Adding points with error bars into a Matlab scatter plot - plot

I have performed a multidimensional cluster analysis in matlab. For each cluster, I have calculated mean and covariance (assuming conditional independence).
I have chosen two or three dimensions out of my raw data and plotted it into a scatter or scatter3 plot.
Now I would like to add the cluster-means and the corresponding standart deviations into the same plot.
In other words, I wand to add some data points with error bars to a scatter plot.
This question is almost what I want. But I would be ok with bars instead of boxes and I wonder if in that case there is a built-in way to do it with less effort.
Any suggestions on how to do that?

Once you realize that line segments will probably suffice for your purpose (and may be less ugly than the usual error bars with the whiskers, depending on the number of points), you can do something pretty simple (which applies to probably any plotting package, not just MATLAB).
Just plot a scatter, then write a loop to plot all line-segments you want corresponding to error bars (or do it in the opposite order like I did with error bars first then the scatter plot, depending if you want your dots or your error bars on top).
Here is the simple MATLAB code, along with an example figure showing error bars in two dimensions (sorry for the boring near-linearity):
As you can see, you can plot error bars for each axis in different colors to aid in visualization.
function scatterError(x, y, xe, ye, varargin)
%Brandon Barker 01/20/2014
nD = length(x);
%Make these defaults later:
dotColor = [1 0.3 0.3]; % conservative pink
yeColor = [0, 0.4, 0.8]; % bright navy blue
xeColor = [0.35, 0.35, 0.35]; % not-too-dark grey
dotSize = 23;
figure();
set(gcf, 'Position', get(0,'Screensize')); % Maximize figure.
set(gca, 'FontSize', 23);
hold all;
for i = 1:nD
plot([(x(i) - xe(i)) (x(i) + xe(i))], [y(i) y(i)], 'Color', xeColor);
plot([x(i) x(i)], [(y(i) - ye(i)) (y(i) + ye(i))], 'Color', yeColor);
end
scatter(x, y, dotSize, repmat(dotColor, nD, 1));
set(gca, varargin{:});
axis square;
With some extra work, it wouldn't be too hard to add whiskers to your error bars if you really want them.

If you are not too picky about what the graph looks like and are looking for performance, a builtin function is indeed often a good choice.
My first thought would be to try using a boxplot, it has quite a lot of options so probably one combination of them will give you the result you need.
Sidenote: At first sight the answer you referred to does not look very inefficient so you may have to manage your expectations when it comes to achievable speedups.

Related

Plotting histograms with R; y axis keeps changing to frequency from proportion/probability

I try to overlay two histograms in the same plane but the option Probability=TRUE (relative frequencies) in hist() is not effective with the code below. It is a problem because the two samples have very different sizes (length(cl1)=9 and length(cl2)=339) and, with this script, I cannot vizualize differences between both histograms because each shows frequencies. How can I overlap two histograms with the same bin width, showing relative frequencies?
c1<-hist(dataList[["cl1"]],xlim=range(minx,maxx),breaks=seq(minx,maxx,pasx),col=rgb(1,0,0,1/4),main=paste(paramlab,"Group",groupnum,"cl1",sep=" "),xlab="",probability=TRUE)
c2<-hist(dataList[["cl2"]],xlim=range(minx,maxx),breaks=seq(minx,maxx,pasx),col=rgb(0,0,1,1/4),main=paste(paramlab,"Group",groupnum,"cl2",sep=" "),xlab="",probability=TRUE)
plot(c1, col=rgb(1,0,0,1/4), xlim=c(minx,maxx), main=paste(paramlab,"Group",groupnum,sep=" "),xlab="")# first histogram
plot(c2, col=rgb(0,0,1,1/4), xlim=c(minx,maxx), add=T)
cl1Col <- rgb(1,0,0,1/4)
cl2Col <- rgb(0,0,1,1/4)
legend('topright',c('Cl1','Cl2'),
fill = c(cl1Col , cl2Col ), bty = 'n',
border = NA)
Thanks in advance for your help!
When you call plot on an object of class histogram (like c1), it calls the S3 method for the histogram. Namely, plot.histogram. You can see the code for this function if you type graphics:::plot.histogram and you can see its help under ?plot.histogram. The help file for that function states:
freq logical; if TRUE, the histogram graphic is to present a
representation of frequencies, i.e, x$counts; if FALSE, relative
frequencies (probabilities), i.e., x$density, are plotted. The default
is true for equidistant breaks and false otherwise.
So, when plot renders a histogram it doesn't use the previously specified probability or freq arguments, it tries to figure it out for itself. The reason for this is obvious if you dig around inside c1, it contains all of the data necessarily for the plot, but does not specify how it should be rendered.
So, the solution is to reiterate the argument freq=FALSE when you run the plot functions. Notably, freq=FALSE works whereas probability=TRUE does not because plot.histogram does not have a probability option. So, your plot code will be:
plot(c1, col=rgb(1,0,0,1/4), xlim=c(minx,maxx), main=paste(paramlab,"Group",groupnum,sep=" "),xlab="",freq=FALSE)# first histogram
plot(c2, col=rgb(0,0,1,1/4), xlim=c(minx,maxx), add=T, freq=FALSE)
This all seems like a oversight/idiosyncratic decision (or lack thereof) on the part of the R devs. To their credit it is appropriately documented and is not "unexpected behavior" (although I certainly didn't expect it). I wonder where such oddness should be reported, if it should be reported at all.

How to represent datapoints that are out of scale in R

I am trying to plot a set of data in R
x <- c(1,4,5,3,2,25)
my Y scale is fixed at 20 so that the last datapoint would effectively not be visible on the plot if i execute the following code
plot(x, ylim=c(0,20), type='l')
i wanted to show the range of the outlying datapoint by showing a smaller box above the plot, with an independent Y scale, representing only this last datapoint.
is there any package or way to approach this problem?
You may try axis.break (plotrix package) http://rss.acs.unt.edu/Rdoc/library/plotrix/html/axis.break.html, with which you can define the axis to break, the style, size and color of the break marker.
The potential disadvantage of this approach is that the trend perception might be fooled. Good luck!

Geom_ribbon() just turns the graph blank

Hi I got a data frame weekly.mean.values with the following structure:
week:mean:ci.lower:ci.upper
Where week is a factor; mean, ci.lower and ci.upper are numeric. For each week, there is only one mean, and one ci.lower or ci.upper.
I was trying to plot a shaded area inside of the 95% confidence interval around the mean, with the following code:
ggplot(weekly.mean.values,aes(x=week,y=mean)) +
geom_line() +
geom_ribbon(aes(ymin=ci.lower,ymax=ci.upper))
The plot, however, came out blank (that is only with x-axis and y-axis present, but no lines, or points, let alone shaded areas).
If I removed the geom_ribbon part, I did get a line. I know that this should be a very simple task but I don't know why I couldn't get geom_ribbon to plot what I wanted. Any hint would be truly appreciated.
I realize this thread is super old, but google still find it.
The answer is that you need to set the ymin and ymax to use a part of the data you are using on the y-axis. It you set them to scalar values then the ribbon covers the entire plot from top to bottom.
You can use
ymin=0
ymax=mean
to go from 0 to your y-point or even
ymin=mean-1
ymax=mean+1
to have the ribbon cover a strip encompassing your actual data.
I may be missing something, but the ribbon will be plotted filled with grey20 by default. You are plotting this layer on top of the data so no wonder it obscures it. Also, it is also possible that the limits for the plot axes derived from the data provided to the initial ggplot() call will not be sufficient to contain the confidence interval ribbon. In that case, I would not be surprised to see a grey/blank plot.
To see if this is the problem, try altering your geom_ribbon() line to:
geom_ribbon(aes(ymin=ci.lower,ymax=ci.upper), alpha = 0.5)
which will plot the ribbon with transparency whic should show the data underneath if the problem is what I think it is.
If so, set the x and y limits to the range of the data +/- the confidence interval you wish to plot and swap the order of the layers (i.e. draw the line on top of the ribbon), and use transparency in the ribbon to show the grid through it.
From ggplot's docs for geom_ribbon (2.1.0):
For each continuous x value, geom_interval displays a y interval. geom_area is a special case of geom_ribbon, where the minimum of the range is fixed to 0.
In this case, x values cannot be factors for geom_ribbon. One solution would be to convert week from a factor to a numeric. e.g.
ggplot(weekly.mean.values,aes(x=as.numeric(week),y=mean)) +
geom_line() +
geom_ribbon(aes(ymin=ci.lower,ymax=ci.upper))
geom_line should handle the switch from factor to numeric without incident, although the X axis scale may display differently.

Axis-labeling in R histogram and density plots; multiple overlays of density plots

I have two related problems.
Problem 1: I'm currently using the code below to generate a histogram overlayed with a density plot:
hist(x,prob=T,col="gray")
axis(side=1, at=seq(0,100, 20), labels=seq(0,100,20))
lines(density(x))
I've pasted the data (i.e. x above) here.
I have two issues with the code as it stands:
the last tick and label (100) of the x-axis does not appear on the histogram/plot. How can I put these on?
I'd like the y-axis to be of count or frequency rather than density, but I'd like to retain the density plot as an overlay on the histogram. How can I do this?
Problem 2: using a similar solution to problem 1, I now want to overlay three density plots (not histograms), again with frequency on the y-axis instead of density. The three data sets are at:
http://pastebin.com/z5X7yTLS
http://pastebin.com/Qg8mHg6D
http://pastebin.com/aqfC42fL
Here's your first 2 questions:
myhist <- hist(x,prob=FALSE,col="gray",xlim=c(0,100))
dens <- density(x)
axis(side=1, at=seq(0,100, 20), labels=seq(0,100,20))
lines(dens$x,dens$y*(1/sum(myhist$density))*length(x))
The histogram has a bin width of 5, which is also equal to 1/sum(myhist$density), whereas the density(x)$x are in small jumps, around .2 in your case (512 even steps). sum(density(x)$y) is some strange number definitely not 1, but that is because it goes in small steps, when divided by the x interval it is approximately 1: sum(density(x)$y)/(1/diff(density(x)$x)[1]) . You don't need to do this later because it's already matched up with its own odd x values. Scale 1) for the bin width of hist() and 2) for the frequency of x length(x), as DWin says. The last axis tick became visible after setting the xlim argument.
To do your problem 2, set up a plot with the correct dimensions (xlim and ylim), with type = "n", then draw 3 lines for the densities, scaled using something similar to the density line above. Think however about whether you want those semi continuous lines to reflect the heights of imaginary bars with bin width 5... You see how that might make the density lines exaggerate the counts at any particular point?
Although this is an aged thread, if anyone catches this. I would only think it is a 'good idea' to forego translating the y density to count scales based on what the user is attempting to do.
There are perfectly good reasons for using frequency as the y value. One idea in particular that comes to mind is that using counts for the y scale value can give an analyst a good idea about where to begin the 'data hunt' for stratifying heterogenous data, if a mixed distribution model cannot soundly or intuitively be applied.
In practice, overlaying a density estimate over the observed histogram can be very useful in data quality checks. For example, in the above, if I were looking at the above graphic as a single source of data with the assumption that it describes "1 thing" and I wish to model this as "1 thing", I have an issue. That is, I have heterogeneous data which may require some level of stratification. The density overlay then becomes a simple visual tool for detecting heterogeneity (apart from using log transformations to smooth between-interval variation), and a direction (locations of the mixed distributions) for stratifying the data.

Density plot in ggplot2 using log scale

I'd like to use ggplot2 density geometry using a log transformation for the x scale:
qplot(rating, data=movies, geom="density", log="x")
This, however, produces a chart with probabilities larger than 1. One solution that seems to work is to scale the dataset before calling qplot:
qplot(rating, data=transform(movies, rating=log(rating))
But then the x axis doesn't look nice. What is the correct way to handle this?
It seems that my question doesn't not, in fact, make sense. It seems that it is OK that probability densities are larger than one, as per [2]. What is important is that the integral over the entire space is equal to one [3].
This gives the right answer.
qplot(rating, y = ..scaled.., data=movies, geom="density", log="x")
stat_density produces new values, one of them is ..scaled.. which is the density scaled from 0 to 1.
HTH

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