I hope the question is correctly posted. It is probably trivial but I am still not able to answer. I checked several options, included the info contained here, but with no fortune. Perhaps, I am still not used to Lattice commands, or the problem is actually not relevant.
I would overlap a barchart with a curve, such as (let's say) a normal standard distribution curve or the density distribution of the data.
Please consider the following data as example, representing the results of several die rolls:
e11 <- data.frame(freq = rep(seq(1, 6, 1), c(53, 46, 42, 65, 47, 44)))
plot_e11 <- barchart(e11,
horizontal = FALSE,
type = "density",
main = "Die results frequencies",
panel = function(x, ...){
panel.barchart(x, ...)
panel.abline(densityplot(e11$freq))})
print(plot_e11a)
It returns the normal barchart instead of the expected result.
How can I add a curve to the barchart, such as the one in the following example?
plot_e11b <- densityplot(e11$freq,
plot.points = FALSE)
panel.abline is the wrong panel function.
panel.abline adds a line of the form y = a + b * x, or vertical
and/or horizontal lines.
densityplot(e11$freq,
panel=function(x, ...) {
tab <- table(x)
panel.barchart(names(tab), tab/length(x),
horizontal=FALSE)
panel.densityplot(x, plot.points=FALSE)},
ylim=c(0, 0.3))
Related
I'm trying to produce a cumulative incidence plot for a competing hazards survival analysis using plot() in R. For some reason, the plot that is produced has a legend that I have not called. The legend is intersecting with the lines on my graph and I can't figure out how to get rid of it. Please help!
My code is as follows:
CompRisk2 <- cuminc(ftime=ADI$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$Typing)
opar <- par(xpd=TRUE) # xpd lets graphics be placed 'outside'
plot(CR,
wh=c(-15, 1.1), # obviously different than the OP's coordinates
xlab="Years",
ylab="Probability of Mortality or Transplant",
xlim=c(0,400),
ylim=c(0,1))
par(opar) # restores graphics parameters
I get the legend to move up and leftward from its original position.
I am working on a script where I need to calculate the coordinates for a beeswarm plot without immediately plotting. When I use beeswarm, I get x-coordinates that aren't swarmed, and more or less the same value:
But if I generate the same plot again it swarms correctly:
And if I use dev.off() I again get no swarming:
The code I used:
n <- 250
df = data.frame(x = floor(runif(n, 0, 5)),
y = rnorm(n = n, mean = 500, sd = 100))
#Plot 1:
A = with(df, beeswarm(y ~ x, do.plot = F))
plot(x = A$x, y=A$y)
#Plot 2:
A = with(df, beeswarm(y ~ x, do.plot = F))
plot(x = A$x, y=A$y)
dev.off()
#Plot 3:
A = with(df, beeswarm(y ~ x, do.plot = F))
plot(x = A$x, y=A$y)
It seems to me like beeswarm uses something like the current plot parameters (or however it is called) to do the swarming and therefore chokes when a plot isn't showing. I have tried to play around with beeswarm parameters such as spacing, breaks, corral, corralWidth, priority, and xlim, but it does not make a difference. FYI: If do.plot is set to TRUE the x-coordinates are calculated correctly, but this is not helpful as I don't want to plot immediately.
Any tips or comments are greatly appreciated!
You're right; beeswarm uses the current plot parameters to calculate the amount of space to leave between points. It seems that setting "do.plot=FALSE" does not do what one would expect, and I'm not sure why I included this parameter.
If you want to control the parameters manually, you could use the functions swarmx or swarmy instead. These functions must be applied to each group separately, e.g.
dfsplitswarmed <- by(df, df$x, function(aa) swarmx(aa$x, aa$y, xsize = 0.075, ysize = 7.5, cex = 1, log = ""))
dfswarmed <- do.call(rbind, dfsplitswarmed)
plot(dfswarmed)
In this case, I set the xsize and ysize values based on what the function would default to for this particular data set. If you can find a set of xsize/ysize values that work for your data, this approach might work for you.
Otherwise, perhaps a simpler approach would be to leave do.plot=TRUE, and then discard the plots.
I can not figure out how the lattice levelplot works. I have played with this now for some time, but could not find reasonable solution.
Sample data:
Data <- data.frame(x=seq(0,20,1),y=runif(21,0,1))
Data.mat <- data.matrix(Data)
Plot with levelplot:
rgb.palette <- colorRampPalette(c("darkgreen","yellow", "red"), space = "rgb")
levelplot(Data.mat, main="", xlab="Time", ylab="", col.regions=rgb.palette(100),
cuts=100, at=seq(0,1,0.1), ylim=c(0,2), scales=list(y=list(at=NULL)))
This is the outcome:
Since, I do not understand how this levelplot really works, I can not make it work. What I would like to have is the colour strips to fill the whole window of the corresponding x (Time).
Alternative solution with other method.
Basically, I'm trying here to plot the increasing risk over time, where the red is the highest risk = 1. I would like to visualize the sequence of possible increase or clustering risk over time.
From ?levelplot we're told that if the first argument is a matrix then "'x' provides the
'z' vector described above, while its rows and columns are
interpreted as the 'x' and 'y' vectors respectively.", so
> m = Data.mat[, 2, drop=FALSE]
> dim(m)
[1] 21 1
> levelplot(m)
plots a levelplot with 21 columns and 1 row, where the levels are determined by the values in m. The formula interface might look like
> df <- data.frame(x=1, y=1:21, z=runif(21))
> levelplot(z ~ y + x, df)
(these approaches do not quite result in the same image).
Unfortunately I don't know much about lattice, but I noted your "Alternative solution with other method", so may I suggest another possibility:
library(plotrix)
color2D.matplot(t(Data[ , 2]), show.legend = TRUE, extremes = c("yellow", "red"))
Heaps of things to do to make it prettier. Still, a start. Of course it is important to consider the breaks in your time variable. In this very simple attempt, regular intervals are implicitly assumed, which happens to be the case in your example.
Update
Following the advice in the 'Details' section in ?color2D.matplot: "The user will have to adjust the plot device dimensions to get regular squares or hexagons, especially when the matrix is not square". Well, well, quite ugly solution.
par(mar = c(5.1, 4.1, 0, 2.1))
windows(width = 10, height = 2.5)
color2D.matplot(t(Data[ , 2]),
show.legend = TRUE,
axes = TRUE,
xlab = "",
ylab = "",
extremes = c("yellow", "red"))
I am working on avoid over crowding of the labels in the following plot:
set.seed(123)
position <- c(rep (0,5), rnorm (5,1,0.1), rnorm (10, 3,0.1), rnorm (3, 4, 0.2), 5, rep(7,5), rnorm (3, 8,2), rnorm (10,9,0.5),
rep (0,5), rnorm (5,1,0.1), rnorm (10, 3,0.1), rnorm (3, 4, 0.2), 5, rep(7,5), rnorm (3, 8,2), rnorm (10,9,0.5))
group <- c(rep (1, length (position)/2),rep (2, length (position)/2) )
mylab <- paste ("MR", 1:length (group), sep = "")
barheight <- 0.5
y.start <- c(group-barheight/2)
y.end <- c(group+barheight/2)
mydf <- data.frame (position, group, barheight, y.start, y.end, mylab)
plot(0,type="n",ylim=c(0,3),xlim=c(0,10),axes=F,ylab="",xlab="")
#Create two horizontal lines
require(fields)
yline(1,lwd=4)
yline(2,lwd=4)
#Create text for the lines
text(10,1.1,"Group 1",cex=0.7)
text(10,2.1,"Group 2",cex=0.7)
#Draw vertical bars
lng = length(position)/2
lg1 = lng+1
lg2 = lng*2
segments(mydf$position[1:lng],mydf$y.start[1:lng],y1=mydf$y.end[1:lng])
segments(mydf$position[lg1:lg2],mydf$y.start[lg1:lg2],y1=mydf$y.end[lg1:lg2])
text(mydf$position[1:lng],mydf$y.start[1:lng]+0.65, mydf$mylab[1:lng], srt = 90)
text(mydf$position[lg1:lg2],mydf$y.start[lg1:lg2]+0.65, mydf$mylab[lg1:lg2], srt = 90)
You can see some areas are crowed with the labels - when x value is same or similar. I want just to display only one label (when there is multiple label at same point). For example,
mydf$position[1:5] are all 0,
but corresponding labels mydf$mylab[1:5] -
MR1 MR2 MR3 MR4 MR5
I just want to display the first one "MR1".
Similarly the following points are too close (say the difference of 0.35), they should be considered a single cluster and first label will be displayed. In this way I would be able to get rid of overcrowding of labels. How can I achieve it ?
If you space the labels out and add some extra lines you can label every marker.
clpl <- function(xdata, names, y=1, dy=0.25, add=FALSE){
o = order(xdata)
xdata=xdata[o]
names=names[o]
if(!add)plot(0,type="n",ylim=c(y-1,y+2),xlim=range(xdata),axes=F,ylab="",xlab="")
abline(h=1,lwd=4)
dy=0.25
segments(xdata,y-dy,xdata,y+dy)
tpos = seq(min(xdata),max(xdata),len=length(xdata))
text(tpos,y+2*dy,names,srt=90,adj=0)
segments(xdata,y+dy,tpos,y+2*dy)
}
Then using your data:
clpl(mydf$position[lg1:lg2],mydf$mylab[lg1:lg2])
gives:
You could then think about labelling clusters underneath the main line.
I've not given much thought to doing multiple lines in a plot, but I think with a bit of mucking with my code and the add parameter it should be possible. You could also use colour to show clusters. I'm fairly sure these techniques are present in some of the clustering packages for R...
Obviously with a lot of markers even this is going to get smushed, but with a lot of clusters the same thing is going to happen. Maybe you end up labelling clusters with a this technique?
In general, I agree with #Joran that cluster labelling can't be automated but you've said that labelling a group of lines with the first label in the cluster would be OK, so it is possible to automate some of the process.
Putting the following code after the line lg2 = lng*2 gives the result shown in the image below:
clust <- cutree(hclust(dist(mydf$position[1:lng])),h=0.75)
u <- rep(T,length(unique(clust)))
clust.labels <- sapply(c(1:lng),function (i)
{
if (u[clust[i]])
{
u[clust[i]] <<- F
as.character(mydf$mylab)[i]
}
else
{
""
}
})
segments(mydf$position[1:lng],mydf$y.start[1:lng],y1=mydf$y.end[1:lng])
segments(mydf$position[lg1:lg2],mydf$y.start[lg1:lg2],y1=mydf$y.end[lg1:lg2])
text(mydf$position[1:lng],mydf$y.start[1:lng]+0.65, clust.labels, srt = 90)
text(mydf$position[lg1:lg2],mydf$y.start[lg1:lg2]+0.65, mydf$mylab[lg1:lg2], srt = 90)
(I've only labelled the clusters on the lower line -- the same principle could be applied to the upper line too). The parameter h of cutree() might have to be adjusted case-by-case to give the resolution of labels that you want, but this approach is at least easier than labelling every cluster by hand.
I'd like to superpose a histogram and an xyplot representing the cumulative distribution function using r's lattice package.
I've tried to accomplish this with custom panel functions, but can't seem to get it right--I'm getting hung up on one plot being univariate and one being bivariate I think.
Here's an example with the two plots I want stacked vertically:
set.seed(1)
x <- rnorm(100, 0, 1)
discrete.cdf <- function(x, decreasing=FALSE){
x <- x[order(x,decreasing=FALSE)]
result <- data.frame(rank=1:length(x),x=x)
result$cdf <- result$rank/nrow(result)
return(result)
}
my.df <- discrete.cdf(x)
chart.hist <- histogram(~x, data=my.df, xlab="")
chart.cdf <- xyplot(100*cdf~x, data=my.df, type="s",
ylab="Cumulative Percent of Total")
graphics.off()
trellis.device(width = 6, height = 8)
print(chart.hist, split = c(1,1,1,2), more = TRUE)
print(chart.cdf, split = c(1,2,1,2))
I'd like these superposed in the same frame, rather than stacked.
The following code doesn't work, nor do any of the simple variations of it that I have tried:
xyplot(cdf~x,data=cdf,
panel=function(...){
panel.xyplot(...)
panel.histogram(~x)
})
You were on the right track with your custom panel function. The trick is passing the correct arguments to the panel.- functions. For panel.histogram, this means not passing a formula and supplying an appropriate value to the breaks argument:
EDIT Proper percent values on y-axis and type of plots
xyplot(100*cdf~x,data=my.df,
panel=function(...){
panel.histogram(..., breaks = do.breaks(range(x), nint = 8),
type = "percent")
panel.xyplot(..., type = "s")
})
This answer is just a placeholder until a better answer comes.
The hist() function from the graphics package has an option called add. The following does what you want in the "classical" way:
plot( my.df$x, my.df$cdf * 100, type= "l" )
hist( my.df$x, add= T )