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help for betafit postestimation
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Title

betafit postestimation -- Postestimation tools for betafit

Description

This file documents postestimation tools for betafit.

dbetafit displays discrete changes and marginal effects after betafit.

The following standard postestimation commands are also available:

command         description
-------------------------------------------------------------------------
INCLUDE help post_estat
INCLUDE help post_estimates
INCLUDE help post_lincom
INCLUDE help post_lrtest
INCLUDE help post_mfx
margins       marginal means, predictive margins, marginal effects, and
average marginal effects
INCLUDE help post_nlcom
predict       predictions and residuals
INCLUDE help post_predictnl
INCLUDE help post_suest
INCLUDE help post_test
INCLUDE help post_testnl
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help for dbetafit
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Syntax for dbetafit

dbetafit [, at(variables_and_values) ]

Description

dbetafit displays changes in predicted dependent variable for three types
of discrete changes in explanatory variables and two types of marginal
effects:

Discrete changes

Min --> Max shows the change in predicted dependent variable when an
explanatory variable changes from its minimum value to its maximum
value, while keeping all other variables at their specified values
(by default their means). This is the only effect shown for indicator
or dummy variables (variables with only two distinct values).

+-SD/2 shows the change in predicted dependent variable when an
explanatory variable moves from half a standard deviation below its
specified value (by default the mean) to half a standard deviation
above its specified value, while keeping all other variables at their
specified values. In other words, it shows the effect of a standard
deviation change in an explanatory variable, centred on the specified
value, on the predicted dependent variable.

+-1/2 shows the change in predicted dependent variable when an
explanatory variable moves from half a unit below its specified value
to half a unit above its specified value, while keeping all other
variables at their specified values. In other words, it shows the
effect of a unit change in explanatory variable, centred on the
specified value, on the predicted dependent variable.

Marginal effects

MFX at x shows the marginal effect of each (non-indicator or
non-dummy) variable, while keeping all variables at their specified
values. The marginal effect is the change in predicted dependent
variable for a unit change in the explanatory variable, assuming that
the effect does not change over that interval.

Max MFX is the maximum marginal effect. Marginal effects change
depending on which values of the explanatory variables they are
evaluated for. The maximum marginal effect shows the marginal effect
you would get if you had chosen those values of the explanatory
variables that would maximize the marginal effect. The marginal
effect is maximum if the predicted proportion is .5; hence this may
lie outside the range of the observed data.

dbetafit is only allowed after betafit in the alternative
parameterization, i.e. if one or both muvar() and phivar() is specified
or if the alternative option is specified. mfx can be used to get
marginal effects after betafit in both the conventional and the
alternative parametrization.

Options

at(variables_and_values) is used to specify at which values of the
explanatory variables the effects are calculated.
variables_and_values is an alternating list of variables and either
numeric values or mean, median, min, max, p1, p5, p10, p25, p50, p75,
p90, p95, p99. The default is mean for all variables. The statistics
p1, p5, ..., p99, are the 1st, 5th, ..., 99th percentiles.

Examples

use http://fmwww.bc.edu/repec/bocode/c/citybudget.dta, clear

betafit governing, mu(minorityleft noleft houseval popdens)

dbetafit, at(minorityleft 0 noleft 0)

(click to run)

Author of dbetafit

Maarten L. Buis,
Universitaet Tuebingen
maarten.buis@uni-tuebingen.de

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help for predict
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Syntax for predict

predict [type] newvar [if] [in] [, statistic var(varname) ]

statistic       Description
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proportion    proportion (the default)
xb            xb, fitted values
stdp          standard error of the prediction
sd            standard deviation
alpha         alpha in the conventional parameterization
beta          beta in the conventional parameterization
mu            mu in the alternative parameterization, equivalent to
proportion
phi           phi in the alternative parameterization
* pearson       Pearson residuals
* working       working residuals
* partial       partial residuals
* scresidual    score residuals
score         first derivative of the log likelihood with respect to
the linear predictor.
-------------------------------------------------------------------------
INCLUDE help unstarred

Options for predict

proportion (the default) calculates the proportions.

xb calculates the linear prediction.

stdp calculates the standard error of the linear prediction.

sd calculates the estimated standard deviation of the depedent variable.

alpha calculates alpha in the conventional parameterization. This can be
specified after estimating a model using betafit, regardless of the
parameterization used.

beta calculates beta in the conventional parameterization. This can be
specified after estimating a model using betafit, regardless of the
parameterization used.

phi calculates phi in the alternative parameterization. This can be
specified after estimating a model using betafit, regardless of the
parameterization used.

pearson calculates the Pearson residuals, the raw residuals scaled by the
estimated standard deviation of the predicted proportion.

working calculates the working residuals as discussed on page 53 of
Hardin and Hilbe (2007).

partial calculates the partial residuals as discussed on page 54 of
Hardin and Hilbe (2007). This requires that one of the explanatory
variables is specified in the var() option.

scresidual calculates the score residuals (see Rocha and Simas
(forthcoming)) or "standardized weighted residuals 1" (see Espinheira
et al. (2008)).  These residuals tend to be more symmetric and
normal/Gaussian-like, making them easier to interpret than Pearson
residuals.

score calculates the first derivative of the log likelihood with respect
to the linear predictions.

References

Cox, Nicholas J. 2005.  Speaking Stata: The protean quantile plot.  The
Stata Journal 5(3): 442-460.
http://www.stata-journal.com/article.html?article=gr0018

Espinheira, Patrícia L., Ferrari, Silvia L.P., and Cribari-Neto,
Francisco. 2008.  On beta regression residuals. Journal of Applied
Statistics 35(4): 407-419.

Hardin, James W. and Hilbe, Joseph M. 2007. Generalized Linear Models and
Extensions (2nd edition).  College Station, TX: Stata Press.

Rocha, Andréa V. and Simas, Alexandre B. (forthcoming).  Influence
diagnostics in a general class of beta regression models.
Mathematics and Statistics.

Winter, Nicholas J.G. 2005. Stata tip 23: Regaining control over axis
ranges.  The Stata Journal 5(3): 467-468.
http://www.stata-journal.com/article.html?article=gr0019

Also see

Online: help for betafit, estimates, lincom, lrtest, mfx, nlcom, predict,
predictnl, suest, test, testnl

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