Re: [R] QQ plotting of various distributions...
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.
Re: [R] QQ plotting of various distributions...
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. Some general advice: be careful who you take advice from on the internet. 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-h...@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.
Re: [R] QQ plotting of various distributions...
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,
Re: [R] QQ plotting of various distributions...
I think it's helpful to show the sampling variability in a QQ plot under repeated sampling. An example is given in Venables, Ripley pg 86. The variance is higher at the tails. Even when the distributions are the same, the QQ plot does not have to resemble a straight line because of sampling. I don't think you can think of any one of these as the correct plot. Also, if the two data sets have an equal number of points, the empirical qq plot is simply a plot of one sorted data set against the other. (Kundu, Statistical Computing, pg 42). On Sun, Sep 27, 2009 at 9:06 AM, Duncan Murdoch murd...@stats.uwo.ca wrote: 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. __ 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.
Re: [R] QQ plotting of various distributions...
Thanks all! I did not want to cause any trouble and, God forbid, offense. I thought, I asked a simple question to improve my understanding and R-skills. It seems that there ain't single gospel truth about QQs. :-) Thanks, again! Best, PM Juliet Hannah wrote: I think it's helpful to show the sampling variability in a QQ plot under repeated sampling. An example is given in Venables, Ripley pg 86. The variance is higher at the tails. Even when the distributions are the same, the QQ plot does not have to resemble a straight line because of sampling. I don't think you can think of any one of these as the correct plot. Also, if the two data sets have an equal number of points, the empirical qq plot is simply a plot of one sorted data set against the other. (Kundu, Statistical Computing, pg 42). On Sun, Sep 27, 2009 at 9:06 AM, Duncan Murdoch murd...@stats.uwo.ca wrote: 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. __ 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.
Re: [R] QQ plotting of various distributions...
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
Re: [R] QQ plotting of various distributions...
#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 mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.htmlhttp://www.r-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] __ 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.
