Questions related to vegans
I am able to run a one-way ANOSIM test in R (vegan package) but I do not know how to carry out a two-way ANOSIM. Here an example for my one-way test for factor Year on my dataset:
- results= anosim(dataset, dune$Year, distance = "bray", permutations = 9999)
I wish to include a second factor, e. g. Zone, for the two-way analysis.
Can anyone help me on that?
Thanks in advance!
Regarding the connection between Beha'alotcha story of quail and pandemic COVID-19 punishment from G-d to go vegan is there any Vegan Rabbi who might have asked whether or not there is a connection, or who might like to answer the question since the plague was fairly recent and they might not have noticed the significance of this punishment and just like there having been ten plagues in Egypt, maybe we haven't yet experienced the potential "worst?" plague yet, and more are to come? Can WE do something to avert this imminent prediction or just go back to business as usual? Are we paying heed to G-d's warning and are we to take this seriously and stop being punished when we finally learn our lesson? Does any Rabbi wish to be interviewed on this topic within a reasonable period of time from the posting of this question? In a mere four-days'-time I shall give a PowerPoint presentation at Synagogue on this very topic, on the story of the quail in the Beha'alotcha Torah Portion and wish to make the above mentioned points! Thanks for responding.
I am facing negative estimates of components of variation (ECV) and high p-values for some terms in a PERMANOVA analysis (working with Primer-e v6 here). According to PERMANOVA+ manual, page 54 (attached), this is an issue in nested designs that might be solved by sequentially pooling these terms when re-doing the analysis. However, this "pooling" option that appears in the manual (Fig. 1.32 in page 58) is not available for me when I try to make the analysis and I can only see the "exclude terms" option (see attached picture). This is unfortunate because, according to the manual, the correct way to go would be pooling rather than excluding. Did any of you have the same issue? do you know if I can do this in Primer-e v.7 or using the Vegan package in R?
Thank you very much in advance for your help.
Together with evidence on the importance of sufficiently high levels of 25-OH-vitamin D for the optimization of functioning in all parameters of the immune system, clinicians observed that
-- transitorily even higher doses are needed in acute peroids of illness/inflammation,
-- transitorily even higher doses are needed when lymphocytes are active in detoxification from metals, parasites, nanotech such as ribbons or other graphene based structures, circulating spike proteins etc. etc,
-- lhigher doses are or may be needed when there is a ack of complementary micronutrients such as magnesium, vit. K2
and similar factors.
In addition to those observations, questions regarding the impact of vitamin D quality emerged:
Do vegan production, duration of storage and othe parameters of quality impact bioavailabilty?
Hi everyone! I hope you all are fine. I have tried many tutorials regarding the calculation of alpha and beta diversity, however, my RStudio is throwing up so many issues. Packages aren't getting installed due to some compatibility issues. That's another discussion. However, may I request you all to kindly guide me in calculating these diversity indices?
What R script should I follow to calculate the Alpha and Beta- diversity indices. People have told me to use vegan, but how to go about it?
P.S. My input taxonomic data comes from Kraken2, so I have sample_kraken_report.txt for all my samples.
In the context of social change, veganism is becoming more and more common? On the other hand, it is a risk factor for eating disorders. Researchers in the field of eating disorders, veganism is an exclusion criterion for you in patients and control subjects. What is your position on this issue?
I am comparing the differences in microbiome community composition between animals according to three different living sites (individuals=2, samplings=5; individuals=2, samplings=16; individuals=3, samplings=17). To avoid repeating individual sampling effect, I set individual as a random effect in model (R code in below: adonis2(spe ~ site, data=spe.env, permutations = 999, method="bray", strata = spe.env$Individual)). Looking at a PCoA plot, the dispersion of the site BJ group is obviously different from the two others, and comparing bray-curtis distances with the vegan::adonis() function yields a high R squared and a non-significant p-value. Must I accept the null hypothesis that the sites are non-homogeneous?
would like to learn more about the socio-cultural clusters of Vegans and Vegetarians in Western Societies.
A breakdown of motivations e.g. climate, ethics, health, etc. would be great. Has it got the potential to go mainstream, given the anthropogenic idiosyncrasies in Western Culture and Rituals?
Cherish your feedback.
I am investigating the shared pollinator communities of a native wildflower, and a related invasive hybrid. The hybrid is replacing the native plant. I have collected data which lists all insect visitors to both plants. I want to see if the bee communities between plants are significantly different. I have tried using nmds in vegan and mvabund in R, but it doesn't seem to work when only comparing two things (for example it seems normally multiple sites are species are compared). How might I go about comparing the assemblages? Secondly there have been some interesting results, for example bumble bees don't seem to visit one plant. How might I test if that is a significant result or more likely just a feature of my sampling.
Any help is very appreciated.
Hi everyone, I am currently trying to analyse communities on natural and artificial reefs, and I decided to use Permanova (Vegan :: Adonis 2) in R to do so. However, being the first time I am running this statistical test, I want to be careful with what I do, and therefore, I am asking for your help. I collected my community data (species at each site), and I would like to test whether reef Age, Depth, and Material (my independent variables) affect biodiversity. However, I do not have the same number of replicates for each group within variables (e.g. Age - young(10), middle(7), and old(5)). How can I adjust my code to account for the unbalanced design of my data? Here is the code I am using (but which I think assumes balanced design).
adonis.1<-adonis(species ~ data$Depth, model = c("raw"), permutations = 9999,
method = "bray", autotransform = FALSE)
Thank you in advance!
- About 25% of all the global climate change problems we are observing can be attributed to the food and the choices that we actually make on a daily basis. Different foods and diets have different carbon footprints. Livestock accounts for over 14% of global greenhouse gas emissions, which is that sort equal to transportation (cars, trucks, planes, trains and ships combined).
- We do not have to be Vegan; we can swap to chicken and fish, obviously. If we can just reduce our red meat intake a little bit, we can help the climate a lot.
- On another note, just switching to a Mediterranean diet can potentially solve 15% of global warming pollution by 2050.
I would like to perform an exploratory PCA with the vegan package (I am working with relative abundances of trnL sequences). From what I read, I can do it with the rda function, for which, if you don't include any other variables such as environmental variables, it works as a PCA. Is that correct? Are there other functions that allow me to do that?
And could you reccomend some paper/tutorial that I can read to better understand this kind of analysis?
Thank you very much!
There are different packages (vegan, BiodiversityR, vegetarian etc.) of R studio environment to conduct diversity analysis (Alpha, Beta, Gamma). hence I have interested to process my research data using these packages. but I have not clear information about the data format (excel sheet), which is applicable for those packages.
Could you help me by suggesting the ways with appropriate examples?
I am comparing the differences in microbiome community composition between animals according to three different dietary regimes: hay, green vegetation, and restricted feed. Looking at a PCoA plot, the centroid and dispersion of the restricted feed group is obviously different from the two others, and comparing bray-curtis distances with the vegan::adonis() function yields a high R squared and a significant p-value. The green vegetation and hay groups appear the same, but running an adonis test comparing these two groups yields a significant p-value (albeit a very low r squared value of 0.02). Must I reject the null hypothesis that the hay fed group and the green vegetation groups are homogeneous, despite the low r-squared value and the disagreement with the PCoA plot results?
I am computing incidence based richness estimators together with their standard error using began package in R . Now I want to evaluate the richness estimators using bias, precision and accuracy.
I'm having troubles in some data analysis.
I have Presence/Absence data from a few communities.
I've run a nMDS based on Jaccard index matrix using vegan package in R.
Further, I'd like to know if is possible to conduct a SIMPER like analysis in order to determine which species are contributing to the similarity/dissimilarities between communities.
I've read some discussions and Jaccard index are not used in SIMPER analysis.
There is any other analysis which I could perform that would give me this answer ??
Thanks for you attention.
The background: I do research on stomach contents and have a dataset with many stomachs as samples (rows of dataset) and abundance for several prey categories in the stomachs (columns of dataset). I can group my data for different factors (e.g. year, season, size-class etc.) for example to test for differences in diet composition between years. I am using the R 'vegan' package.
My question: When I run e.g. a PERMANOVA (in fact the adonis2 function from vegan) on the raw data, means several thousand stomachs as individual samples, I got high significances but also low R2 values as the high number of residuals 'spoil' the model. When I summarise the data and THEN perform the multivariate statistic, I got lower significances but also higher R2 values, which is desirable (as they explain the contribution to the model). The problem here is, that sometimes I have only 1 degree of freedom (e.g. comparing only to years with each other) and then the statistic doesn't work at all.
What would be the right way to do, when dealing with such data? Going for one or the other way of structuring the data? Or go for something completely different, e.g. Kruskal-ANOVA?
Many thanks for any suggestions.
I am really interested in the production of vegan “cheese” using a nut-free base, but including RBD coconut oil so an allergen free end product is achieved (coconut protein being a named allergen in USA). Although I have experience of dairy cheese making process & there is a vast array of technical information available relating to this, there seems to be a real shortage of accessible information regarding production of vegan “cheese” as this is relatively new technology.
If anybody has any knowledge of data sources, published research or similar, it would be much appreciated.
I use the 'vegan' function in the R package 'diveRsity' to calculate the Inbreeding coefficient of populations with SSR data. However, ten of twenty-six values are negative. If there is something wrong with analyze. Many thanks for your help.
I am trying to test the effects of Plant ID (Different flower colours) and Petal Type (each flower was divided into two different petal types) on 5 different pigment concentration measurements (5 response variables).
I used the adonis() function in the Vegan package:
adonis(apc[,4:8]~apc$Plant_ID, nperm=999, strata = apc$Petal_Type)
adonis(apc[,4:8]~apc$Petal_Type, nperm=999, strata = apc$Plant_ID)
Subsequently I checked for multivariate homogeneity of group dispersions with betadisper function and a permutation test for each of the factors. Both were not significant.
Now I want to see which levels within my factors differ between each other, I was thinking to use the pairwise.perm.manova() function (from the RVAideMemoire package) for that.
However, how will I then be able to see the effects of Plant ID and Petal Type on each of my individual 5 response variables? For example, I know that Plant_ID influences my response variables, but I want to see which of the response variables (different pigment types) differ between plant ID and or Petal types.
Anybody ideas about this issue?
When I using the rda function in r, I met this question. As you can see in the figure, there are a lot of deep blue points which refers to the species. But I would like remain only the sites point (samples). ggord is used because the 95% confidence interval circle for grouping is needed.
The R script is below:
asv1_vir <- decostand(t(asv_vir), "hellinger")
asv_vir.rda.f <- rda(asv1_vir ~., env_log10)
ggord(asv_vir.rda.f, grp_in=group,repel=TRUE, vec_ext=0.7, cols=c('red','cyan'), arrow=0.3,size=3, obslab=FALSE,ellipse = TRUE, hull = TRUE)+ theme_classic()
The species data I think will be asv_vir.rda.f$CCA$v.
Please feel free to have any discussions and suggestions!
Thank you for your time.
Hello Research Gate community,
I have a question about my interpretation of capscale() in the vegan R pachage and how to assess the variance explained by the interaction effect.
Imagine a significant model like this: Var1 + Var2 + Var1:Var2
> RsquareAdj(capscale(otu_table ~ Var1 + Var2 + Var1:Var2, metadata, distance = "bray"))$adj.r.squared
Then I can obtain the variance of the main factors
> RsquareAdj(capscale(otu_table ~ Var1 , metadata, distance = "bray"))$adj.r.squared
> RsquareAdj(capscale(otu_table ~ Var2, metadata, distance = "bray"))$adj.r.squared
Then, is this the right way to calculate the Adj.R2 for the interaction?
> RsquareAdj(capscale(otu_table~ Var1:Var2 + Condition(Var1) + Condition(Var2), metadata, distance = "bray"))$adj.r.squared
However, if I sum the variances altogether I do not get the variance explained by the full model
0.09308548 + 0.1270805 + 0.05174793 = 0.2719139
I looked online but I could not find any decent explanation of this.
Thank you for your help!
Vegan or be a pure vegetarian is ones independent choice for a healthy living. Some vegetarian are eagerly demanding that everyone should be"Vegan"for the betterment of our blue planet 'Earth'.But I opined that it's totally impossible.Because,if it will happen,then the natural relationship between Bio-geo-chemical cycle along with socioeconomic and socio-religious bonding might be disrupted and disturbed.
*Dear Researcher Community, please give a look on it and suggests best solutions to this concept and problem.
I have obtained the following Shannon indices from a trio of samples: 27.73 (healthy tissues), 24.55 (primary colon tumor), and 1.20 (metastatic tissues).
The indices have been obtained with the diversity function of R's package vegan.
How can I calculate whether the samples are statistically different?
For my thesis I have been collecting data concerning fish biomass, species richness/diversity among several marine habitats. I would like to perform a MVA to compare the species found at the 6 different marine habitats.
My plan was to perform a DCA and see whether I should continue with a PCA or a CCA based upon the rule of thumb introduced by Lepš & Šmilauer (2003) (to see whether the DCA axis is > 4 S.D. or < 3 S.D.). I suspect the outcome to be > 4 S.D. suggesting unimodal methods to be used next, in this case CCA.
I want to add the 6 different habitats as dummy variables (so K-1 is 5 categories represented by binary data). I am performing the analyses in R using the vegan package. I was wondering how to incorporate these dummy variables and in which steps of the analysis.
I now have rows of sites and columns with species counts (sites by species counts matrix). I added the 5 habitat categories as columns (so as the ones and zeros). Do I include the dummy variables for every step of the analysis (both DCA and CCA)? Does anyone have experience with this / some recommendations of papers?
Kind regards, Anne
I am having very inconsistent results performing ordination on my 'sample x sample' taxa abundance matrix from a 16S sequencing run. I annotated my sequences using SILVA (version 132), and have 1480 variables x 57 samples as my input. I am using 'vegan' with R and the function 'metaMDS()' to create a variable "ord" that will be used to create an NMDS plot. My code is as follows:
ord = metaMDS(data, distance="bray", k=2, trace = T, autotransform = F)
Which gives me:
*** No convergence -- monoMDS stopping criteria:
8: no. of iterations >= maxit
12: stress ratio > sratmax
I use this exact same script to analyze other 16S matrices, all with success. I am only running into this issue with one matrix, and I have already checked it for "NAs", text errors, etc, and found none. I altered the "k" argument to increase the number of dimensions with no success. I have also written this new line of code (below), also without consistent success.
ord = metaMDS(data, distance = "bray", k = 3, maxit=1000, trymax = 300, wascores = FALSE,
autotransform = FALSE, trace = 2, noshare = FALSE)
However, the most peculiar part is that this new line of code gives me convergence approximately half the time.
1) Why does this second line of code work some of the time?
2) How may I accurately and consistently analyze this matrix?
Thank you in advance,
I got a biom table resulted after running Dada2 in Qiime2. Now I want to analyse the microbiome using the Vegan package for R. In addition I would like to use the MicrobiomeAnalyst web server (https://www.microbiomeanalyst.ca/) which includes pipelines to obtain a metabolic profile of each sample.
Could someone tell me how I have to treat those data to apply the functions in R. The biom table has to be transformed into another table?
Did anyone use the MicrobiomeAnalyst web server?
thank you in advance
I have a biodiversity data from two different sampling time (summer and winter) and seven different sites. Each site has three replicate sampling locations. I have previously tested the difference between sites (sampling time grouped together) using ANOSIM and SIMPER, and visualize it using nMDS + 95% ellipse using the vegan package on R.
Now, I am trying to test if there's any difference in results driven by time. I'm imagining the test to compare the difference of each site on there two different sampling times, and then compare the seven sites as a group. Hope I'm making sense, all my knowledge in stats and R are self-learned. Any advice and/or suggestions are much appreciated! Thanks.
Give me a hand. How to install vegan from a CRANmirror when have the message.
Warning in install.packages(NULL, .libPaths()[1L], dependencies = NA, type = type) :
'lib = "C:/Program files/R/R-3.5.3/library"' is not writable
Error in install.packages(NULL, .libPaths()[1L], dependencies = NA, type = type) :
unable to install packages
I am testing bias levels for food-choice groups and had intended to run a one-way anova however my sample sizes are very different as follows:- omnivores (n = 98), pescatarians (n = 7), vegetarians (n = 12) and vegans (n = 5). My data has no outliers, is largely normally distributed, I have homogeneity of variance as assessed by Levene's test for equality of variances (p = .07). I am concerned however that the range in group sizes is too broad, and that some of my groups might be too small to enable me to rely on my anova results?
Any opinions or advice would be very much appreciated. Thanks in advance.
I did db-RDA for a community dataset with two factors and one covariate. The results showed the interaction effect was significant. Then, I need to do the pairwise comparison for all the levels.
There is a function multiconstrained in R to do the multiple comparisons but my db-RDA from capscale with a conditional variable (or you call covariate). Is there a way to do that?
We want to determine if there are statistical differences in the structure of the microbial communities of a lake. For this, samples were taken, monthly, for 10 years, from two depths and considering two fractions (particle associated and free-living). In this way, we have a Species-Sites table where in the columns we have the OTUs and in the rows, for each date, the depth-fraction combinations. Normaly we perform the statistical analysis using the function adonis of package Vegan (Permanova). However, we would like to know if it is correct to assume that each one of the dates as repetitions? Is there any other test e.g. mixed model or LASSO that can determine significance in the differences between depths or fractions considering the time series? Any suggestion on how to perform this analysis will be appreciated.