I have a dataset including 100 species and therefore it's very bad to plot. So I want to pick out a subset of these species and plot them in a RDA plot. I have been following this
guideline
The code looks like this:
## load vegan
require("vegan")
## load the Dune data
data(dune, dune.env)
## PCA of the Dune data
mod <- rda(dune, scale = TRUE)
## plot the PCA
plot(mod, scaling = 3)
## build the plot up via vegan methods
scl <- 3 ## scaling == 3
colvec <- c("red2", "green4", "mediumblue")
plot(mod, type = "n", scaling = scl)
with(dune.env, points(mod, display = "sites", col = colvec[Use],
scaling = scl, pch = 21, bg = colvec[Use]))
text(mod, display = "species", scaling = scl, cex = 0.8, col = "darkcyan")
with(dune.env, legend("topright", legend = levels(Use), bty = "n",
col = colvec, pch = 21, pt.bg = colvec))
This is the plot you end up with. Now i would really like to remove some of the species from the plot, but not the analysis. So the plot only shows like Salrep, Viclat, Aloge and Poatri.
Help is appreciated.
The functions you are doing the actual plotting with have an argument select (at least text.cca() and points.cca(). select takes either a logical vector of length i indicating whether the ith thing should be plotted, or the (numeric) indices of the things to plot. The example would then become:
## Load vegan
library("vegan")
## load the Dune data
data(dune, dune.env)
## PCA of the Dune data
mod <- rda(dune, scale = TRUE)
## plot the PCA
plot(mod, scaling = 3)
## build the plot up via vegan methods
scl <- 3 ## scaling == 3
colvec <- c("red2", "green4", "mediumblue")
## Show only these spp
sppwant <- c("Salirepe", "Vicilath", "Alopgeni", "Poatriv")
sel <- names(dune) %in% sppwant
## continue plotting
plot(mod, type = "n", scaling = scl)
with(dune.env, points(mod, display = "sites", col = colvec[Use],
scaling = scl, pch = 21, bg = colvec[Use]))
text(mod, display = "species", scaling = scl, cex = 0.8, col = "darkcyan",
select = sel)
with(dune.env, legend("topright", legend = levels(Use), bty = "n",
col = colvec, pch = 21, pt.bg = colvec))
Which gives you:
You may also use the ordiselect() function from the goeveg-package:
https://CRAN.R-project.org/package=goeveg
It offers selection of species for ordination plots based on abundances and/or species fit to axes.
## Select ssp. with filter: 50% most abundant and 50% best fitting
library(goeveg)
sel <- ordiselect(dune, mod, ablim = 0.5, fitlim = 0.5)
sel # 12 species selected
The result object of the function (containing the names of selected species) can be put into the select argument (as described above).
Related
I am doing quarterly analysis, for which I want to plot a graph. To maintain continuity on x axis I have turned quarters into factors. But then when I am using plot function and trying to color it red, the col argument is not working.
An example:
quarterly_analysis <- data.frame(Quarter = as.factor(c(2020.1,2020.2,2020.3,2020.4,2021.1,2021.2,2021.3,2021.4)),
AvgDefault = as.numeric(c(0.24,0.27,0.17,0.35,0.32,0.42,0.38,0.40)))
plot(quarterly_analysis, col="red")
But I am getting the graph in black color as shown below:
Converting it to a factor is not ideal to plot unless you have multiple values for each factor - it tries to plot a box plot-style plot. For example, with 10 observations in the same factor, the col = "red" color shows up as the fill:
set.seed(123)
fact_example <- data.frame(factvar = as.factor(rep(LETTERS[1:3], 10)),
numvar = runif(30))
plot(fact_example$factvar, fact_example$numvar,
col = "red")
With only one observation for each factor, this is not ideal because it is just showing you the line that the box plot would make.
You could use border = "red:
plot(quarterly_analysis$Quarter,
quarterly_analysis$AvgDefault, border="red")
Or if you want more flexibility, you can plot it numerically and do a little tweaking for more control (i.e., can change the pch, or make it a line graph):
# make numeric x values to plot
x_vals <- as.numeric(substr(quarterly_analysis$Quarter,1,4)) + rep(seq(0, 1, length.out = 4))
par(mfrow=c(1,3))
plot(x_vals,
quarterly_analysis$AvgDefault, col="red",
pch = 7, main = "Square Symbol", axes = FALSE)
axis(1, at = x_vals,
labels = quarterly_analysis$Quarter)
axis(2)
plot(x_vals,
quarterly_analysis$AvgDefault, col="red",
type = "l", main = "Line graph", axes = FALSE)
axis(1, at = x_vals,
labels = quarterly_analysis$Quarter)
axis(2)
plot(x_vals,
quarterly_analysis$AvgDefault, col="red",
type = "b", pch = 7, main = "Both", axes = FALSE)
axis(1, at = x_vals,
labels = quarterly_analysis$Quarter)
axis(2)
Data
set.seed(123)
quarterly_analysis <- data.frame(Quarter = as.factor(paste0(2019:2022,
rep(c(".1", ".2", ".3", ".4"),
each = 4))),
AvgDefault = runif(16))
quarterly_analysis <- quarterly_analysis[order(quarterly_analysis$Quarter),]
I don't understand how the scaling works in Vegan, when plotting ordinations.
I found this question which will help clarify my point. For what I can read on the "Numerical ecology with R" book, there is differences between scaling = 1 and scaling = 2. In particular, with scaling 1 "The angles among descriptor vectors do not reflect their correlations" while with scaling 2 "The angles between descriptors in the biplot reflect their correlations".
So, I run this code (partially copy-pasted from the cited question) and I get two different plots (the axis span is different, so maybe the scaling parameter is doing something) but I don't see much difference between the angles of the descriptor vectors so I am trying to understand what, if anything, is wrong.
What I am missing, here?
library("vegan")
data(varespec)
data(varechem)
ord <- rda(varespec)
set.seed(1)
(fit <- envfit(ord, varechem, perm = 999))
## make up a fake `status`
status <- factor(rep(c("Class1","Class2"), times = nrow(varespec) / 2))
## manual version with extra things
colvec <- c("red","green")
scl <- 1
plot(ord, type = "n", scaling = scl, main="Scaling 1")
points(ord, display = "sites", col = colvec[status], pch = (1:2)[status])
points(ord, display = "species", pch = "+")
plot(fit, add = TRUE, col = "black")
dev.new()
scl <- 2
plot(ord, type = "n", scaling = scl, main="Scaling 2")
points(ord, display = "sites", col = colvec[status], pch = (1:2)[status])
points(ord, display = "species", pch = "+")
plot(fit, add = TRUE, col = "black")
I am planning to reproduce the attached figure, but I have no clue how to do so:
Let´s say I would be using the CO2 example dataset, and I would like to plot the relative change of the Uptake according to the Treatment. Instead of having the three variables in the example figure, I would like to show the different Plants grouped for each day/Type.
So far, I managed only to get this bit of code, but this is far away from what it should look like.
aov1 <- aov(CO2$uptake~CO2$Type+CO2$Treatment+CO2$Plant)
plot(TukeyHSD(aov1, conf.level=.95))
Axes should be switched, and I would like to add statistical significant changes indicated with letters or stars.
You can do this by building it in base R - this should get you started. See comments in code for each step, and I suggest running it line by line to see what's being done to customize for your specifications:
Set up data
# Run model
aov1 <- aov(CO2$uptake ~ CO2$Type + CO2$Treatment + CO2$Plant)
# Organize plot data
aov_plotdata <- data.frame(coef(aov1), confint(aov1))[-1,] # remove intercept
aov_plotdata$coef_label <- LETTERS[1:nrow(aov_plotdata)] # Example labels
Build plot
#set up plot elements
xvals <- 1:nrow(aov_plotdata)
yvals <- range(aov_plotdata[,2:3])
# Build plot
plot(x = range(xvals), y = yvals, type = 'n', axes = FALSE, xlab = '', ylab = '') # set up blank plot
points(x = xvals, y = aov_plotdata[,1], pch = 19, col = xvals) # add in point estimate
segments(x0 = xvals, y0 = aov_plotdata[,2], y1 = aov_plotdata[,3], lty = 1, col = xvals) # add in 95% CI lines
axis(1, at = xvals, label = aov_plotdata$coef_label) # add in x axis
axis(2, at = seq(floor(min(yvals)), ceiling(max(yvals)), 10)) # add in y axis
segments(x0=min(xvals), x1 = max(xvals), y0=0, lty = 2) #add in midline
legend(x = max(xvals)-2, y = max(yvals), aov_plotdata$coef_label, bty = "n", # add in legend
pch = 19,col = xvals, ncol = 2)
Starting with the following code:
library(vegan)
data(dune)
data(dune.env)
Ordination.model1 <- cca(dune ~ Management,dune.env)
plot1 <- plot(Ordination.model1, choices=c(1,2), scaling=1)
I get a plot with sites, species, centroids, and biplot arrows. I want to build up a plot with just the sites depicted by points, and the arrows with customized labels.
So far, I have:
colvec <- c("red", "green", "blue")
plot(Ordination.model1, type="n", scaling=1)
with(dune.env, points(Ordination.model1, display ="sites", col=colvec[Use], scaling=1, pch =16, bg = colvec[Use]))
I am stuck as far as how to put the arrows in. Thanks in advance!
You can add arrows using text. I was not able to use your code as I kept getting errors, however here is a basic example that does what you want. I took it from R Help: CCA Plot
Once you add text the arrows should show.
require(vegan)
data(varespec)
data(varechem)
vare.cca <- cca(varespec ~ ., data = varechem)
plot(vare.cca, display = c("sites","species"), scaling = 3)
text(vare.cca, scaling = 3, display = "bp")
Here is an example with the labels argument:
## S3 method for class 'cca':
text((x, display = "sites", labels, choices = c(1, 2),
scaling = "species", arrow.mul, head.arrow = 0.05, select, const,
axis.bp = TRUE, correlation = FALSE, hill = FALSE, ...))
labels:
Optional text to be used instead of row names:
Plot or Extract Results of Constrained Correspondence Analysis or Redundancy Analysis
I was able to rename the arrows: below is the full code.
library(vegan)
data(dune)
data(dune.env)
Ordination.model1 <- cca(dune ~ Management,dune.env)
summary(Ordination.model1) # Lets you see the current biplot labels in the output.
colvec <- c("red", "green", "blue", "orange")
plot(Ordination.model1, type="n", scaling=1)
with(dune.env, points(Ordination.model1, display ="sites", col=colvec[Management],scaling=1, pch =16, bg = colvec[Management]))
labl <- c("HF", "NM", "SF") # new labels. Need to be in the same order as the old biplot labels.
text(Ordination.model1, display="bp", scaling=1, labels=labl)
I am relatively new to R and I am trying to get my head around how to do ordination techniques in R, so that I don't need to use other software.
I am trying to get a PCA with environmental factors in the place of species.
As I have sites which differ qualitatively (in terms of land use) I wanted to be able to show that difference in the final plot (with different colours). Therefore, I used the method a la Gavin Simpson with the package vegan. So far so good. Here is also the code that I used for that:
with(fish, status)
scl <- -1 ## scaling = -1
colvec <- c("red2", "mediumblue")
plot(pond.pca, type = "n", scaling = scl)
with(fish, points(pond.pca, display = "sites", col = colvec[status], scaling = scl, pch = 21, bg = colvec[status]))
head(with(fish, colvec[status]))
text(pond.pca, display = "species", scaling = scl, cex = 0.8, col = "darkcyan")
with(fish, legend("topright", legend = levels(status), bty = "n", col = colvec, pch = 21, pt.bg = colvec))
The problem arises when I try to put arrows for my environmental variables in the ordination plot. If I use biplot and other functions like ordiplot etc. I ll not be able to keep the different colours for my two types of sites, therefore I don't want to use those. If I use the command here:
plot(envfit(pond.pca, PondEnv38, scaling=-1), add=TRUE, col="black")
I get nice arrows, only the are not aligned (and in some cases are completely opposite) with the environmental variables that I ve given with the code before (line 5). I tried to change the scaling but they just cannot align.
Does anyone know how to deal with that problem?
Any tips would be useful.
It is not clear what you are doing wrong as you don't provide a reproducible example of the problem and I am having difficulty following your description of what is wrong. Here is a fully worked out example for you to follow that does what you seem to being trying to do.
data(varespec)
data(varechem)
ord <- rda(varespec)
set.seed(1)
(fit <- envfit(ord, varechem, perm = 999))
## make up a fake `status`
status <- factor(rep(c("Class1","Class2"), times = nrow(varespec) / 2))
> head(status)
[1] Class1 Class2 Class1 Class2 Class1 Class2
Now plot
layout(matrix(1:2, ncol = 2))
## auto version
plot(fit, add = FALSE)
## manual version with extra things
colvec <- c("red","green")
scl <- -1
plot(ord, type = "n", scaling = scl)
points(ord, display = "sites", col = colvec[status], pch = (1:2)[status])
points(ord, display = "species", pch = "+")
plot(fit, add = TRUE, col = "black")
layout(1)
Which gives
And all the arrows seem to be pointing as they would if you plotted the envfit object directly.