How to create a boxplot from a PDF? (2024)

I like this idea, but here's a simpler version:

Hi Teja and Tom,

Both are really nice solutions, but I still run into one problem with my data.

So strangly, the histogram doesn't resemble the pdf plotted against the pixelweigth. I get the same result with the inverted distribution.

thanks for your help!!

It looks like your distribution is not symmetric. The normal distribution is symmetric, so it would not resemble the histogram in that respect.

Hi Tom,

the curve was calculated via a Gaussian fit and is symmetric. The x-axis though is based on data, which was fitted with nlinfit and looks like a logarithmic decay. So, after correction the x-axis is not linear anymore. That's why it is so important to plot the pixelweigth against the pdf, otherwise the distribution is not symmetric anymore.

In between, I calculate start parameters for the fit, which is not important for the example.

Next, I fit the pixel position and the Molecular weight of the DNA standard.

The pixelrange is just the y-length of my image in pixel. Here 1:598

After lots of corrections of the original data I fit a Gauss fit through it and calculate the curve, mean and sigma.

After that I need a representation of the normal distributed data along this specialized x-axis (pixelweigth). But not as a curve ... I was asked to display it as a boxplot. And since it is a normal distribution, I thought it must be possible. But Matlab doesn't give an option in "boxplot" to specify a different axis.

thanks for the help! much appreciated :)

Hello Tom, thanks for your answer! Can you explain how to interpolate in this case?

For my problem I created two solutions, but I don't like both.

a) I gauss fit my original data to create the pdf, mean and sigma. Then, I sample with randn (1Mio) & the mean and sigma as parameters. This creates a normal distribution based on my fit which can be plotted via boxplot. Since I already fit my original data with a gaussfit, I am not very interested in the outliers. I just was asked to represent the normal distribution as boxplot for easier comparison of mean and range of data. So, I would feel much better when I wouldn't have to sample a new distribution and of course it takes ages to calculate.

b) I calculate mean and the quartiles of the pdf and extract the respective position from the pixelweigth. Then I draw a barplot(colored for the upper quartile and white for the lower quartile) with error bars. I couldn't make this work, since the pdf is only normally distributed when it is plotted against the pixelweigth.

Bit stuck there ^^ Thanks for your help! Jette