure of segregation
The code below shows a sample of point locations of Brent Goose on mudflats.
The black points are the subspecies Dark-bellied Brent Goose, and white
points the subspecies Light-bellied Brent Goose:
geese - data.frame(lightLat=c(55.66735, 55.66735, 55.67341, 55.66735,
55.66735),
lightLong=c(-1.833845,-1.833844,-1.833843,-1.833841,-1.833840),
darkLat=c(55.66735,55.66735,55.66735,55.66735,55.61628),
darkLong=c(-1.833851,-1.833851,-1.833848,-1.833850,-1.833849))
library(ggplot2)
ggplot(geese, aes(lightLong, lightLat)) + geom_point(size=5, color='white')
+
geom_point(data=data.frame(geese), aes(darkLong, darkLat), color='black',
size=5) +
xlab('long') +
ylab('lat')
The two subspecies segregate, as can be seen. I¹m looking for a statistical
measure of segregation? Can anyone suggest a suitable measure?
Thanks
Ross
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Re: [R-sig-eco] measure of segregation
Hello,
This looks like a job for point pattern statistics. Have a look at the
spatstat package and the related docs. There are functions (pcfcross
e.g.) to compare the spatial distribution of marked point patterns (in
your case the marks will be species name).
Make sure that point pattern models can be applied to your data set
first (and check whether you need to account for environmental
heterogeneity, i.e. first order processes).
HTH
Alex
P.S.: you can find a handy tutorials on the web at www.spatstat.org ...
On 10/03/2013 15:24, Ross Ahmed wrote:
The code below shows a sample of point locations of Brent Goose on mudflats.
The black points are the subspecies Dark-bellied Brent Goose, and white
points the subspecies Light-bellied Brent Goose:
geese - data.frame(lightLat=c(55.66735, 55.66735, 55.67341, 55.66735,
55.66735),
lightLong=c(-1.833845,-1.833844,-1.833843,-1.833841,-1.833840),
darkLat=c(55.66735,55.66735,55.66735,55.66735,55.61628),
darkLong=c(-1.833851,-1.833851,-1.833848,-1.833850,-1.833849))
library(ggplot2)
ggplot(geese, aes(lightLong, lightLat)) + geom_point(size=5, color='white')
+
geom_point(data=data.frame(geese), aes(darkLong, darkLat), color='black',
size=5) +
xlab('long') +
ylab('lat')
The two subspecies segregate, as can be seen. I¹m looking for a statistical
measure of segregation? Can anyone suggest a suitable measure?
Thanks
Ross
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--
Alexandre Villers, PhD.
Spatial Ecology Population Dynamics
Section of Ecology, Department of Biology
University of Turku
FIN-20014 Turku
Finland
@mail: alexandre.vill...@utu.fi
phone: 00358 (0)2 333 5039
web page http://vanha.sci.utu.fi/biologia/ekologia/villers_eng.htm
*Use open source and free softwares*
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Re: [R-sig-eco] Adonis and Random Effects
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 ig! nore 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 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:
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
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
