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help for ^binreg^                                       (STB-50: sbe29)
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Binomial regression models
--------------------------

	^binreg^ depvar [varlist] [weight] [^if^ exp] [^in^ range] [^,^ 
		^nocons^tant ^s^cale^(x2^|^dev^|#^)^ [^ln^]^o^ffset^(^varname^)^
		^disp(^#^)^ ^coeff^ [^or^|^rr^|^hr^|^rd^] ^le^vel^(^#^)^ ^it^erate^(^#^)^ ^lt^ol^(^#^)^ 
                ^ini^t^(^varname^)^ ^n(^varname|#^)^ ^nolo^g^(^#^)^ ]


^aweight^s, ^fweight^s, and ^iweight^s are allowed; see help @weights@.

^binreg^ shares the features of all estimation commands; see help @est@.


The syntax of @predict@ after ^binreg^ is

	^predict^ [type] newvarname [^if^ exp] [^in^ range] [^,^ statistic] [^nooff^set]

where statistic is

	^m^u            predicted mean of y = g_inverse(xb); the default
	^xb^            linear prediction
	^stdp^          standard error of the linear prediction
	^d^eviance      deviance residual
	^p^earson       Pearson residual

These statistics are available both in and out of sample; type "^predict^ ...
^if e(sample)^ ..." if wanted only for the estimation sample.


Description
-----------

^binreg^ fits generalized linear models for the binomial family.  The available
links are 

    Option      Implied link        Coefficients
    ------      --------------      --------------------------
    ^or^          logit               Odds ratios      
    ^rr^          log                 Risk ratios
    ^hr^          log complement      Health ratios
    ^rd^          identity            Risk differences


(*) eform is not valid with the identity link.


Options
-------

^noconstant^ specifies that the linear predictor has no intercept term, thus
    forcing it through the origin on the scale defined by the link function.

^scale(x2^|^dev^|#^)^ overrides the default scale parameter.  By default, ^scale(1)^ is
    assumed for discrete distributions (binomial, Poisson, negative binomial)
    and ^scale(x2)^ for continuous distributions (Gaussian, gamma, inverse
    Gaussian).

    ^scale(x2)^ specifies the scale parameter be set to the Pearson chi-squared
    (or generalized chi-squared) statistic divided by the residual degrees of
    freedom.

    ^scale(dev)^ sets the scale parameter to the deviance divided by the residual
    degrees of freedom.  This provides an alternative to ^scale(x2)^ for con-
    tinuous distributions and over- or under-dispersed discrete distributions.

    ^scale(^#^)^ sets the scale parameter to #.

[^ln^]^offset(^varname^)^ specifies an offset to be added to the linear predictor.
    ^offset()^ specifies the values directly:     g(E(y)) = xB + varname.
    ^lnoffset()^ specifies exponentiated values:  g(E(y)) = xB + ln(varname).

^disp(^#^)^ multiplies the variance of y by # and divides the deviance by #.  The
    resulting distributions are members of the quasi-likelihood family.

^coeff^ displays the non-exponentiated coefficients and corresponding standard
    errors and confidence intervals.  This has no effect on the RD option as
    it is always presenting the non-exponentiated coefficients.  

^level(^#^)^ specifies the confidence level, in percent, for confidence intervals
    of the coefficients; see help @level@.

^iterate(^#^)^ specifies the maximum number of iterations allowed in estimating the
    model; ^iterate(50)^ is the default.

^ltol(^#^)^ specifies the convergence criterion for the change in deviance between
    iterations; ^ltol(1e-6)^ is the default.

^init(^varname^)^ specifies varname containing an initial estimate for the mean of
    depvar.  This can be useful if you encounter convergence difficulties,
    especially with binomial models with power or odds-power links.

^n(^varname|#^)^ specifies either a constant integer to use as the denominator
    for the binomial family, or a variable which holds the denominator for 
    each observation.  This is useful for grouped data (see @glogit@ for a 
    complete description).

^nolog^ suppresses the iteration log.

^or^ requests the logit link and results in odds ratios if ^coeff^ is not 
    specified.

^rr^ requests the log link and results in risk ratios if ^coeff^ is not 
    specified.

^hr^ requests the log complement link and results in health ratios if ^coeff^
    is not specified.

^rd^ requests the identity link and results in risk differences.



Options for @predict@
-------------------

^mu^, the default, requests the predicted value of y; y_hat = g_inverse(xb).

^xb^ requests the linear predictor xb.

^stdp^ requests the standard error of the linear predictor.

^deviance^ requests the deviance residuals.

^pearson^ requests Pearson residuals.

^nooffset^ is relevant only if you specified ^offset()^ or ^lnoffset()^ for ^glm^.  It
    modifies the calculations made by ^predict^ so that they ignore the offset
    variable; the linear prediction is treated as x_j*b rather than x_j*b +
    offset_j.


Remarks
-------

 . ^binreg low age lwt race2 race3 smoke ptl ht ui, or^

 . ^binreg dead ln_dose, rr coeff n(n)^


Authors
-------

      James Hardin
      jhardin@@stat.tamu.edu

      Mario Cleves
      mcleves@@stata.com


Also see
--------

    STB:  STB-50 sbe29
 Manual:  [U] 23 Estimation and post-estimation commands,
          [U] 29 Overview of model estimation in Stata,
          [R] glm
On-line:  help for @est@, @postest@; @glm@, @logistic@, @logit@