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ipf([Model] [, print:F or silent:T, incr:T, pvals:T, maxiter:m,\
  epsilon:eps]), vec a REAL vector, m an integer > 0, eps REAL > 0

Keywords: glm, categorical data
ipf(Model) uses iterative proportional fitting to compute a Poisson
regression (log linear) fit of the model specified in the CHARACTER
variable Model.  The default output is the deviance from the full model.

See topic 'models' for information on specifying Model.

ipf(Model,inc:T) fits the same model except a sequential analysis of
deviance is computed.  The sequential analysis of deviance has a line
for each term in the model giving the term name, degrees of freedom, and
the change of deviance obtained by including the term in the given
order.  Because each of the submodels must be fit iteratively, with a
complicated models or a large data set ipf(Model,inc:T) can take many
times longer to execute than ipf(Model).

ipf(Model [,...], pvals:T) prints chi-squared P values with each

If option 'pvals' has value True, P values will be printed unless
pvals:F is an argument.

ipf([,keywords]) or ipf(,inc [,keywords]) fits the last model used by
any of the GLM commands such as regress() or poisson().  See topic

If there are any non-factors in the model ipf() defaults to poisson().

ipf() also defaults to poisson(), if it does not identify the model as
balanced.  The only forms of balance it recognizes are complete balance
(equal number of cases in every cell) and balanced main effect models
(no interactions and all two-way marginals have equal cell sizes, for
example a Latin square design)

ipf() sets the side effect variables RESIDUALS, WTDRESIDUALS, SS, DF,
HII, DEPVNAME, TERMNAMES, and STRMODEL.  See topic 'glm'.  All except
HII should be the same as computed by poisson(Model,inc) used.  Since
HII cannot be computed easily, it is set to a constant vector with
values m/n where m = (Model degrees of freedom) and n is the number of
values in the dependent variable vector.  Thus sum(HII) = m as it
should.  Without keyword phrase 'inc:T' (see below), TERMNAMES has value
vector("","", ...,"Overall model","ERROR1"), DF has value vector(0,0,
..., ModelDF,ErrorDF) and SS has value vector(0,0,...,ModelDeviance,

ipf(Model,maxiter:m,epsilon:eps), where m is a positive integer and eps
is positive, is the same as ipf(Model) except up to m iterations may
take place (the default is 25) and eps is the convergence criterion
(default 1e-6).  You need not specify either or both.

ipf(Model [,...], print:F) is the same as ipf(Model [,...])  except that
most printing is suppressed and the only result is to set the side
effect variables.

ipf(Model [,...], silent:T) does computations, creating side effect
variables, but prints nothing except actual error messages.

Keyword phrase 'coefs:F' cannot be used with ipf().

Coefficients may be retrieved by coefs(); standard errors are not
available.  You must use poisson() if you require standard errors.

Gary Oehlert 2003-01-15