It seems I misunderstood Sunil's response and somewhat freaked out because it appeared that he was giving the wrong method for making a QQ plot, but was actually demonstrating the sampling variability. My apologies to Sunil.
2009/9/27 Duncan Murdoch <murd...@stats.uwo.ca>: > Eric Thompson wrote: >> >> The supposed example of a Q-Q plot is most certainly not how to make a >> Q-Q plot. I don't even know where to start.... >> >> First off, the two "Q:s in the title of the plot stand for "quantile", >> not "random". The "answer" supplied simply plots two sorted samples of >> a distribution against each other. While this may resemble the general >> shape of a QQ plot, that is where the similarities end. >> > > The empirical quantiles of a sample are simply the sorted values. You can > plot empirical quantiles of one sample versus some version of quantiles from > a distribution (what qqnorm does) or versus empirical quantiles of another > sample (what Sunil did). The randomness in his demonstration did two > things: it generated some data, and it showed the variability of the plot > under repeated sampling. >> >> Some general advice: be careful who you take advice from on the >> internet. > > That's good advice. > > Duncan Murdoch > >> The Wikipedia entry for Q-Q plot may be a good start if you >> don't know what a Q-Q plot is, although you should also use it with >> caution. >> >> Lets say you have some samples that may be normally distributed: >> >> set.seed(1) >> x <- rnorm(30) >> >> # now try with R's built in function >> qqnorm(x, xlim = c(-3, 3), ylim = c(-3, 3)) >> >> # Now try Sunil's "Q-Q plot" method, but for rnorm >> # rather than rgamma >> some_data <- x >> test_data <- rnorm(30) >> points(sort(some_data),sort(test_data), col = "blue") >> >> # Note that the points are NOT the same! >> >> This should have been obvious for the simple reason that the QQ plot >> should not be influenced by the random number generator that you are >> using! A QQ plot is uniquely reproducible. The more general (and >> correct) way to get the QQ plot involves choosing a plotting position >> and the quantile function (e.g. qnorm or qgamma functions in R) of the >> pertinent distribution: >> >> # Sort the data: >> x.s <- sort(x) >> n <- length(x) >> >> # Plotting position (must be careful here in general!) >> p <- ppoints(n) >> >> # Compute the quantile >> x.q <- qnorm(p) >> >> points(x.q, x.s, col = "red") >> >> # and they fall exactly on the points generated by qqnorm(). >> >> Now, you should be able to generalize this for any distribution. Hope >> this helps. >> >> >> Eric Thompson >> >> >> >> >> 2009/9/27 Petar Milin <pmi...@ff.uns.ac.rs>: >> >>> >>> Thanks for the answer. Now, only problem is to to get parameter(s) of a >>> given function. For gamma, I shall try with gammafit() from mhsmm >>> package. >>> Also, I shall look for others appropriate parameter estimates. Will use >>> SuppDists too. >>> >>> Best, >>> PM >>> >>> Sunil Suchindran wrote: >>> >>>> >>>> #same shape >>>> >>>> some_data <- rgamma(500,shape=6,scale=2) >>>> test_data <- rgamma(500,shape=6,scale=2) >>>> plot(sort(some_data),sort(test_data)) >>>> # You can also use qqplot(some_data,test_data) >>>> abline(0,1) >>>> >>>> # different shape >>>> >>>> some_data <- rgamma(500,shape=6,scale=2) >>>> test_data <- rgamma(500,shape=4,scale=2) >>>> plot(sort(some_data),sort(test_data)) >>>> abline(0,1) >>>> >>>> It is helpful to assess the sampling variability, by >>>> creating repeated sets of test_data, and plotting >>>> all of these along with your observations to create >>>> a confidence "envelope". >>>> >>>> The SuppDists provides Inverse Gauss. >>>> >>>> >>>> On Thu, Sep 17, 2009 at 11:46 AM, Petar Milin <pmi...@ff.uns.ac.rs> >>>> wrote: >>>> >>>> Hello! >>>> I am trying with this question again: >>>> I would like to test few distributional assumptions for some >>>> behavioral response data. There are few theories about true >>>> distribution of those data, like: normal, lognormal, gamma, >>>> ex-Gaussian (exponential-Gaussian), Wald (inverse Gaussian) etc. The >>>> best way would be via qq-plot, to show to students differences. >>>> First two are trivial: >>>> qqnorm(dat$X) >>>> qqnorm(log(dat$X)) >>>> Then, things are getting more "hairy". I am not sure how to make >>>> plots for the rest. I tried gamma with: >>>> qqmath(~ X, data=dat, distribution=function(X) >>>> � qgamma(X, shape, scale)) >>>> Which should be the same as: >>>> plot(qgamma(ppoints(dat$X), shape, scale), sort(dat$X)) >>>> Shape and scale parameters I got via mhsmm package that has >>>> gammafit() for shape and scale parameters estimation. >>>> Am I on right track? Does anyone know how to plot the rest: >>>> ex-Gaussian (exponential-Gaussian), Wald (inverse Gaussian)? >>>> >>>> Thanks, >>>> PM >>>> >>>> ______________________________________________ >>>> R-help@r-project.org <mailto:R-help@r-project.org> mailing list >>>> https://stat.ethz.ch/mailman/listinfo/r-help >>>> PLEASE do read the posting guide >>>> http://www.R-project.org/posting-guide.html >>>> <http://www.r-project.org/posting-guide.html> >>>> and provide commented, minimal, self-contained, reproducible code. >>>> >>>> >>>> >>> >>> ______________________________________________ >>> R-help@r-project.org mailing list >>> https://stat.ethz.ch/mailman/listinfo/r-help >>> PLEASE do read the posting guide >>> http://www.R-project.org/posting-guide.html >>> and provide commented, minimal, self-contained, reproducible code. >>> >>> >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> > > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
