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 "))
Related
I'm trying to plot the regression coefficient where I get a plot like this
For example I would like to add the model name into the plot such as this on the top of the image
`PC2 ~ Index + Lane + Gen`
How to do that? I would like to add the model names to the respective plots
My code which I'm using
plot_list = list()
for (i in seq(length(bb))) {
p = ggcoefstats(bb[[i]])
plot_list[[i]] = p
}
pdf("plots1.pdf",height = 10,width = 6)
for (i in seq(length(bb))) {
print(plot_list[[i]])
}
dev.off()
My data bb which is my model output
> bb
$`PC2 ~ Sex + Index + Lane`
Call:
lm(formula = x, data = mrna.pcs)
Coefficients:
(Intercept) SexM IndexAR002 IndexAR003 IndexAR004 IndexAR005 IndexAR006 IndexAR007 IndexAR008 IndexAR009 IndexAR010
0.8055 -11.3695 2.6964 -7.9438 -1.7453 -10.5309 -10.7135 -9.8775 4.4912 0.7830 -4.8674
IndexAR011 IndexAR012 IndexAR013 IndexAR014 IndexAR015 IndexAR016 IndexAR018 IndexAR019 IndexAR020 IndexAR021 IndexAR022
-8.1402 -10.6590 -8.1678 1.0441 -0.4174 7.2952 12.9489 -7.4206 -6.6895 -10.6862 4.9863
IndexAR023 IndexAR025 IndexAR027 Lane2 Lane3 Lane4 Lane5 Lane6 Lane7 Lane8
1.5614 -15.7488 -1.5925 12.3677 -10.3617 -55.5894 25.6420 34.1251 42.4888 16.1013
Any suggestion or help would be really appreciated
I was not previously familiar with this package your using to create this visual, which is not actually ggplot2. According to this page, there is a title parameter that you should use within the function: https://indrajeetpatil.github.io/ggstatsplot/reference/ggcoefstats.html
You might benefit by converting this to ggplot2, however. The first thing I thought when I saw your visual was "this needs ggrepel". That will automatically move your text labels around so that they do not overlap. Seeing those coefficient values would seem to be very important. AFAIK, ggrepel only works with ggplot2 not other graphing libraries.
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$data #Replace the data with the scaled version
t
I hope this is what you meant!
I'm trying to create a ggplot and add results of a correlation test I have done.
Something along the lines of:
p+annotate("text",x=12.5,y=15.25,label=c(cor.test$estimate,cor.test$p.value))
I keep getting error messages no matter what I try.
Any ideas?
I have actually managed to add stat details to the plot by using stat_cor() from the package ggpubr
library(ggpubr)
p+stat_cor(method="pearson")
There is a package in development that can do this for you (ggstatsplot is on CRAN).
Here is an example of how to create correlation plot:
ggstatsplot::ggscatterstats(data = iris, x = Sepal.Length, y = Sepal.Width)
This will produce a plot that looks like the following (you can similarly get results from Spearman's rho (type = 'spearman') or robust correlation test (type = 'robust')):
Check out the documentation of the function for more.
I'm trying to label outliers on a Chi-square Q-Q plot using mvOutlier() function of the MVN package in R.
I have managed to identify the outliers by their labels and get their x-coordinates. I tried placing the former on the plot using text(), but the x- and y-coordinates seem to be flipped.
Building on an example from the documentation:
library(MVN)
data(iris)
versicolor <- iris[51:100, 1:3]
# Mahalanobis distance
result <- mvOutlier(versicolor, qqplot = TRUE, method = "quan")
labelsO<-rownames(result$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$outlier[,2]==TRUE,1]
#recalculate chi-squared values for ranks 50 and 49 (i.e., p=(size:(size-n.outliers + 1))-0.5)/size and df = n.variables = 3
chis = qchisq(((50:49)-0.5)/50,3)
text(xcoord,chis,label=labelsO)
As it is mentioned in the previous reply, MVN packge does not support to label outliers. Although it is not really necessary since it can be done manually, we still might consider to add "labeling outliers" option within mvOutlier(...) function. Thanks for your interest indeed. We might include it in the following updates of the package.
The web-based version of the MVN package has now ability to label outliers (Advanced options under Outlier detection tab). You can access this web-tool through http://www.biosoft.hacettepe.edu.tr/MVN/
Here is some code that tries to compute the marginal effects of each of the predictors in a model (using the effects package) and then plot the results. To do this, I am looping over the "term.labels" attribute of the glm terms object).
library(DAAG)
library(effects)
formula = pres.abs ~ altitude + distance + NoOfPools + NoOfSites + avrain + meanmin + meanmax
summary(logitFrogs <- glm(formula = formula, data = frogs, family = binomial(link = "logit")))
par(mfrow = c(4, 2))
for (predictorName in attr(logitFrogs$terms, "term.labels")) {
print(predictorName)
effLogitFrogs <- effect(predictorName, logitFrogs)
plot(effLogitFrogs)
}
This produces no picture at all. On the other hand, explicitly stating the predictor names does work:
effLogitFrogs <- effect("distance", logitFrogs)
plot(effLogitFrogs)
What am I doing wrong?
Although you call function plot(), actually it calls function plot.eff() and it is lattice plot and so par() argument is ignored. One solution is to use function allEffects() and then plot(). This will call function plot.efflist(). With this function you do not need for loop because all plots are made automatically.
effLogitFrogs <- allEffects(predictorName, logitFrogs)
plot(effLogitFrogs)
EDIT - solution with for loop
There is "ugly" solution to use with for() loop. For this we need also package grid. First, make as variables number of rows and columns (now it works only with 1 or 2 columns). Then grid.newpage() and pushViewport() set graphical window.
Predictor names are stored in vector outside the loop. Using functions pushViewport() and popViewport() all plots are put in the same graphical window.
library(lattice)
library(grid)
n.col=2
n.row= 4
grid.newpage()
pushViewport(viewport(layout = grid.layout(n.row,n.col)))
predictorName <- attr(logitFrogs$terms, "term.labels")
for (i in 1:length(predictorName)) {
print(predictorName[i])
effLogitFrogs <- effect(predictorName[i], logitFrogs)
pushViewport(viewport(layout.pos.col=ceiling(i/n.row), layout.pos.row=ifelse(i-n.row<=0,i,i-n.row)))
p<-plot(effLogitFrogs)
print(p,newpage=FALSE)
popViewport(1)
}
add print to your loop resolve the problem.
print(plot(effLogitFrogs))
plot call plot.eff , which create the plot without printing it.
allEffects generete an object of type eff.list. When we try to plot this object, its calls plot.efflist function which prints the plot so no need to call print like plot.eff.