R: qqplot using cauchy

1
votes

I have asked this question elsewhere

I want to verify if my data follows a normal or any other type of distribution (like cauchy for example).

I really want to understand how to use qqplot =]

Even though the qqnorm works well:

qqnorm(data);qqline(data)

When I try the qqplot:

qqplot(data, "normal")
qqplot(data, "cauchy")

it generates an error:

Error in plot.window(...) : valores finitos são necessários para 'ylim'

In addition it creates the warning messages:

1: In xy.coords(x, y, xlabel, ylabel, log) : NAs introduced by coercion
2: In min(x) : no non-missing arguments to min; returning Inf
3: In max(x) : no non-missing arguments to max; returning -Inf

1
rdistribution
Please remember to dput() your sample data. Also cross-posting is actually not allowed. – Hack-R
I'm voting to close this question as off-topic because it's cross-posted at CrossValidated – Hack-R
I don't think this should be closed. This is a programming question with a well-defined answer, and the CV folks kind of implicitly sent the asker here by indicating that programming questions were off-topic for CV. Whether the answer to the programming question will help with the underlying statistical analysis is another matter. – Ryan C. Thompson
If anything, it's the CV question that should be closed. – Hong Ooi

1 Answers

2
votes

You should read the documentation for qqplot. The second argument to qqplot should be another data vector, not a string. If you want to compare your data to a specific distribution, you can follow the technique used in qqnorm and generate a vector of quantiles for any distribution. Let's say x is the data we want to plot:

x <- rcauchy(5000)

Since x has 5000 elements, we want to generate 5000 evenly-spaced quantiles from our target distribution. First, let's try the normal distribution:

y.norm <- qnorm(ppoints(length(x)))
qqplot(x, y.norm)

Now let's try the same thing with the Cauchy distribution.

y.cauchy <- qcauchy(ppoints(length(x)))
qqplot(x, y.cauchy)

(Note that the Cauchy distribution in particular will not behave very well in QQ plots, so this may not actually help you with your real goal.)