Plotting an hyperbolic density using ggplot

[dtotaj]

Hi guys!
I just started learning R and i’ve run into something that I can’t solve.
My problem is the following: I have a time series of daily return on a stock. I have used ghyp package to fit my daily returns to a hyperbolic distribution. Now I want to plot density of that hyperbolic distribution (eg. NIG).

I want to do my plot using ggplot but the ggplot can’t read ghyp package.
Would appreciate any advice.

[andresrcs]

Hi Denis, Welcome!

Could you ask this with a minimal REPRoducible EXample (reprex)? A reprex makes it much easier for others to understand your issue and figure out how to help.

If you've never heard of a reprex before, you might want to start by reading this FAQ:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[mara]

@dtotaj

I want to do my plot using ggplot but the ggplot can’t read ghyp package.

Could you say more about what you mean by this (or, better yet, as suggested, include a small example)? From a quick look at the ghyp documentation, it seems like several base plotting methods are in there. You should be able to recreate these using ggplot2, it's a matter of having your data in a certain format (e.g. a data frame).

[ron]

I fully endorse @andresrcs and @mara reprex suggestion.

But, without having any experience of ghyp, I would generally plot a distribution by:

• defining the range of x values I want to cover;
• calculate the y values using the relevant density function; and
• put x and y into a data.frame and then plot it with ggplot and geom_line.

For a Normal distribution that might be:

library(ggplot2)
x <- seq(-3,3,0.01)
y <- dnorm(x, mean = 0, sd = 1)
DF <- data.frame(x = x, y = y)
ggplot(DF,aes(x = x, y = y)) + geom_line()

^{Created on 2019-04-03 by the reprex package (v0.2.1)}

So, assuming you there is a dghyp function in the ghyp package and you obtain parameters for your hyperbolic distribution from your fitting process, I think you should be able to pretty much follow that pattern.

Hope this helps.

Ron.

[dtotaj]

@mara sorry for not clarifying good enough.

I have a time series of daily returns on S&P 500 (sample size 3051). I plotted a histogram of daily returns and plotted a normal distribution using parameters (sigma and mean) of my sample to visualise "goodness of fit". To do that I run the following code:

ggplot(snp.srdf, aes(x=snp.srdf$SNP.Adjusted))+geom_histogram(aes(y= stat(density)), binwidth = 0.008, color="azure4" ) + stat_function(fun=dnorm, args = list(mean=mean(snp.srdf$SNP.Adjusted), sd=sd(snp.srdf$SNP.Adjusted)), lwd=1, col="darkgray")

Which looks like this:

From the plot it is visible that a gaussian distribution is not a proper fit so I use StepAIC.ghyp (snp.srdf$SNP.Adjusted) to find the appropriate distribution. The best fit turns to be a symmetric NIG with certain parameters. I fitted my data into that distribution using fit.NIGuv() and saved the results.

So now I am trying to plot the same histogram from the beginning (histogram of my sample) with a NIG density. I have tried to use the stat_function with dghyp but it doesn't work.

Do you know of any way plotting that distribution using ggplot or is there a function in which I can add the NIG parameters (sigma, mu, lambda, chi) to get a density plot.

Thank you and regards!

[mara]

Hmm, well the normal inverse gaussian (if that's what NIG stands for), isn't one of the built-in kernels for ggplot2's geom_density()

Smoothed density estimates — geom_density

See available density kernels here.

EDIT: turns out these helpers only take the stats package distributions as well.
You might take a look at the post here: Plotting Probability Distributions with ggplot2 and ggfortify, I haven't tried myself yet, but could be helpful.

[FJCC]

Looking at the documentation of ghyp, the fit.NIGuv() function returns an object that can be fed to the dghyp() function, which returns the density of the distribution.

dghyp(x, object = ghyp(), logvalue = FALSE

You can make a data frame with

MySeq <- seq(-0.15, 0.15, length.out = 100)
fitDF <- data.frame(x = MySeq,
                    y =  dghyp(MySeq, Your_fit.NIGuvObject))

You should be able to add that to your plot with

+ geom_line(mapping = aes(x = x, y = y), data = fitDF)

I hope I didn't make too many mistakes writing that code with nothing to test it on.