I'm conducting a meta-analysis (MA). I used the standard R command to conduct the analysis. My MA has many studies to analyze.
e.g. 30 studies to make the pooled analysis. After the command of the forest plot, I click on zoom to see the graph. However, the graph occupies the entire screen and many data do not appear.
For example, only the middle of the graph appears, but the top and bottom do not appear.
I tried to understand the ggplot2 and images packages, but I did not succeed.
How could fix this problem?
Related
I am working on a forest plot with the metafor package. With the help of (http://www.metafor-project.org/doku.php/plots:forest_plot_with_subgroups) and several forum entries I wrote a code but unfortunately the letters are blurry. I tried making the plot bigger with windows () which did help a bit but still it is not ready for putting in a paper.
It looked like this:also blurry with zooming in
When I used only a subset of effect sizes for the subgroup analysis the forest plot the text was better readable but only until about 15 effect sizes). And because the row spacing is so narrow I can´t even think about putting summary polygons for subgroups in between.
This was the code I used for my data (90 effect sizes in multilevel model):
forest(Model1,
slab= data$Author, #labeled by author
# cex=0.75, #only made the text bigger but did not change the row distance, so the text overlapped
ylim=c(-1, 100),
header="Author(s) and Year")
There were a lot of extra arguments in examples of forest plots I saw online but I am not sure of their meaning in forest plot.
Does someone know how to solve this spacing problem or what arguments I need to add to the code e.g. to broaden the space between the rows? Is there a “guide” for working with forest?
Thank you very much in advance!
Blurriness has nothing to do with the forest() function - it's a matter of how you are saving the plot. You should use functions like png() with a high enough resolution. Or use pdf() where you can zoom in to your heart's content without ever seeing any blurriness.
As for a "guide", I would suggest reading the documentation first:
https://wviechtb.github.io/metafor/reference/forest.rma.html
https://wviechtb.github.io/metafor/reference/forest.default.html
Then you will start to understand what the arguments do.
I got some figures after I did decision tree model using part library.
This figures shows fundamental function of part library.
In these figures, I understand all excepts fourth kind of figure.
This figure don't have any powful feature. It does not show any information. How can I understand this figure?
You are right. The fourth panel shows the visualization of a tree without any splits. Thus, in this case none of the available split variables improved the cost-complexity criterion of rpart and hence only the root node remains.
The visualization using the partykit package employs stacked bar plots for every terminal node in the trees (the default visualization for binary classification in the package). Thus, there is only a single stacked bar in case of a root node only.
Hi,
I am running a simple linear regression model in R at the moment and wanted to check my assumptions. As seen by the plot, my red line does not appear to be flat and instead curved in places.
I am having a little difficulty interpreting this - does this imply non-linearity? And if so, what does this say about my data?
Thank you.
The observation marked 19 on your graph (bottom right corner) seems to have significant influence and is pulling down your line more than other points are pulling it up. The relationship looks linear all in all, getting rid of that outlier by either nullifying it by increasing sample size (Law of large numbers) or removing the outlier(s) should fix your problem without compromising the story your data is trying to tell you and give you the nice graph you're looking for.
I'm new to R, but have worked out how to graph the distribution of my students' grades for a given term using a density plot, and have made some ridgeline plots to show how the distribution evolves throughout the academic year.
I'm thinking it might be fun (and make the graphs easier to interpret) if I could make a kind of flip-book animation that went from one terms grades to the next, relatively quickly, to see how the distribution changes. At its simplest, I could just pop these distribution plots into Powerpoint and just scroll through the pages, but I'm wondering what commands I need to put into R's ggplot command to ensure that the axes/scaling from one chart to the next stays consistent from one chart to the next?
At the moment, I'm just making a simple chart using this command, where HT102 is the data from the 2nd term of Year 10, and A8 is a vector containing all the (numeric) grades. I am then doing the same thing with another set of grades called ht103, and so on...
ggplot(ht102, aes(x = A8)) +
geom_density(alpha=.3)
What would you recommend to keep the scaling consistent, and any thoughts on a better way to animate this than just popping them into powerpoint?
Apologies, this is probably the simplest question. I'm having trouble making a scatterplot in R Studio. I am trying to see if amphipod counts are correlated to oxygen content. Whenever I plot this using:
plot(Amphipod~Oxygen...ml.l.)
I get a graph with boxes around certain points and I have no idea why. Only 5 points and I can't see anything different about those.