{smcl}
{* 19june2002/3june2004}{...}
{hline}
help for {hi:dpplot7}
{hline}
{title:Density probability plots}
{p 8 16}{cmd:dpplot7}
{it:varname}
[{cmd:if} {it:exp}]
[{cmd:in} {it:range}]
[ {cmd:,}
{cmd:a(}{it:#}{cmd:)}
{cmd:dist(}{it:name}{cmd:)}
{cmd:param(}{it:numlist}{cmd:)}
{it:graph_options}
]
{title:Description}
{p}{cmd:dpplot7} plots density probability plots for {it:varname} given a
reference distribution, by default normal (Gaussian).
{p}Note: {cmd:dpplot7} is the original version, written for Stata 7,
of {cmd:dpplot}. Users of Stata 8 or later should switch to {cmd:dpplot}.
{title:Remarks}
{p}
To establish notation, and to fix ideas with a concrete example: consider an
observed variable Y, whose distribution we wish to compare with a normally
distributed variable X. That variable has density function f(X), distribution
function P = F(X) and quantile function X = Q(P). (The distribution function
and the quantile function are inverses of each other.) Clearly, this notation
is fairly general and also covers other distributions, at least for continuous
variables.
{p}
The particular density function f(X | parameters) most pertinent to
comparison with data for Y can be computed given values for its
parameters, either estimates from data on Y, or parameter values chosen
for some other good reason. In the case of a normal distribution, these
parameters would usually be the mean and the standard deviation. Such density
functions are often superimposed on histograms or other graphical displays.
In Stata, {cmd:graph, histogram} has a {cmd:normal} option which adds
the normal density curve corresponding to the mean and standard deviation of the
data shown.
{p}
The density function can also be computed indirectly via the quantile function
as f(Q(P)). For example, if P were 0.5, then f(Q(0.5)) would be
the density at the median. In practice P is calculated as
so-called plotting positions p_i attached to values y_(i) of a sample of Y
of size n which have rank i: that is, the y_(i) are the order statistics
y_(1) <= ... <= y_(n). One simple rule uses p_i = (i - 0.5) / n.
Most other rules follow one of a family (i - a) / (n - 2a + 1) indexed by a.
{p}
Plotting both f(X | parameters) and f(Q(P = p_i)), calculated using plotting
positions, versus observed Y gives two curves. In our example, the first is
normal by construction and the second would be a good estimate of a normal
density if Y were truly normal with the same parameters. In terms of Stata
functions, the two curves are based on {cmd:normden(}(X - mean) / SD){cmd:)}
and {cmd:normden(invnorm(}p_i{cmd:))}. The match or mismatch between the
curves allows graphical assessment of goodness or badness of fit. What is more,
we can use experience from comparing frequency distributions, as shown on
histograms, dot plots or other similar displays, in comparing or identifying
location and scale differences, skewness, tail weight, tied values, gaps,
outliers and so forth.
{p}Such {it:density probability plots} were suggested by Jones and Daly (1995).
They are best seen as special-purpose plots, like normal quantile plots
and their kin, rather than general-purpose plots, like histograms or dot plots.
{p}Extending the discussion in Jones and Daly (1995), the advantages (+) and
limitations (-) of these plots include
{p 4 4}+1. No choices of binning or origin (cf. histograms, dot plots, etc.)
or of kernel or of degree of smoothing (cf. density estimation) are required.
{p 4 4}+2. Some people find them easier to interpret than quantile-quantile
plots.
{p 4 4}+3. They work well for a wide range of sample sizes. At the same
time, as with any other method, a sample of at least moderate size is
preferable (one rule of thumb is >= 25).
{p 4 4}+4. If X has bounded support in one or both directions, then this
should be clear on the plot.
{p 4 4}-1. Results may be difficult to decipher if observed and reference
distributions differ in modality. For example, if the reference distribution is
unimodal but the observed data hint at bimodality, nevertheless f(Q(P)) must be
unimodal even though f(Y) may not be. Similarly, when the reference
distribution is exponential, then f(Q(P)) must be monotone decreasing whatever
the shape of f(Y).
{p 4 4}-2. It may be difficult to discern subtle differences in one or both
tails of the observed and reference distributions.
{p 4 4}-3. Comparison is of a curve with a curve: some people argue
that graphical references should where possible be linear (and ideally
horizontal). (A linear reference is a clear advantage of quantile plots.)
{p 4 4}-4. There is no simple extension to comparison of two samples
with each other.
{p}
Programmers may wish to inspect the code and add code for other distributions.
If parameters are not estimated, then naturally their values must be
supplied: the order of parameters should seem natural or at least conventional.
{title:Options}
{p 0 4}{it:graph_options} are options of {cmd:graph, twoway}.
The defaults include
{cmd:gap(4) symbol(oi) connect(.s) l1title("Probability density") xla yla}.
{p 0 4}{cmd:a()} specifies a family of plotting positions,
as explained above. The default is 0.5. Choice of {cmd:a} is rarely material
unless the sample size is very small, and then the exercise is moot whatever
is done.
{p 0 4}{cmd:dist()} specifies a distribution to act as a reference. In this
preliminary version, the distributions allowed are {cmd:exponential} and
{cmd:normal}, the latter being the default. {cmd:Gaussian} is a synonym
for {cmd:normal}. Abbreviations down to at least three letters (e.g. {cmd:exp},
{cmd:nor}) are allowed.
{p 0 4}{cmd:param()} specifies parameter values which give reference
distributions; specifications override parameters estimated from the data.
{p 4 4}With {cmd:dist(normal)} up to two parameters may be specified. The first
is the mean and the second is the standard deviation.
{p 4 4}With {cmd:dist(exponential)} one parameter may be specified, namely the
mean.
{title:Examples}
{p 4 8}{inp:. dpplot7 mpg}
{p 4 8}{inp:. set obs 1000}{p_end}
{p 4 8}{inp:. gen rnd = invnorm(uniform())}{p_end}
{p 4 8}{inp:. dpplot7 rnd, param(0 1)}
{title:Author}
Nicholas J. Cox, University of Durham, U.K.
n.j.cox@durham.ac.uk
{title:References}
{p}Jones, M.C. and F. Daly. 1995. Density probability plots.
{it:Communications in Statistics, Simulation and Computation}
24: 911-927.
{title:Also see}
{p 0 19}On-line: help for {help graph}, {help diagplots}
{p_end}