vegan

问题 So I'm using R to perform a DCA (detrended correspondence analysis) through Vegan package and everytime I plot my results I get a grid in the middle of the plot. I want to get rid of it. Here is my code: dca <- decorana(dados) plot(dca, type = "n", ann = FALSE, origin = TRUE) text(dca, display = "sites") points(dca, display = "species", pch = 21, col="red", bg = "red", cex=0.5) mtext(side = 3, text = "Análise de Correspondência Retificada (DCA)", font=2, line = 2.5, cex=1.8, font.lab=6, cex

问题 Why do I have this error message : > vegan::reorder.hclust Error: 'reorder.hclust' is not an exported object from 'namespace:vegan' While this S3 method is well available. For example if I type help(reorder.hclust, package = "vegan") I obtain the intended help page and vegan:::reorder.hclust displays the source code of the function on the console... Also the NAMESPACE file of my vegan installation contains S3method(reorder, hclust) . I would like to use this function in another package were I

问题 Why do I have this error message : > vegan::reorder.hclust Error: 'reorder.hclust' is not an exported object from 'namespace:vegan' While this S3 method is well available. For example if I type help(reorder.hclust, package = "vegan") I obtain the intended help page and vegan:::reorder.hclust displays the source code of the function on the console... Also the NAMESPACE file of my vegan installation contains S3method(reorder, hclust) . I would like to use this function in another package were I

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 8 years ago . Improve this question I have a species abundance dataset with quite a few zeros in it and even when I set trymax = 1000 for metaMDS() the program is unable to find a stable solution for the stress. I have already tried combining data (collapsing multiple years together to reduce the number of zeros) and I can't

问题 I have a dataset consisting of 132 observations and 10 variables. These variables are all categorical. I am trying to see how my observations cluster and how they are different based on the percentage of variance. i.e I want to find out if a) there are any variables which helps to draw certain observation points apart from one another and b) if yes, what is the percentage of variance explained by it? I was advised to run a PCoA (Principle Coordinates Analysis) on my data. I ran it using vegan

问题 The function adonis from the vegan package performs a non-parametric MANOVA, also known as PERMANOVA. The issue (nor not, maybe I just don't fully understand how the test works) is that every time I run it using the same data, I get a slightly different p-value. Here is an example data. dframetest <- data.frame(X = rnorm(20), Y = rnorm(20), Z = rnorm(20), Label = c(rep("A",10),rep("B",10))) adonis(dframetest[,1:3] ~ Label, permutations = 1000, data = dframetest, method = "euclidean") If you

问题 I made a NMDS plot and plotted my envfit like follows dataframe for mytable sites=c("Site A","Site B","Site C","Site D","Site E","Site F","Site G","Site H","Site I","Site J","Site K","Site L","Site M","Site N","Site O","Site P","Site Q","Site R","Site S","Site T","Site U") american.elm=c(41.91,10.11,2.62,5.31,7.51,9.72,17.44,9.06,19.83,30.81,62.6,21.29,20.7,28.68,27.69,34.89,35.65,3.87,12.68,1.58,2.97) white.birch=c(7.07,15.89,26.77,15.61,14.59,6.33,2.23,11.66,21.49,20.15,7.61,23.29,0,0,0,0,0

问题 I made a NMDS plot and plotted my envfit like follows dataframe for mytable sites=c("Site A","Site B","Site C","Site D","Site E","Site F","Site G","Site H","Site I","Site J","Site K","Site L","Site M","Site N","Site O","Site P","Site Q","Site R","Site S","Site T","Site U") american.elm=c(41.91,10.11,2.62,5.31,7.51,9.72,17.44,9.06,19.83,30.81,62.6,21.29,20.7,28.68,27.69,34.89,35.65,3.87,12.68,1.58,2.97) white.birch=c(7.07,15.89,26.77,15.61,14.59,6.33,2.23,11.66,21.49,20.15,7.61,23.29,0,0,0,0,0

问题 I have a question regarding how to recreate the results from the envfit() function in the vegan package. Here is an example of envfit() being used with an ordination and an environmental vector. data(varespec) data(varechem) ord <- metaMDS(varespec) chem.envfit <- envfit(ord, varechem, choices = c(1,2), permutations = 999) chem.scores.envfit <- as.data.frame(scores(chem.envfit, display = "vectors")) chem.scores.envfit "The values that you see in the table are the standardised coefficients

问题 I have a question regarding how to recreate the results from the envfit() function in the vegan package. Here is an example of envfit() being used with an ordination and an environmental vector. data(varespec) data(varechem) ord <- metaMDS(varespec) chem.envfit <- envfit(ord, varechem, choices = c(1,2), permutations = 999) chem.scores.envfit <- as.data.frame(scores(chem.envfit, display = "vectors")) chem.scores.envfit "The values that you see in the table are the standardised coefficients