This should be easy to fix, I genuinely don't know what is wrong.
Suppose I wanted to perform the EM algorithm for the Old Faithful data in R and plot the results:
install.packages('mixtools')
library('mixtools')
test<-normalmixEM(faithful$waiting, k=2)
plot(test, which=2, xlim= c(30, 100))
lines(density(faithful$waiting), lty=2, lwd=2)
This works.
But if I wanted to change the x-label or y-axis I get an error message:
plot(test, which=2, xlim= c(30, 100), xlab="", ylim= c(0, 0.06))
lines(density(faithful$waiting), lty=2, lwd=2)
The message is:
argument 4 matches multiple formal arguments
Can someone please help me out? What am I doing wrong? I'm really puzzled.
Thanks!
From the documentation you need to follow this form:
plot(x, whichplots = 1,
loglik = 1 %in% whichplots,
density = 2 %in% whichplots,
xlab1="Iteration", ylab1="Log-Likelihood",
main1="Observed Data Log-Likelihood", col1=1, lwd1=2,
xlab2=NULL, ylab2=NULL, main2=NULL, col2=NULL,
lwd2=2, alpha = 0.05, marginal = FALSE, ...)
you'll need to use xlab2 = ...
plot() is a generic function that actually calls a more specific function (called a "method") depending on what you are trying to plot (see this chapter from Hadley Wickham's Advanced R book for details). In this case, you are feeding-in an object of class "mixEM" to plot(). You can see this by running, e.g.:
class(test)
The generic function plot() is calling the method plot.mixEM() because you are feeding in an object of type "mixEM". To see which parameters of plot.mixEM() you can control, check out that function's help page
?plot.mixEM
The helpfile makes it clear that you need xlab2 as an argument instead of xlab. However, I don't immediately see how to change ylim, so you should view the source code for plot.mixEM to see if there's a way to adjust it other components of the graph:
getAnywhere(plot.mixEM)
Related
I'm trying to produce a cumulative incidence plot for a competing hazards survival analysis using plot() in R. For some reason, the plot that is produced has a legend that I have not called. The legend is intersecting with the lines on my graph and I can't figure out how to get rid of it. Please help!
My code is as follows:
CompRisk2 <- cuminc(ftime=ADI$time_DeathTxCensor, fstatus=ADI$status, group=ADI$natADI_quart)
cols <- c("darkorange","coral1","firebrick1","firebrick4","lightskyblue","darkturquoise","dodgerblue","dodgerblue4")
par(bg="white")
plot(CompRisk2,
col=cols,
xlab="Years",
ylab="Probability of Mortality or Transplant",
xlim=c(0,10),
ylim=c(0,0.6))
Which produces the following plot:
I tried adding the following code to move the legend out of the frame, but I got an error:
legend(0,5, legend=c(11,21,31,41,12,22,32,42),
col=c("darkorange","coral1","firebrick1","firebrick4","lightskyblue","darkturquoise","dodgerblue","dodgerblue4"),
lty=1:2, cex=0.8, text.font=4, box.lty=0)
Error: Error in title(...) : invalid graphics parameter
Any help would be much appreciated!
You are using the cuminc function from the cmprsk package. This produces an object of class cuminc, which has an S3 plot method. ?plot.cuminc shows you the documentation and typing plot.cuminc shows you the code.
There is some slightly obscure code that suggests a workaround:
u <- list(...)
if (length(u) > 0) {
i <- pmatch(names(u), names(formals(legend)), 0)
do.call("legend", c(list(x = wh[1], y = wh[2], legend = curvlab,
col = color, lty = lty, lwd = lwd, bty = "n", bg = -999999),
u[i > 0]))
}
This says that any additional arguments passed in ... whose names match the names of arguments to legend will be passed to legend(). legend() has a plot argument:
plot: logical. If ‘FALSE’, nothing is plotted but the sizes are returned.
So it looks like adding plot=FALSE to your plot() command will work.
In principle you could try looking at the other arguments to legend() and see if any of them will adjust the legend position/size as you want. Unfortunately the x argument to legend (which would determine the horizontal position) is masked by the first argument to plot.cuminc.
I don't think that the ellipsis arguments are intended for the legend call inside plot.cuminc. The code offered in Ben's answer suggests that there might be a wh argument that determines the location of the legend. It is not named within the parameters as "x" in the code he offered, but is rather given as a positionally-defined argument. If you look at the plot.cuminc function you do in fact find that wh is documented.
I cannot test this because you have not offered us access to the ADI-object but my suggestion would be to try:
opar <- par(xpd=TRUE) # xpd lets graphics be placed 'outside'
plot(CompRisk2,
col=cols, wh=c(-.5, 7),
xlab="Years",
ylab="Probability of Mortality or Transplant",
xlim=c(0,10),
ylim=c(0,0.6))
par(opar) # restores original graphics parameters
It's always a bit risky to put out a code chunk without testing, but I'm happy to report that I did find a suitable test and it seems to work reasonably as predicted. Using the code below on the object in the SO question prior question about using the gg-packages for cmprsk:
library(cmprsk)
# some simulated data to get started
comp.risk.data <- data.frame("tfs.days" = rweibull(n = 100, shape = 1, scale = 1)*100,
"status.tfs" = c(sample(c(0,1,1,1,1,2), size=50, replace=T)),
"Typing" = sample(c("A","B","C","D"), size=50, replace=T))
# fitting a competing risks model
CR <- cuminc(ftime = comp.risk.data$tfs.days,
fstatus = comp.risk.data$status.tfs,
cencode = 0,
group = comp.risk.data$Typing)
opar <- par(xpd=TRUE) # xpd lets graphics be placed 'outside'
plot(CR,
wh=c(-15, 1.1), # obviously different than the OP's coordinates
xlab="Years",
ylab="Probability of Mortality or Transplant",
xlim=c(0,400),
ylim=c(0,1))
par(opar) # restores graphics parameters
I get the legend to move up and leftward from its original position.
I am trying to plot the GMM of my dataset using the Mclust package in R. While the plotting is a success, I do not want points to show in the final plot, just the ellipses. For a reference, here is the plot I have obtained:
GMM Plot
But, I want the resulting plot to have only the ellipses, something like this:
GMM desired plot
I have been looking at the Mclust plot page in: https://rdrr.io/cran/mclust/man/plot.Mclust.html and looking at the arguments of the function, I see there is a scope of adding other graphical parameters. Looking at the documentation of the plot function, there is a parameter called type = 'n' which might help to do what I want but when I write it, it produces the following error:
Error in plot.default(data[, 1], data[, 2], type = "n", xlab = xlab, ylab = ylab, :
formal argument "type" matched by multiple actual arguments
For reference, this is the code I used for the first plot:
library(mclust)
Data1_2 <- Mclust(Data, G=15)
summary(Data1_2, parameters = TRUE, classification = TRUE)
plot(Data1_2, what="classification")
The code I tried using for getting the graph below is:
Data1_4 <- Mclust(Data, G=8)
summary(Data1_4, parameters = TRUE, classification = TRUE)
plot(Data1_4, what="classification", type = "n")
Any help on this matter will be appreciated. Thanks!
If you look under the source code of plot.Mclust, it calls plot.Mclust.classification which in turn calls coordProj for the dot and ellipse plot. Inside this function, the size is controlled by the option CEX= and shape PCH=.
So for your purpose, do:
library(mclust)
clu = Mclust(iris[,1:4], G = 3, what="classification")
plot(clu,what="classification",CEX=0)
I'm over-plotting three densities onto my data histogram, using denscomp in the fitdistrplus package in R. The code below is working perfectly, but I don't know how to make the lines thicker.
denscomp(list(TryWeibull, TryGamma, TryLognormal), legendtext = plot.legend,
fitcol = c("indianred3","gray38", "darkblue"), fitlty = c("dashed", "longdash", "dotdash"),
xlab = "Age", ylab = "Proportion", main="")
fitcol is giving me the correct colours, fitly is giving me the correct line types, but I can't work out the command to make the lines thicker. I have two distribution densities that are close together and I have been unsuccessful in clearly identifying them using colour/line type differences. .
I am trying to de-emphasize the Weibull and emphasise the gamma and lognormal. The proportions are estimates, so I am trying to fit the general shape, not the exact values.
I can't see an option in the denscomp function to specify line widths. I would rather not use the ggplot option, but can shift to that if required. I was hoping there was a function option I'm overlooking.
Edited to add: I raised this as a feature request on GitHub and it has been implemented into the package.
Although the author of this package allows you to specify multiple line types (fitlty) and line colours (fitcol), they didn't allow you to specify multiple line widths. But since R is open-source, you are free to modify the function in any way.
Type the following at the R console:
fix(denscomp)
Then add a new argument to the function after fitcol, called fitlwd.
..., fitcol, fitlwd, addlegend = TRUE, ...
Then after line 30 add the following:
if (missing(fitlwd))
fitlwd <- 1
Then after line 34 add the following:
fitlwd <- rep(fitlwd, length.out = nft)
Then modify line 136 as follows:
col = fitcol[i], lwd=fitlwd[i], ...)
Finally, modify line 142:
col = fitcol, lwd=fitlwd,
Save and call the new function as before but now specifying the fitlwd argument:
denscomp(..., fitlwd=c(1,3,3))
I had the same question and followed Edward's solution, which was great and I learnt a lot, but it turned out you can just use ggplot to do that.
denscomp(..., plotstyle = "ggplot") + geom_line(linetype = "dashed",size = 1))
Hoping for some pointers or some experiences insight as i'm literally losing my mind over this, been trying for 2 full days to set up the right values to have a function spit out clean simple line plots from the gbm.plot function (packages dismo & gbm).
Here's where I start. bty=n in par to turn off the box & leave me with only left & bottom axes. Gbm.plot typically spits out one plot per explanatory variable, so usually 6 plots etc, but I'm tweaking it to do one per variable & looping it. I've removed the loop & lots of other code so it's easy to see what's going on.
png(filename = "whatever.png",width=4*480, height=4*480, units="px", pointsize=80, bg="white", res = NA, family="", type="cairo-png")
par(mar=c(2.6,2,0.4,0.5), fig=c(0,1,0.1,1), las=1, bty="n", mgp=c(1.6,0.5,0))
gbm.plot(my_gbm_model,
n.plots=1,
plot.layout = c(1,1),
y.label = "",
write.title=F,
variable.no = 1, #this is part of the multiple plots thing, calls the explanatory variable
lwd=8, #this controls the width of the main result line ONLY
rug=F)
dev.off()
So this is what the starting condition looks like. Aim: make the axes & ticks thicker. That's it.
Putting "lwd=20" in par does nothing.
Adding axes=F into gbm.plot() turns the axes and their numbers off. So I conclude that the control of these axes is handled by gbm.plot, not par. Here's where it get's frustrating and crap. Accepted wisdom from searches says that lwd should control this but it only controls the wiggly centre line as per my note above. So maybe I could add axis(side=1, lwd=8) into gbm.plot() ?
It runs but inexplicably adds a smoother! (which is very thin & hard to see on the web but it's there, I promise). It adds these warnings:
In if (smooth & is.vector(predictors[[j]])) { ... :
the condition has length > 1 and only the first element will be used
Fine, R's going to be a dick for seemingly no reason, I'll keep plugging the leaks as they come up. New code with axis as before and now smoother turned off:
png(filename = "whatever.png",width=4*480, height=4*480, units="px", pointsize=80, bg="white", res = NA, family="", type="cairo-png")
par(mar=c(2.6,2,0.4,0.5), fig=c(0,1,0.1,1), las=1, bty="n", mgp=c(1.6,0.5,0))
gbm.plot(my_gbm_model,
n.plots=1,
plot.layout = c(1,1),
y.label = "",
write.title=F,
variable.no = 1,
lwd=8,
rug=F,
smooth=F,
axis(side=1,lwd=8))
dev.off()
Gives error:
Error in axis(side = 1, lwd = 8) : plot.new has not been called yet
So it's CLEARLY drawing axes within plot since I can't affect the axes from par and I can turn them off in plot. I can do what I want and make one axis bold, but that results in a smoother and warnings. I can turn the smoother off, but then it fails because it says plot.new hadn't been called. And this doesn't even account for the other axis I have to deal with, which also causes the plot.new failure if I call 2 axis sequentially and allow the smoother.
Am I the butt of a big joke here, or am I missing something obvious? It took me long enough to work out that par is supposed to be before all plots unless you're outputting them with png etc in which case it has to be between png & plot - unbelievably this info isn't in ?par. I know I'm going off topic by ranting, sorry, but yeah, 2 full days. Has this been everyone's experience of plotting in R?
I'm going to open the vodka in the freezer. I appreciate I've not put the full reproducible code here, apologies, I can do if absolutely necessary, but it's such a huge timesuck to get to reproducible stage and I'm hoping someone can see a basic logical/coding failure screaming out at them from what I've given.
Thanks guys.
EDIT: reproducibility
core data csv: https://drive.google.com/file/d/0B6LsdZetdypkWnBJVDJ5U3l4UFU
(I've tried to make these data reproducible before and I can't work out how to do so)
samples<-read.csv("data.csv", header = TRUE, row.names=NULL)
my_gbm_model<-gbm.step(data=samples, gbm.x=1:6, gbm.y=7, family = "bernoulli", tree.complexity = 2, learning.rate = 0.01, bag.fraction = 0.5))
Here's what will widen your axis ticks:
..... , lwd.ticks=4 , ...
I predict on the basis of no testing because I keep getting errors with what limited code you have provided) that it will get handled correctly in either gbm.plot or in a subsequent axis call. There will need to be a subsequent axis call, two of them in fact (because as you noted 'lwd' gets passed around indiscriminately):
png(filename = "whatever.png",width=4*480, height=4*480, units="px", pointsize=80, bg="white", res = NA, family="", type="cairo-png")
par(mar=c(2.6,2,0.4,0.5), fig=c(0,1,0.1,1), las=1, bty="n", mgp=c(1.6,0.5,0))
gbm.plot(my_gbm_model,
n.plots=1,
plot.layout = c(1,1),
y.label = "",
write.title=F,
variable.no = 1,
lwd=8,
rug=F,
smooth=F, axes="F",
axis(side=1,lwd=8))
axis(1, lwd.ticks=4, lwd=4)
# the only way to prevent `lwd` from also affecting plot line
axis(2, lwd.ticks=4, lwd=4)
dev.off()
This is what I see with a simple example:
png(); Speed <- cars$speed
Distance <- cars$dist
plot(Speed, Distance,
panel.first = lines(stats::lowess(Speed, Distance), lty = "dashed"),
pch = 0, cex = 1.2, col = "blue", axes=FALSE)
axis(1, lwd.ticks=4, lwd=4)
axis(2, lwd.ticks=4, lwd=4)
dev.off()
Why is R inconsistent with the add parameter in the plot() function?
It sometimes works and sometimes doesn't!
In this example, it takes the parameter add=TRUE with no problem:
plot(0:10, 0:10*3)
plot(identity, add=TRUE, xlim=c(0,10))
plot(function (x) { sin(x)*10 }, add=TRUE, xlim=c(0,10))
But when I issue
plot(c(2, 3, 4), c(20,10,15), add=TRUE, pch="A")
It doesn't work!! It says that "add" is not a graphical parameter.
Please do not write that I should use points() instead. I know I can use it.
I want to understand the strange behaviour of R - why does it sometimes work and sometimes not?
This is admittedly annoying and inconsistent, but it's explicable.
identity is an object of a class — function — that has a plot method (plot.function) with an add argument, while the default plot method does not have an add argument.
In general, when trying to plot object bar, you should try class(bar); if it is of class foo then try methods(class="foo") to see that it has a plot method, or methods("plot") to see that plot.foo exists. Try ?plot.foo (or help("plot.foo") to see help, or plot.foo to see the function itself. (If the method is a private function in the package mypkg you may need mypkg:::plot_foo or or getAnywhere(plot.foo) to find it.)
This is because when you call plot(0:10, 0:10*3) or plot(c(2, 3, 4), c(20,10,15)), you are indirectly calling plot.default(), which in turn calls plot.xy(), whereas the other two calls you mention are running plot.function(). add is an argument for plot.function(), but not for plot.xy().
You can get around this inconsistency by setting par(new = TRUE), but then you need to make sure that you don't add fresh axis labels or redraw the axes. EDIT: As pointed out in the comment, you have to make sure that the range is the same as the previous plot. e.g.:
plot(0:10, 0:10*3)
plot(identity, add=T, xlim=c(0,10))
plot(function (x) { sin(x)*10 }, add=T, xlim=c(0,10))
par(new = TRUE)
plot(c(2, 3, 4), c(20,10,15), pch="A",
axes = FALSE, ## don't redraw the axes
xlab = '', ylab = '', ## no fresh axis labels
xlim = c(0,10), ylim = c(0,30)) ## keep the same limits as before
As Ben Bolker mentions, methods('plot') will show you what methods can be called when running plot() - the different methods have different arguments, which are listed when you call args(plot.foo) or in the help page ?plot.foo