donwayho 发表于 2017-4-15 21:29 ![](https://bbs-cdn.datacourse.cn/static/image/common/back.gif)
请问用你这种方式得到的残差和用predict de, de得到的deviance residual有什么区别呢?
predict de, de
oprobit没有上面这个命令
Title
[R] oprobit postestimation -- Postestimation tools for oprobit
Description
The following postestimation commands are available after oprobit:
Command Description
----------------------------------------------------------------------------
contrast contrasts and ANOVA-style joint tests of estimates
estat ic Akaike's and Schwarz's Bayesian information criteria
(AIC and BIC)
estat summarize summary statistics for the estimation sample
estat vce variance-covariance matrix of the estimators (VCE)
estat (svy) postestimation statistics for survey data
estimates cataloging estimation results
(1) forecast dynamic forecasts and simulations
lincom point estimates, standard errors, testing, and
inference for linear combinations of coefficients
linktest link test for model specification
(2) lrtest likelihood-ratio test
margins marginal means, predictive margins, marginal effects,
and average marginal effects
marginsplot graph the results from margins (profile plots,
interaction plots, etc.)
nlcom point estimates, standard errors, testing, and
inference for nonlinear combinations of coefficients
predict predictions, residuals, influence statistics, and other
diagnostic measures
predictnl point estimates, standard errors, testing, and
inference for generalized predictions
pwcompare pairwise comparisons of estimates
suest seemingly unrelated estimation
test Wald tests of simple and composite linear hypotheses
testnl Wald tests of nonlinear hypotheses
----------------------------------------------------------------------------
(1) forecast is not appropriate with mi or svy estimation results.
(2) lrtest is not appropriate with svy estimation results.
Syntax for predict
predict [type] {stub* | newvar | newvarlist} [if] [in] [, statistic
outcome(outcome) nooffset]
predict [type] {stub* | newvarlist} [if] [in] , scores
statistic Description
----------------------------------------------------------------------------
Main
pr predicted probabilities; the default
xb linear prediction
stdp standard error of the linear prediction
----------------------------------------------------------------------------
If you do not specify outcome(), pr (with one new variable specified)
assumes outcome(#1).
You specify one or k new variables with pr, where k is the number of
outcomes.
You specify one new variable with xb and stdp.
These statistics are available both in and out of sample; type predict ...
if e(sample) ... if wanted only for the estimation sample.
Menu for predict
Statistics > Postestimation > Predictions, residuals, etc.
Options for predict
+------+
----+ Main +----------------------------------------------------------------
pr, the default, calculates the predicted probabilities. If you do not also
specify the outcome() option, you specify k new variables, where k is
the number of categories of the dependent variable. Say that you fit a
model by typing oprobit result x1 x2, and result takes on three values.
Then you could type predict p1 p2 p3 to obtain all three predicted
probabilities. If you specify the outcome() option, you must specify
one new variable. Say that result takes on the values 1, 2, and 3.
Typing predict p1, outcome(1) would produce the same p1.
xb calculates the linear prediction. You specify one new variable, for
example, predict linear, xb. The linear prediction is defined, ignoring
the contribution of the estimated cutpoints.
stdp calculates the standard error of the linear prediction. You specify
one new variable, for example, predict se, stdp.
outcome(outcome) specifies for which outcome the predicted probabilities are
to be calculated. outcome() should contain either one value of the
dependent variable or one of #1, #2, ..., with #1 meaning the first
category of the dependent variable, #2 meaning the second category, etc.
nooffset is relevant only if you specified offset(varname) for oprobit. It
modifies the calculations made by predict so that they ignore the offset
variable; the linear prediction is treated as xb rather than as xb +
offset.
scores calculates equation-level score variables. The number of score
variables created will equal the number of outcomes in the model. If
the number of outcomes in the model was k, then
The first new variable will contain the derivative of the log likelihood
with respect to the regression equation.
The other new variables will contain the derivative of the log
likelihood with respect to the cutpoints.