help for wtdset

Declare data to be Waiting Time Distribution data

wtdset eventvar exitvar
[if exp] [weight],
start(date)
end(date)
[id(varname)
robust
cluster(varname)
scale(#)]

wtd

fweights are allowed; see help weights.

Description

wtdset declares data to be Waiting Time Distribution
(wtd) data; see help wtd.

wtd displays how the data are currently declared.

Caution: wtdset is destructive in the sense that
it prunes and collapses data so that subsequent wtd commands can
exploit frequency weighting. This improves estimation speed
dramatically, but it is the users responsibility (exclusively!) to
ensure preservation of original data, see help save.

Options for wtdset

start(date) specifies the starting date with
syntax acceptable by the d() function, see help dfcn. If
your earliest possible observation is 1 january 1997 then the
starting date should be set to 31 december 1996. In other words
the starting date is time zero, with observation of dates only
possible for date 1, 2,...

end(date) specifies the date of the last possible
observation time.

id(varname) specifies the subject-id variable. For
each subject only the first observation in the observation period is
kept. If id is not specified it is assumed that each observation
corresponds to a single individual.

robust specifies that the Huber/White/sandwich
estimator of variance is to be used as default in place of the
traditional calculation in subsequent estimations with wtdml.
Although set it may be overruled in the wtdml statement using
option norobust, and if not set, it can be added in the
wtdml statement. robust combined with cluster()
further allows observations which are not independent within cluster
(although they must be independent between clusters). See
[U] 23.14 Obtaining robust variance estimates and help
robust.

cluster(varname) specifies that data are
clustered, ie. the observations are independent across groups
(clusters) but not necessarily independent within groups. varname
specifies to which group each observation belongs. Specifying
cluster() implies default robust variance estimation.

scale(#) defines the scale of analysis time. This can
be handy for making units of rate estimates more readable (such as
converting person-days to person-years).

Examples

. wtdset event exit, start(31dec1996) end(31dec1997) id(id)

. wtdset event exit, start(31dec1996) end(31dec1997) id(id) scale(365)

. wtd

Also see

Manual: [ST] stset

Online: help for wtd, dfcn