why does probplot return negative/larger than 1 values ?

3 次查看（过去 30 天）
Itzik Ben Shabat
Itzik Ben Shabat 2015-10-9
评论： Tom Lane 2015-10-24
Hi,
h=probplot(Y);
is supposed to show the probability distrbution of the values in Y. so probability cant be negative or larger than 1 but if i check
get(h,'ydata');
it has both negative values and values larger than 1. how is that possible ? perhaps i misunderstood something?

请先登录，再回答此问题。

回答（2 个）

Walter Roberson
Walter Roberson 2015-10-9
get(h,'ydata') is going to return whatever data the routine needed to generate in order to plot nicely. If you look at the y axis locations you can see that the spacing is not nearly equidistant in probability value, with values being close together near the middle and further apart near the top or bottom.
A plausible explanation would be that the data stored is in terms of standard deviations from mean 0.5
  3 个评论
显示 1更早的评论
Walter Roberson
Walter Roberson 2015-10-9
http://www.mathworks.com/help/stats/cdf.html if you know the probability distribution. If you do not know the probability distribution and want to find the "best" fit, then you would use other routines.
Tom Lane
Tom Lane 2015-10-24
For a normal distribution, Walter is correct. The plot is constructed from standard normal values on the Y axis. The ticks are positioned and labeled, though, to indicate the probability values rather than the normal values. For instance, the tick mark labeled 0.75 is at the position where the plotted points have the value y=norminv(.75). Why all this? Because this construction (y values from standard normal) means that x data having a normal distribution with any mean or standard deviation will look roughly linear. If the points had been plotted with y data on the probability scale (rather than just labeled with those values), we would not have this simple linear relationship.

请先登录，再进行评论。

Image Analyst
Image Analyst 2015-10-10
In the Statistics and Machine Learning Toolbox:
[f,xi] = ksdensity(x,pts) returns a probability density estimate, f, for the sample in the vector x, evaluated at the specified values in vector pts. Here, the xi and pts vectors contain identical values.
[pdca,gn,gl] = fitdist(x,distname,'By',groupvar) creates probability distribution objects by fitting the distribution specified by distname to the data in x based on the grouping variable groupvar. It returns a cell array of fitted probability distribution objects, pdca, a cell array of group labels, gn, and a cell array of grouping variable levels, gl.
  3 个评论
显示 1更早的评论
Walter Roberson
Walter Roberson 2015-10-11
I am unclear as to the task here. Is it to figure out which probability distribution something is? Is it to estimate the probability distribution? Is it to assume that the data is already normally distributed and to figure out the probability of each sample relative to the known distribution?
If the task is to figure out which probability distribution something is, then you need to use one of the fitting routines.
If the task is to estimate the shape of the probability distribution, then ksdensity() is appropriate.
If the task is to figure out the probability of each sample relative to the known distribution then the cdf() function I linked to above is what should be used.
Image Analyst
Image Analyst 2015-10-11
To give a probability, you'd have to give a range, like the probability you'll get a value between 0.42 and 0.43 or whatever. You can use the CDF for that like Walter says. Do you have such a range? Because the probability of an exact value is zero for continuous functions, though it is non-zero for discrete data sets like a quantized image of 256 gray levels.

请先登录，再进行评论。

类别

在 Help Center 和 File Exchange 中查找有关 Exploration and Visualization 的更多信息

标签

产品

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by