When I am trying to perform a funnel plot using the "netmeta" package in r. However, when i try to run the code, it returns a NULL.
#sensitivity OR
p1 <- pairwise(type, event = TP, TP+FN, studlab = total$studynames,
data = total, sm = "OR", allstudies = TRUE)
p1
nb1 <- netmetabin(p1, method = "MH", ref = "A")
netleague(nb1, digits = 3)
netrank(nb1)
nb1
plot(netrank(nb1))
forest(nb1)
Forest plot seems to work and calculating SUCRA scores (netrank) seems to work too (meaning the objects are created correctly). When I use the exact same code for the example databases it does work (not binary data). Is it possible that this is due to the fact that my object is "netmetabin" instead of "netmeta"? If so, what is the alternative to making a funnel plot for binary data for a diagnostic network meta-analysis?
thanks in advance.
Related
I am using the isomap-function from vegan package in R to analyse community data of epiphytic mosses and lichens. I started analysing the data using NMDS but due to the structure of the data ran into problems which is why I switched to ISOMAP which works perfectly well and returns very nice results. So far so good... However, the output of the function does not support plotting of species within the ISOMAP plot as species scores are not available. Anyway, I would really like to add species information to enhance the interpretability of the output.
Does anyone of you has a solution or hint to this problem? Is there a way to add species kind of post hoc to the plot as it can be done with environmental data?
I would greatly appreciate any help on this topic!
Thank you and best regards,
Inga
No, there is no function to add species scores to isomap. It would look like this:
`sppscores<-.isomap` <-
function(object, value)
{
value <- scale(value, center = TRUE, scale = FALSE)
v <- crossprod(value, object$points)
attr(v, "data") <- deparse(substitute(value))
object$species <- v
object
}
Or alternatively:
`sppscores<-.isomap` <-
function(object, value)
{
wa <- vegan::wascores(object$points, value, expand = TRUE)
attr(wa, "data") <- deparse(substitute(value))
object$species <- wa
object
}
If ord is your isomap result and comm are your community data, you can use these as:
sppscores(ord) <- comm # either alternative
I have no idea (yet) which of these alternatives is more correct. The first adds species scores as vectors of their linear increase, the second as their weighted averages in ordination space, but expanded so that we allow some species be more extreme than the site units where they occur.
These will add new element species to the result object ord. However, using these in vegan would need more coding, but you can extract the species scores with vegan::scores, but their scaling is based on the original scale of community data, and may be badly scaled with respect to points of site units, and working on this would require more work. However, you can plot them separately, or then multiply with a constant giving similar scaling as site unit scores.
sp <- scores(ord, display="species", choices=1:2)
plot(sp, type = "n", asp = 1) # does not allow plotting text
text(sp, labels = rownames(sp)) # so we must add text
I have applied DBSCAN algorithm on built-in dataset iris in R. But I am getting error when tried to visualise the output using the plot( ).
Following is my code.
library(fpc)
library(dbscan)
data("iris")
head(iris,2)
data1 <- iris[,1:4]
head(data1,2)
set.seed(220)
db <- dbscan(data1,eps = 0.45,minPts = 5)
table(db$cluster,iris$Species)
plot(db,data1,main = 'DBSCAN')
Error: Error in axis(side = side, at = at, labels = labels, ...) :
invalid value specified for graphical parameter "pch"
How to rectify this error?
I have a suggestion below, but first I see two issues:
You're loading two packages, fpc and dbscan, both of which have different functions named dbscan(). This could create tricky bugs later (e.g. if you change the order in which you load the packages, different functions will be run).
It's not clear what you're trying to plot, either what the x- or y-axes should be or the type of plot. The function plot() generally takes a vector of values for the x-axis and another for the y-axis (although not always, consult ?plot), but here you're passing it a data.frame and a dbscan object, and it doesn't know how to handle it.
Here's one way of approaching it, using ggplot() to make a scatterplot, and dplyr for some convenience functions:
# load our packages
# note: only loading dbscacn, not loading fpc since we're not using it
library(dbscan)
library(ggplot2)
library(dplyr)
# run dbscan::dbscan() on the first four columns of iris
db <- dbscan::dbscan(iris[,1:4],eps = 0.45,minPts = 5)
# create a new data frame by binding the derived clusters to the original data
# this keeps our input and output in the same dataframe for ease of reference
data2 <- bind_cols(iris, cluster = factor(db$cluster))
# make a table to confirm it gives the same results as the original code
table(data2$cluster, data2$Species)
# using ggplot, make a point plot with "jitter" so each point is visible
# x-axis is species, y-axis is cluster, also coloured according to cluster
ggplot(data2) +
geom_point(mapping = aes(x=Species, y = cluster, colour = cluster),
position = "jitter") +
labs(title = "DBSCAN")
Here's the image it generates:
If you're looking for something else, please be more specific about what the final plot should look like.
Just a little question on how to store and recall forest plot in R. I am creating forest plot using the meta function in R. I may have the need to store the graph and recall in the viewer at a later time. I've tried with this expression:
forest.meta <- forest(meta, [...])
where [...] are the options, but when I type "forest.meta", I get a "null" error rather than the graph again in the viewer.
Where I am wrong?
Thank you in advance for any help.
You get NULL because that's what the function returns, much as base R's plot does. It's not like ggplot where an actual plot object is returned for you to manipulate.
However, all is not lost. Since forest plots using grid graphics, we can grab the contents of the plotting window, store them as a collection of graphical objects, and plot them again later:
library(meta)
data(Olkin1995)
m1 <- metabin(ev.exp, n.exp, ev.cont, n.cont,
data = Olkin1995, subset = c(41, 47, 51, 59),
sm = "RR", method = "I",
studlab = paste(author, year))
forest(m1)
# Now grab the plot
my_plot <- grid::grid.grab()
The plot is now stored as my_plot, so suppose we want to use the plotting window for something else meantime
plot(1:10)
When we're done, we can recall the exact same plot by doing:
grid::grid.newpage()
grid::grid.draw(my_plot)
I have a graph that is created by complex numbers from the function below. I would like to extract the resulting data points which correpond with the line from the data plot as to be able to work with a vector of data.
library(multitaper)
NW<-10
K<-5
x<-c(2,3,1,3,4,6,7,8,5,4,3,2,4,5,7,8,6,4,3,2,4,5,7,8,6,4,5,3,2,5,7,8,6,4,5,3,6,7,8,8,9,7,6,5,4,7)
resSpec <- spec.mtm(as.ts(x), k= K, nw=NW, nFFT = length(x),
centreWithSlepians = TRUE, Ftest = TRUE,
jackknife = FALSE, maxAdaptiveIterations = 100,
plot =FALSE, na.action = na.fail)
plot(resSpec)
What would be the best procedure. I have tried saving the plot in emf. I wanted to use package ReadImages which was I believe the right package. (however this was not available for R versión 3.02 so I could not use it). What would be the correct procedure of saving and extracting and are there other packages and in what file types could I save the graph (as far as I can see R (OS windows) only permist emf.)
Any help welcomed
I'm an R programming beginner and I'm trying to implement the clustering.plot method available in R package EMA. My clustering works fine and I can see the results populated as well. However, when I try to generate a heat map using clustering.plot, it gives me an error "Error in plot.new (): graphic edges too large". My code below,
#Loading library
library(EMA)
library(colonCA)
#Some information about the data
data(colonCA)
summary(colonCA)
class(colonCA) #Expression set
#Extract expression matrix from colonCA
expr_mat <- exprs(colonCA)
#Applying average linkage clustering on colonCA data using Pearson correlation
expr_genes <- genes.selection(expr_mat, thres.num=100)
expr_sample <- clustering(expr_mat[expr_genes,],metric = "pearson",method = "average")
expr_gene <- clustering(data = t(expr_mat[expr_genes,]),metric = "pearson",method = "average")
expr_clust <- clustering.plot(tree = expr_sample,tree.sup=expr_gene,data=expr_mat[expr_genes,],title = "Heat map of clustering",trim.heatmap =1)
I do not get any error when it comes to actually executing the clustering process. Could someone help?
In your example, some of the rownames of expr_mat are very long (max(nchar(rownames(expr_mat)) = 271 characters). The clustering_plot function tries to make a margin large enough for all the names but because the names are so long, there isn't room for anything else.
The really long names seem to have long stretches of periods in them. One way to condense the names of these genes is to replace runs of 2 or more periods with just one, so I would add in this line
#Extract expression matrix from colonCA
expr_mat <- exprs(colonCA)
rownames(expr_mat)<-gsub("\\.{2,}","\\.", rownames(expr_mat))
Then you can run all the other commands and plot like normal.