I'm trying to create a ggplot and add results of a correlation test I have done.
Something along the lines of:
p+annotate("text",x=12.5,y=15.25,label=c(cor.test$estimate,cor.test$p.value))
I keep getting error messages no matter what I try.
Any ideas?
I have actually managed to add stat details to the plot by using stat_cor() from the package ggpubr
library(ggpubr)
p+stat_cor(method="pearson")
There is a package in development that can do this for you (ggstatsplot is on CRAN).
Here is an example of how to create correlation plot:
ggstatsplot::ggscatterstats(data = iris, x = Sepal.Length, y = Sepal.Width)
This will produce a plot that looks like the following (you can similarly get results from Spearman's rho (type = 'spearman') or robust correlation test (type = 'robust')):
Check out the documentation of the function for more.
Related
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.
I am plotting my coefficient estimates using the function plot_summs() and would like to divide my coefficients into two separate groups.
The function plot_summs() has an argument groups, however, when I try to use it as explained in the documentation, I do not get any results nor error. Can someone give me an example of how I can use this argument please?
This is the code I currently have:
plot_summs(model.c, scale = TRUE, groups = list(pane_1 = c("AQI_average", "temp_yearly"), pane_2 = c("rain_1h_yearly", "snow_1h_yearly")), coefs = c("AQI Average"= "AQI_average", "Temperature (in Farenheit)" = "temp_yearly","Rain volume in mm" = "rain_1h_yearly", "Snow volume in mm" = "snow_1h_yearly"))
And the image below is what I get as a result. What I would like to get is to have two panes separate panes. One which would include "AQI_average" and "temp_yearly" and the other one that would have "rain_1h_yearly" and "snow_1h_yearly". Event though I use the groups argument, I do not get this.
Output of my code
By minimal reproducible example, markus is refering to a piece of code that enables others to exactly reproduce the issue you are refering to on our respective computers, as described in the link that they provided.
To me, it seems the problem is that the groups function does not seem to work in plot_summs - it seems someone here also pointed it out.
If plot_summs is replaced by plot_coef, the groups function work for me. However, the scale function does not seem to be available. A workaround might be:
r <- lm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width, data = iris)
y <- plot_summs(r, scale = TRUE) #Plot for scaled version
t <- plot_coefs(r, #Plot for unscaled versions but with facetting
groups =
list(
pane_1 = c("Sepal.Width", "Petal.Length"),
pane_2 = c("Petal.Width"))) + theme_linedraw()
y$data$group <- t$data$group #Add faceting column to data for the plot
t$data <- y$data #Replace the data with the scaled version
t
I hope this is what you meant!
I would like to plot the results of the robust PCA (pcaCoDa) from the robCompositions package using ggplot2.
Previously, it worked with ggbiplot (https://github.com/vqv/ggbiplot) however, I can no longer get it to work with my current R version (3.6.0).
Is there a way to do a biplot with the pcaCoda results with ggplot2 using CRAN packages?
Here is a working example without using ggplot:
library(robCompositions)
df <- arcticLake
a <- pcaCoDa(df)
biplot(a)
And another example without using the robust PCA, but using the autoplot function:
library(ggplot2)
autoplot(princomp(df))
However, I would like to use the robust PCA with ggplot/autoplot. When I try to plot it, i get the following error:
autoplot(a)
Error: Objects of type pcaCoDa not supported by autoplot.
I also tried the following and also get an error:
autoplot(a$princompOutputClr)
Error in scale.default(data, center = FALSE, scale = 1/scale) :
length of 'scale' must equal the number of columns of 'x'
Any advice? Thanks!
For some reasons that I ignore pcaCoda returns one value less for scale and center compared to the output of other pca methods such as prcomp or princomp. I think that's the reason why autoplot does not want to plot this object.
Alternatively, if you want to apply the robust algortithm, you can use the package pcaMethods available on bioconductor, here i provided an example using the iris dataset that you can found on the documentation of pcaMethods (https://bioconductor.org/packages/release/bioc/html/pcaMethods.html):
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("pcaMethods")
library(pcaMethods)
library(ggplot2)
robust = pca(iris[c(1, 2, 3, 4)], method = "robustPca", scale = "uv", center = TRUE)
iris = merge(iris, scores(robust), by =0)
ggplot(iris, aes( x= PC1, y = PC2, colour = Species))+
geom_point()+
stat_ellipse()
Does it look what you are trying to get ?
I am using lme4 and lmerTest to run a mixed model and then use backward variable elimination (step) for my model. This seems to work well. After running the 'step' function in lmerTest, I plot the final model. The 'plot' results appear similar to ggplot2 output.
I would like to change the layout of the plot. The obvious answer is to do it manually myself creating an original plot(s) with ggplot2. If possible, I would like to simply change the layout of of the output, so that each plot (i.e. plotted dependent variable in the final model) are in their own rows.
See below code and plot to see my results. Note plot has three columns and I would like three rows. Further, I have not provided sample data (let me know if I need too!).
library(lme4)
library(lmerTest)
# Full model
Female.Survival.model.1 <- lmer(Survival.Female ~ Location + Substrate + Location:Substrate + (1|Replicate), data = Transplant.Survival, REML = TRUE)
# lmerTest - backward stepwise elimination of dependent variables
Female.Survival.model.ST <- step(Female.Survival.model.1, reduce.fixed = TRUE, reduce.random = FALSE, ddf = "Kenward-Roger" )
Female.Survival.model.ST
plot(Female.Survival.model.ST)
The function that creates these plots is called plotLSMEANS. You can look at the code for the function via lmerTest:::plotLSMEANS. The reason to look at the code is 1) to verify that, indeed, the plots are based on ggplot2 code and 2) to see if you can figure out what needs to be changed to get what you want.
In this case, it sounds like you'd want facet_wrap to have one column instead of three. I tested with the example from the **lmerTest* function step help page, and it looks like you can simply add a new facet_wrap layer to the plot.
library(ggplot2)
plot(Female.Survival.model.ST) +
facet_wrap(~namesforplots, scales = "free", ncol = 1)
Try this: plot(difflsmeans(Female.Survival.model.ST$model, test.effs = "Location "))
I'm trying to label outliers on a Chi-square Q-Q plot using mvOutlier() function of the MVN package in R.
I have managed to identify the outliers by their labels and get their x-coordinates. I tried placing the former on the plot using text(), but the x- and y-coordinates seem to be flipped.
Building on an example from the documentation:
library(MVN)
data(iris)
versicolor <- iris[51:100, 1:3]
# Mahalanobis distance
result <- mvOutlier(versicolor, qqplot = TRUE, method = "quan")
labelsO<-rownames(result$outlier)[result$outlier[,2]==TRUE]
xcoord<-result$outlier[result$outlier[,2]==TRUE,1]
text(xcoord,label=labelsO)
This produces the following:
I also tried text(x = xcoord, y = xcoord,label = labelsO), which is fine when the points are near the y = x line, but might fail when normality is not satisfied (and the points deviate from this line).
Can someone suggest how to access the Chi-square quantiles or why the x-coordinate of the text() function doesn't seem to obey the input parameters.
Looking inside the mvOutlier function, it looks like it doesn't save the chi-squared values. Right now your text code is treating xcoord as a y-value, and assumes that the actual x value is 1:2. Thankfully the chi-squared value is a fairly simple calculation, as it is rank-based in this case.
result <- mvOutlier(versicolor, qqplot = TRUE, method = "quan")
labelsO<-rownames(result$outlier)[result$outlier[,2]==TRUE]
xcoord<-result$outlier[result$outlier[,2]==TRUE,1]
#recalculate chi-squared values for ranks 50 and 49 (i.e., p=(size:(size-n.outliers + 1))-0.5)/size and df = n.variables = 3
chis = qchisq(((50:49)-0.5)/50,3)
text(xcoord,chis,label=labelsO)
As it is mentioned in the previous reply, MVN packge does not support to label outliers. Although it is not really necessary since it can be done manually, we still might consider to add "labeling outliers" option within mvOutlier(...) function. Thanks for your interest indeed. We might include it in the following updates of the package.
The web-based version of the MVN package has now ability to label outliers (Advanced options under Outlier detection tab). You can access this web-tool through http://www.biosoft.hacettepe.edu.tr/MVN/