我注意到 Stata 系数表提到了“Robust Std. Err.”，而glmm可能没有使用稳健的错误。这将解释 SE 差异。

此外，ppml似乎实际上删除了“非显着”回归量，并且 R 的quasipoisson家族允许以不同于负二项式回归的方式过度分散，这可能不同于ppml.

我注意到您在几个地方询问了哪些 R 包会产生与ppml（经济学）重力模型等效的结果，但没有得到答案。我很遗憾看到这一点，并希望我能提供更明智的建议。看来您需要的是具有稳健标准误差的泊松回归，它可以处理零值。我不确定哪些 R 包支持它。（不确定是否ppml处理过度分散。）

贝叶斯回归包，例如rstanarm可能更稳健地处理异方差，但我不确定。我倾向于使用像student_t家庭这样的东西，但你必须使用poisson，所以我不确定那里的答案。您可以尝试负二项式系列（neg_binomial_2in rstanarm's stan_glm），它也可以处理过度分散，并且可能比quasipoisson.

另请参阅：何时在泊松回归中使用稳健标准误差？

为了扩展韦恩的出色答案，ppml使用稳健的（异方差）方差-协方差矩阵以及对该矩阵的有限样本调整以减少偏差。

sandwich()这些与R 中同名包中的计算非常相似。唯一的区别是有限样本调整的完成方式。在该sandwich(...)函数中，默认情况下根本不进行有限样本调整，即三明治除以 1/n，其中 n 是观察次数。或者，sandwich(..., adjust = TRUE)可以使用除以 1/(n - k)，其中 k 是回归器的数量。然而，Stata 除以 1/(n - 1)。

以下是通过使用调整因子为 1/(n-1) 的自定义三明治方差来使 R 与 Stata 匹配的方法：

. clear

. set more off

. capture ssc install rsource

. use http://personal.lse.ac.uk/tenreyro/mock, clear

. saveold ~/Desktop/mock, version(12) replace
(saving in Stata 12 format, which can be read by Stata 11 or 12)
file ~/Desktop/mock.dta saved

. rsource, terminator(XXX) rpath("/usr/local/bin/R") roptions("--vanilla")
Assumed R program path: "/usr/local/bin/R"

Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

Beginning of R output

R version 3.2.4 (2016-03-10) -- "Very Secure Dishes"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin13.4.0 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

>   library("foreign")
>   library("sandwich")
>   library("lmtest")
>   mock<-read.dta("~/Desktop/mock.dta")
>   glmm<-glm(formula=y ~ x + w, family=quasipoisson(link="log"),data=mock)
> 
>   sandwich1 <- function(object, ...) sandwich(object) * nobs(object) / (nobs(object) - 1)
>   coeftest(glmm,vcov=sandwich1)

z test of coefficients:

            Estimate Std. Error z value  Pr(>|z|)    
(Intercept) 0.516969   0.098062  5.2718 1.351e-07 ***
x           0.125657   0.101591  1.2369    0.2161    
w           0.013410   0.710752  0.0189    0.9849    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

> 
End of R output

. 
. ppml y x w

note: checking the existence of the estimates

Number of regressors excluded to ensure that the estimates exist: 0
Number of observations excluded: 0

note: starting ppml estimation
note: y has noninteger values

Iteration 1:   deviance =  139.7855
Iteration 2:   deviance =  137.7284
Iteration 3:   deviance =  137.7222
Iteration 4:   deviance =  137.7222

Number of parameters: 3
Number of observations: 100
Pseudo log-likelihood: -173.89764
R-squared: .01628639
Option strict is: off
------------------------------------------------------------------------------
             |               Robust
           y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           x |   .1256565   .1015913     1.24   0.216    -.0734588    .3247718
           w |   .0134101   .7107518     0.02   0.985    -1.379638    1.406458
       _cons |   .5169689   .0980624     5.27   0.000     .3247702    .7091676
------------------------------------------------------------------------------

这是生成上述输出的 Stata/R 代码。我正在使用rsource从 Stata 运行 R（您需要调整rpath()以下内容以匹配您的设置），但这并不是必需的：您可以只rsource从 R 运行该部分。

clear
set more off
capture ssc install rsource

use http://personal.lse.ac.uk/tenreyro/mock, clear
saveold ~/Desktop/mock, version(12) replace

rsource, terminator(XXX) rpath("/usr/local/bin/R") roptions("--vanilla")
  library("foreign")
  library("sandwich")
  library("lmtest")
  mock<-read.dta("~/Desktop/mock.dta")
  glmm<-glm(formula=y ~ x + w, family=quasipoisson(link="log"),data=mock)

  sandwich1 <- function(object, ...) sandwich(object) * nobs(object) / (nobs(object) - 1)
  coeftest(glmm,vcov=sandwich1)  
XXX 

ppml y x w