Stata 15 help for tpoisson_postestimation

[R] tpoisson postestimation -- Postestimation tools for tpoisson

Postestimation commands

The following postestimation commands are available after tpoisson:

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 * forecast dynamic forecasts and simulations * hausman Hausman's specification test lincom point estimates, standard errors, testing, and inference for linear combinations of coefficients * 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 ------------------------------------------------------------------------- * forecast, hausman, and lrtest are not appropriate with svy estimation results.

Syntax for predict

predict [type] newvar [if] [in] [, statistic nooffset]

statistic Description ------------------------------------------------------------------------- Main n number of events; the default ir incidence rate cm conditional mean, E(y | ll < y < ul) pr(n) probability Pr(y = n) pr(a,b) probability Pr(a < y < b) cpr(n) conditional probability Pr(y = n | ll < y < ul) cpr(a,b) conditional probability Pr(a < y < b | ll < y < ul) xb linear prediction stdp standard error of the linear prediction score first derivative of the log likelihood with respect to xb ------------------------------------------------------------------------- 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

Description for predict

predict creates a new variable containing predictions such as numbers of events, incidence rates, conditional means, probabilities, conditional probabilities, linear predictions, standard errors, and equation-level scores.

Options for predict

+------+ ----+ Main +-------------------------------------------------------------

n, the default, calculates the predicted number of events, which is exp(xb) if neither offset() nor exposure() was specified when the model was fit; exp(xb + offset) if offset() was specified; or exp(xb) x exposure if exposure() was specified.

ir calculates the incidence rate exp(xb), which is the predicted number of events when exposure is 1. This is equivalent to specifying both the n and the nooffset options.

cm calculates the conditional mean, E(y|ll < y < ul), where ll is the left-truncation point specified at estimation and ul is the right-truncation point specified at estimation.

pr(n) calculates the probability Pr(y = n), where n is a nonnegative integer that may be specified as a number or a variable.

pr(a,b) calculates the probability Pr(a < y < b), where a and b are nonnegative integers that may be specified as numbers or variables;

b missing (b > .) means plus infinity; pr(20,.) calculates Pr(y > 20); pr(20,b) calculates Pr(y > 20) in observations for which b > . and calculates Pr(20 < y < b) elsewhere.

pr(.,b) produces a syntax error. A missing value in an observation of the variable a causes a missing value in that observation for pr(a,b).

cpr(n) calculates the conditional probability Pr(y = n|ll < y < ul), where n is a nonnegative integer that may be specified as a number or a variable. ll and ul are as defined in cm.

cpr(a,b) calculates the conditional probability Pr(a < y < b | ll < y < ul), where a and b are as defined in {opt pr(a,b)} with the additional restrictions that a > ll and b < ul. ll and ul are as defined in cm.

xb calculates the linear prediction, which is xb if neither offset() nor exposure() was specified when the model was fit; xb + offset if offset() was specified; or xb + ln(exposure) if exposure() was specified; see nooffset below.

stdp calculates the standard error of the linear prediction.

score calculates the equation-level score, the derivative of the log likelihood with respect to the linear prediction.

nooffset is relevant only if you specified offset() or exposure() when you fit the model. It modifies the calculations made by predict so that they ignore the offset or exposure variable; the linear prediction is treated as xb rather than as xb + offset or xb + ln(exposure). Specifying predict ..., nooffset is equivalent to specifying predict ..., ir.

Syntax for margins

margins [marginlist] [, options]

margins [marginlist] , predict(statistic ...) [predict(statistic ...) ...] [options]

statistic Description ------------------------------------------------------------------------- n number of events; the default ir incidence rate cm conditional mean, E(y | ll < y < ul) pr(n) probability Pr(y = n) pr(a,b) probability Pr(a < y < b) cpr(n) conditional probability Pr(y = n | ll < y < ul) cpr(a,b) conditional probability Pr(a < y < b | ll < y < ul) xb linear prediction stdp not allowed with margins score not allowed with margins -------------------------------------------------------------------------

Statistics not allowed with margins are functions of stochastic quantities other than e(b).

For the full syntax, see [R] margins.

Menu for margins

Statistics > Postestimation

Description for margins

margins estimates margins of response for numbers of events, incidence rates, conditional means, probabilities, conditional probabilities, and linear predictions.

Examples

Setup . webuse runshoes . replace shoes = . if shoes<4

Fit truncated Poisson regression . tpoisson shoes distance male age, ll(3)

Predict the number of pairs of shoes purchased . predict shoehat, n

Predict the number of shoes purchased, conditional on each person having bought more than 3 pairs of shoes . predict shoecondhat, cm

Predict the probability each person has 1-3 pairs of shoes . predict p, pr(1,3)

Predict the probability each person has 6 or more pairs of shoes given that they have more than 3 pairs of shoes . predict p2, cpr(6,.)


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