{smcl}
{* 6may2004}{...}
{hline}
help for {hi:circsummarize}
{hline}
{title:Summary statistics for circular data}
{p 8 17 2}
{cmdab:circsu:mmarize}
{it:varlist}
[{it:weight}]
[{cmd:if} {it:exp}]
[{cmd:in} {it:range}]
[{cmd:,}
{cmd:ci}
{cmdab:l:evel(}{it:#}{cmd:)}
{cmdab:ray:leigh}
{cmdab:kui:per}
{cmdab:d:etail}
]
{p 8 17 2}
{cmdab:circsu:mmarize}
{it:varname}
[{it:weight}]
[{cmd:if} {it:exp}]
[{cmd:in} {it:range}]
[{cmd:,}
{cmd:by(}{it:byvar}{cmd:)}
{cmd:ci}
{cmdab:l:evel(}{it:#}{cmd:)}
{cmdab:ray:leigh}
{cmdab:kui:per}
{cmdab:d:etail}
]
{p 4 4 2} {cmd:fweight}s and {cmd:aweight}s are allowed; see help
{help weights}. If weights are specified, they are used in calculating the mean
direction and the vector strength (and hence the Rayleigh test and confidence
limits if produced, although this may not be justifiable). Weights are not used
in calculating the circular range or the Kuiper test.
{p 4 4 2}The abbreviation {cmd:circsu} (only) is allowed.
{title:Description}
{p 4 4 2}{cmd:circsummarize} produces summary statistics for circular
variables with scales between 0 and 360 degrees. These are by default
{p 8 8 2}the mean direction (in degrees),
{p 8 8 2}the vector strength or mean resultant length (i.e.
length of resultant / number of observations)
{p 8 8 2}and the circular range (length of smallest arc including all
observations) (in degrees).
{p 4 4 2}
Suppose we have {it:n} measurements of a circular variable {it:t}.
Then calculate in turn{p_end}
{p 8 8 2}{it:C} = SUM cos {it:t}{p_end}
{p 8 8 2}{it:S} = SUM sin {it:t}{p_end}
{p 8 8 2}arctan({it:S} / {it:C}), the mean direction{p_end}
{p 8 8 2}{it:R} = sqrt({it:C} * {it:C} + {it:S} * {it:S}),
the length of the resultant vector{p_end}
{p 8 8 2}{it:R} / {it:n}, the vector strength or mean resultant length.
{title:Options}
{p 4 8 2}
{cmd:by()} specifies that results are to be shown for each group
defined by values of {it:byvar}. This option is only available if a
single {it:varname} is specified.
{p 4 8 2}
{cmd:ci} produces a confidence interval for the vector mean suitable for
large samples (at least 25 values, say). This is based on an
assumption that sampling variation follows a normal distribution.
If we calculate further
{p 12 12 2}{it:m}_2 = (1 / {it:n}) SUM cos 2({it:t} - vector mean of {it:t}),
{p 8 8 2}then the circular standard error CSE is estimated by
{p 12 12 2}sqrt[ {it:n} * (1 - {it:m}_2) / (2 * {it:R} * {it:R})
{p 8 8 2}and the confidence interval is estimated as
{p 12 12 2}(vector mean - arcsin({it:z} * CSE),
vector mean + arcsin({it:z} * CSE)),
{p 8 8 2}where {it:z} is the appropriate percentage point,
given {cmd:level()}, from the Normal distribution with mean 0 and
standard deviation 1.
{p 4 8 2}{cmd:level()} specifies the confidence level in percent for the
confidence interval. See help for {help ci}.
{p 4 8 2}{cmd:rayleigh} produces the Rayleigh test, which tests a null
hypothesis of uniformity against an alternative hypothesis of unimodality.
The resulting {it:P}-value is shown.
{p 4 8 2}{cmd:kuiper} produces the Kuiper test, which tests a null hypothesis
of uniformity against any alternative. The resulting {it:P}-value is shown.
A numerical approximation due to Stephens (1970, p.118) is used that
gives accuracy to 3 decimal places for {it:P} < 0.447.
{p 4 8 2}{cmd:detail} is a synonym for {cmd:ci rayleigh kuiper}.
{title:Examples}
{p 4 8 2}{cmd:. circsummarize axisasp wallasp}
{p 4 8 2}{cmd:. circsummarize wallasp, by(grade) detail}
{title:References}
{p 4 8 2}
Fisher, N.I. 1993. {it:Statistical analysis of circular data.} Cambridge:
Cambridge University Press.
{p 4 8 2}
Stephens, M.A. 1970. Use of the Kolmogorov-Smirnov, Cram{c e'}r-von Mises and
related statistics without extensive tables.
{it:Journal, Royal Statistical Society Series B} 32: 115-22.
{title:Author}
{p 4 4 2}Nicholas J. Cox, University of Durham, U.K.{break}
n.j.cox@durham.ac.uk
{title:Also see}
{p 4 13 2}
On-line: help for {help ci}