| Name : R-eikosograms
| |
| Version : 0.1.1
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp153.3.7
| Date : 2024-06-14 11:26:42
|
| Group : Development/Libraries/Other
| Source RPM : R-eikosograms-0.1.1-lp153.3.7.src.rpm
|
| Size : 4.35 MB
| |
| Packager : (none)
| |
| Summary : The Picture of Probability
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Description :
An eikosogram (ancient Greek for probability picture) divides the unit
square into rectangular regions whose areas, sides, and widths,
represent various probabilities associated with the values of one or
more categorical variates. Rectangle areas are joint probabilities,
widths are always marginal (though possibly joint margins, i.e.
marginal joint distributions of two or more variates), and heights of
rectangles are always conditional probabilities. Eikosograms embed the
rules of probability and are useful for introducing elementary
probability theory, including axioms, marginal, conditional, and joint
probabilities, and their relationships (including Bayes theorem as a
completely trivial consequence). They are markedly superior to Venn
diagrams for this purpose, especially in distinguishing probabilistic
independence, mutually exclusive events, coincident events, and
associations. They also are useful for identifying and understanding
conditional independence structure. As data analysis tools, eikosograms
display categorical data in a manner similar to Mosaic plots,
especially when only two variates are involved (the only case in which
they are essentially identical, though eikosograms purposely disallow
spacing between rectangles). Unlike Mosaic plots, eikosograms do not
alternate axes as each new categorical variate (beyond two) is
introduced. Instead, only one categorical variate, designated the
\"response\", presents on the vertical axis and all others, designated
the \"conditioning\" variates, appear on the horizontal. In this way,
conditional probability appears only as height and marginal
probabilities as widths. The eikosogram is therefore much better suited
to a response model analysis (e.g. logistic model) than is a Mosaic
plot. Mosaic plots are better suited to log-linear style modelling as
in discrete multivariate analysis. Of course, eikosograms are also
suited to discrete multivariate analysis with each variate in turn
appearing as the response. This makes it better suited than Mosaic
plots to discrete graphical models based on conditional independence
graphs (i.e. \"Bayesian Networks\" or \"BayesNets\"). The eikosogram and
its superiority to Venn diagrams in teaching probability is described
in W.H. Cherry and R.W. Oldford (2003)
< https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/paper.pdf>, its
value in exploring conditional independence structure and relation to
graphical and log-linear models is described in R.W. Oldford (2003)
< https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/independence/paper.pdf>,
and a number of problems, puzzles, and paradoxes that are easily
explained with eikosograms are given in R.W. Oldford (2003)
< https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/examples/paper.pdf>.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
