I would like to plot goups for my ordination using the function ordihull in vegan. However I have some sites with only one occurence. Using ordihull this sites do not appear in the plot. See the example below, when only one site has BF as management. What I would like to have is a BF label where the one remaining BF management site is located in the ordination plot.
library(vegan)
data(dune)
data(dune.env)
#remove all but one row with BF as management
dune <- dune[-c(2,11),]
dune.env <- dune.env[-c(2,11),]
mod <- cca(dune ~ Management, dune.env)
attach(dune.env)
plot(mod, type="n", scaling = 3)
pl <- ordihull(mod, Management, scaling = 3, label = TRUE)
orihull ignores groups with a single observation and thus doesn't populate the group centroids object with the centre of the convex hull. You could argue it should; I'll need to take this up with Jari and see if we can fix this.
To solve the problem, you have to add the location of the single observation in a secondary step using the text() method. [With the correct removal of all bar one of the BF observations -- -c(2,11)] the following does what you want:
plot(mod, type="n", scaling = 3)
with(dune.env, ordihull(mod, Management, scaling = 3, label = TRUE))
with(dune.env,
text(mod, labels = Management, select = Management == "BF",
scaling = 3, display = "sites"))
Giving
This is made trivial because you can specify select to choose to plot on the one observation with Management == "BF".
Related
I am having trouble with plotting a forest plot based on a multi-level model, in which I'd also like to display pooled effects of subgroups, as well as the results for subgroup differences.
So far, I have managed to produce a plot of the data where clusters are grouped together. I would like to extend this plot by adding pooled effects of subgroups at the right positions, without losing the grouping of the clusters. (As it is explained here, but also while keeping what is shown in the last example of this).
This is the code I have used so far to produce the "normal" forest plot for my model (sorry, it's pretty long):
# ma_data => my data
# main_3L => my multi-level model
# Prepare row argument for separation by study
dd <- c(0, diff(ma_data$ID))
dd[dd > 0] <- 1
rows <- (1:main_3L$k) + cumsum(dd)
par(tck=-.01, mgp = c(1.6,.2,0), cex=1)
# refactor ID var
ma_data$ID_plot <- substr(ma_data$short_cite, 1, nchar(ma_data$short_cite))
ma_data$ID_plot <- paste(sub(" ||) ","",substr(ma_data$ID_plot,0,2)), substr(ma_data$ID_plot,3,nchar(ma_data$ID_plot)), sep="")
tiff("./figures/forestFull_ext1.tiff", width=3200,height=4500, res=300)
# Plot the forest!
metafor::forest(main_3L,
addpred = TRUE, # adds prediction interval
cex=0.5,
header="Author(s) and Year",
rows=rows, # uses the vector created above
order=order(ma_data$ID, ma_data$es_adj),
ylim=c(0.5,max(rows)+3),
xlim=c(-5,3),
xlab="Hedges' G",
ilab=cbind(as.character(ma_data$setup),as.character(ma_data$target_1), as.character(ma_data$measure_type), ma_data$task, as.character(ma_data$cogdom_pooled), ma_data$sample_size_exp),
ilab.xpos=c(-3.9,-3.6,-3.3,-2.8,-2.2,-1.7),
slab=ma_data$ID_plot,
mlab = mlabfun("Overall RE Modell", main_3L, main_3L.I2)) # Adds Q,Qp, I² and sigma² values.
abline(h = rows[c(1,diff(rows)) == 2] - 1, lty="dotted")
# adds a second polygon with robust estimates for standard error
addpoly(coeftest.main_3L$beta, sei = coeftest.main_3L$SE,
rows = -2.5,
cex = 0.5,
mlab = "Robust RE Model estimate",
col = "darkred")
par(cex=0.5, font=2)
# text(c(-4,-3.7,-3.2,-2.5, -2), 150.5, pos=3, c("Target", "Measure","Task","Cognitive Domain", "N"))
text(c(-3.9,-3.6,-3.3,-2.8,-2.2,-1.7), 150.5, pos=3, c("Setup", "Target", "Measure","Task","Cognitive Domain", "N"))
dev.off()
Specifically, I need to know how to "make space" for the additional rows and polygons.
Also, is there an option in the forest() function to display only the pooled effects of subgroups and main effect, bot not the individual effect sizes? I know that it is possible in the meta package, but have not found anything similar in metafor.
Any help is greatly appreciated!
I have 2 csv data files. Each file has a "date_time" column and a "temp_c" column. I want to make the x-axis have the "date_time" from both files and then use 2 y-axes to display each "temp_c" with separate lines. I would like to use plot instead of ggplot2 if possible. I haven't been able to find any code help that works with my data and I'm not sure where to really begin. I know how to do 2 separate plots for these 2 datasets, just not combine them into one graph.
plot(grewl$temp_c ~ grewl$date_time)
and
plot(kbll$temp_c ~ kbll$date_time)
work separately but not together.
As others indicated, it is easy to add new data to a graph using points() or lines(). One thing to be careful about is how you format the axes as they will not be automatically adjusted to fit any new data you input using points() and the like.
I've included a small example below that you can copy, paste, run, and examine. Pay attention to why the first plot fails to produce what you want (axes are bad). Also note how I set this example up generally - by making fake data that showcase the same "problem" you are having. Doing this is often a better strategy than simply pasting in your data since it forces you to think about the core component of the problem you are facing.
#for same result each time
set.seed(1234)
#make data
set1<-data.frame("date1" = seq(1,10),
"temp1" = rnorm(10))
set2<-data.frame("date2" = seq(8,17),
"temp2" = rnorm(10, 1, 1))
#first attempt fails
#plot one
plot(set1$date1, set1$temp1, type = "b")
#add points - oops only three showed up bc the axes are all wrong
lines(set2$date2, set2$temp2, type = "b")
#second attempt
#adjust axes to fit everything (set to min and max of either dataset)
plot(set1$date1, set1$temp1,
xlim = c(min(set1$date1,set2$date2),max(set1$date1,set2$date2)),
ylim = c(min(set1$temp1,set2$temp2),max(set1$temp1,set2$temp2)),
type = "b")
#now add the other points
lines(set2$date2, set2$temp2, type = "b")
# we can even add regression lines
abline(reg = lm(set1$temp1 ~ set1$date1))
abline(reg = lm(set2$temp2 ~ set2$date2))
Plotting several clusters using seqdplot in TraMineR can make the legend messy, especially in combination with numerous states. This calls for additional options for modifying the legend which is available with the function seqlegend. However, I have a hard time combining a state distribution plot (seqdplot) with a separate modified legend (seqlegend). Ideally one wants to plot the clusters (e.g. 9) without a legend and then add the separate legend in the available bottom right row, but instead the separate legend is generating a new plot window. Can anyone help?
Here's an example using the biofam data. With the data I use in my own research the legend becomes much more messy since I have 11 states.
#Data
library(TraMineR)
library(WeightedCluster)
data(biofam)
biofam.seq <- seqdef(biofam[501:600, 10:25])
#OM distances
biofam.om <- seqdist(biofam.seq, method = "OM", indel = 3, sm = "TRATE")
#9 clusters
wardCluster <- hclust(as.dist(biofam.om), method = "ward.D2")
cluster9 <- cutree(wardCluster, k = 9)
#State distribution plot
seqdplot(biofam.seq, group = cluster9, with.legend = F)
#Separate legend
seqlegend(biofam.seq, title = "States", ncol = 2)
#Combine state distribution plot and separate legend
#??
Thank you.
The seqplot function does not allow to control the number of columns of the legend, nor does it allow to add a legend title. So you have to compose the plot yourself by generating a separated plot for each group with the legend disabled and adding the legend afterwards. Here is how you can do that:
cluster9 <- factor(cluster9)
levc <- levels(cluster9)
lev <- length(levc)
par(mfrow=c(5,2))
for (i in 1:lev)
seqdplot(biofam.seq[cluster9 == levc[i],], border=NA, main=levc[i], with.legend=FALSE)
seqlegend(biofam.seq, ncol=4, cex = 1.2, title='States')
========================
Update, Oct 1, 2018 =================
Since TraMineR V 2.0-9, the seqplot family of functions now support (when applicable) the argument ncol to control the number of columns in the legend. To add a title to the legend, you still have to proceed as shown above.
AFAIK seqlegend() doesn't work when the other plots you are plotting utilizes the groups arguments. In your case the only thing seqlegend() is adding is a title "States". If you are looking to add a legend so you can customize what is in the legend and so forth, you can accomplish that by providing the corresponding alphabet and states that are used in your analysis.
The package's website has several walkthroughs and guides enumerating the various options and so forth: Link to their webiste
#Data
library(TraMineR)
library(WeightedCluster)
data(biofam)
## Generate alphabet and states
alphabet <- 0:7
states <- letters[seq_along(alphabet)]
biofam.seq <- seqdef(biofam[501:600, 10:25], states = states, alphabet = alphabet)
#OM distances
biofam.om <- seqdist(biofam.seq, method = "OM", indel = 3, sm = "TRATE")
#9 clusters
wardCluster <- hclust(as.dist(biofam.om), method = "ward.D2")
cluster9 <- cutree(wardCluster, k = 9)
#State distribution plot
seqdplot(biofam.seq, group = cluster9, with.legend = TRUE)
Okay so I'm sure this has been asked before but I can't find a nice answer anywhere after many hours of searching.
I have some data, I run a classification then I make a dendrogram.
The problem has to do with aesthetics, specifically; (1) how to cut according to the number of groups (in this example I want 3), (2) make the group labels aligned with the branches of the trees, (2) Re-scale so that there aren't any huge gaps between the groups
More on (3). I have dataset which is very species rich and there would be ~1000 groups without cutting. If I cut at say 3, the tree has some branches on the right and one 'miles' off to the right which I would want to re-scale so that its closer. All of this is possible via external programs but I want to do it all in r!
Bonus points if you can put an average silhouette width plot nested into the top right of this plot
Here is example using iris data
library(ggplot2)
data(iris)
df = data.frame(iris)
df$Species = NULL
ED10 = vegdist(df,method="euclidean")
EucWard_10 = hclust(ED10,method="ward.D2")
hcd_ward10 = as.dendrogram(EucWard_10)
plot(hcd_ward10)
plot(cut(hcd_ward10, h = 10)$upper, main = "Upper tree of cut at h=75")
I suspect what you would want to look at is the dendextend R package (it also has a paper in bioinformatics).
I am not fully sure about your question on (3), since I am not sure I understand what rescaling means. What I can tell you is that you can do quite a lot of dendextend. Here is a quick example for coloring the branches and labels for 3 groups.
library(ggplot2)
library(vegan)
data(iris)
df = data.frame(iris)
df$Species = NULL
library(vegan)
ED10 = vegdist(df,method="euclidean")
EucWard_10 = hclust(ED10,method="ward.D2")
hcd_ward10 = as.dendrogram(EucWard_10)
plot(hcd_ward10)
install.packages("dendextend")
library(dendextend)
dend <- hcd_ward10
dend <- color_branches(dend, k = 3)
dend <- color_labels(dend, k = 3)
plot(dend)
You can also get an interactive dendrogram by using plotly (ggplot method is available through dendextend):
library(plotly)
library(ggplot2)
p <- ggplot(dend)
ggplotly(p)
I would like to plot vectors from a capscale ordination using VEGAN.
I am familiar with the display ="bp" command, but this adds labels that are obscured by site points. Is there an easy means of removing these? I am happy to add them in later i.e. once exported and within word for publication.
My code thus far is as follows:
plot(mod, scaling = 3, type="n")
with(data, points(mod, display="sites", cex=Pointsize,
pch=ifelse(Cat=="Reference",21,19)) ,bg=Cat,)
with(data,text(mod,display="bp"))
Help will be appreciated
Use the points() method instead of the text() method:
points(mod, display = "bp")
(There also should be no need for the with(data) in that last line of code you show.)
Here is a reproducible example:
require(vegan)
data(varespec)
data(varechem)
ord <- cca(varespec ~ ., data = varechem)
plot(ord, type = "n", display = "sites")
points(ord, display = "sites")
points(ord, display = "bp")