ordplot Diagnose form of discrete frequency distributions ordplot

Visualizing Categorical Data: ordplot

$Version: 1.3 (9 Nov 2000)
Michael Friendly
York University

The ordplot macro ( [download] get ordplot.sas)

Diagnose form of discrete frequency distributions

The ORDPLOT macro constructs a plot whose slope and intercept can diagnose the form of a discrete frequency distribution. This is a plot of k n(k) / n(k-1) against k, where k is the basic count and n(k) is the frequency of occurrence of k. The macro displays both a weighted and unweighted least squares line and uses the slope and intercept of the weighted line to determine the form of the distribution.

Method

Usage

The ORDPLOT macro is called with keyword parameters. The COUNT= and FREQ= variables are required. The arguments may be listed within parentheses in any order, separated by commas. For example: 
  %ordplot(data=horskick, count=Deaths, freq=corpsyrs);

Parameters

DATA=
Name of the input data set [Default: DATA=_LAST_]
COUNT=
The name of the basic count variable
FREQ=
The name of the variable giving the number of occurrences of COUNT
LABEL=
Label for the horizontal (COUNT=) variable. If not specified the variable label for the COUNT= variable in the input data set is used.
OUT=
The name of the output data set [Default: OUT=ORDPLOT]
NAME=
Name of the graphics catalog entry [Default: NAME=ORDPLOT]

Example

%include vcd(ordplot);        *-- or include in an autocall library;

  data horskick;
     input deaths corpsyrs;
     label deaths='Number of Deaths'
        corpsyrs='Number of Corps-Years';
  cards;
        0    109
        1     65
        2     22
        3      3
        4      1
  ;
  %ordplot(data=horskick, count=Deaths, freq=corpsyrs);
This produces the plot below. The near-zero slope of the (red) weighted least-squares line diagnoses this as a Poisson distribution.

See also

distplot Plots for discrete distributions
goodfit Goodness of fit tests for discrete distributions
poisplot Poissonness plot for discrete distributions
rootgram Hanging rootograms for discrete distributions