feature request: supporting fixtest objects

[IndrajeetPatil]

library(fixest)

gravity_results <- feglm(Euros ~ log(dist_km) | Origin + Destination + Product + Year, trade)

gravity_results_ols <- feols(log(Euros) ~ log(dist_km) | Origin + Destination + Product + Year, trade)

gravity_results_negbin <- fenegbin(Euros ~ log(dist_km) | Origin + Destination + Product + Year, trade)

class(gravity_results)
#> [1] "fixest"

class(gravity_results_ols)
#> [1] "fixest"

class(gravity_results_negbin)
#> [1] "fixest"

summary(gravity_results)
#> GLM estimation, family = poisson, Dep. Var.: Euros
#> Observations: 38,325 
#> Fixed-effects: Origin: 15,  Destination: 15,  Product: 20,  Year: 10
#> Standard-errors: Clustered (Origin) 
#>              Estimate Std. Error z value  Pr(>|z|)    
#> log(dist_km)  -1.5279   0.115699 -13.206 < 2.2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> Log-likelihood: -7.025e+11   Adj. Pseudo-R2: 0.76403 
#>            BIC:  1.405e+12     Squared Cor.: 0.61202

summary(gravity_results_ols)
#> OLS estimation, Dep. Var.: log(Euros)
#> Observations: 38,325 
#> Fixed-effects: Origin: 15,  Destination: 15,  Product: 20,  Year: 10
#> Standard-errors: Clustered (Origin) 
#>              Estimate Std. Error z value  Pr(>|z|)    
#> log(dist_km)  -2.1699   0.154312 -14.062 < 2.2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> Log-likelihood: -75,682.54   Adj. R2: 0.70514 
#>                            R2-Within: 0.21932

summary(gravity_results_negbin)
#> Negative Binomial ML estimation, Dep. Var.: Euros
#> Observations: 38,325 
#> Fixed-effects: Origin: 15,  Destination: 15,  Product: 20,  Year: 10
#> Standard-errors: Clustered (Origin) 
#>              Estimate Std. Error z value  Pr(>|z|)    
#> log(dist_km)  -1.7108   0.166378 -10.283 < 2.2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> Over-dispersion parameter: theta = 0.5487735  
#> Log-likelihood:  -646,586.98   Adj. Pseudo-R2: 0.03464 
#>            BIC: 1,294,419.32     Squared Cor.: 0.4376

^{Created on 2019-11-19 by the reprex package (v0.3.0)}

Session info

devtools::session_info()
#> - Session info ---------------------------------------------------------------
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#>  collate  English_United States.1252  
#>  ctype    English_United States.1252  
#>  tz       Europe/Berlin               
#>  date     2019-11-19                  
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[karldw]

@IndrajeetPatil, thanks for requesting this!
For others who see this issue, the fixest author prefers that these methods are in broom, rather than fixest. (lrberge/fixest#2)

[IndrajeetPatil]

@karldw Please feel free to make a PR here if you wish. This will be a good addition to the current collection of broom tidiers.

[grantmcdermott]

@IndrajeetPatil @karldw

Just to let you know I've started working on this here: https://github.com/grantmcdermott/broom/tree/fixest-tidiers

I've added a tidy.fixest method, but still need to do the glance and augmentequivalents, say nothing of adding tests, etc. So if anyone feels like pitching in...

FWIW, since fixest follows a particular protocol for recovering different SE estimates — which further implies, for example, that summary.fixest() simply yields another fixest object — I've decided to allow users two ways of adjusting the SEs with tidy():

1. Either through the fixest object that is fed to tidy()
2. Or, specify it directly in the tidy() call with the analogous "se" and "cluster" arguments.

Example:

> library(fixest)
> gravity <- feols(log(Euros) ~ log(dist_km) | Origin + Destination + Product + Year, trade)
> 
> tidy(gravity, conf.int = T)
# A tibble: 1 x 7
  term         estimate std.error statistic p.value conf.low conf.high
  <chr>           <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>
1 log(dist_km)    -2.17    0.0209     -104.       0    -2.21     -2.13
>  
> ## To get robust or clustered SEs, users can either:
>
> ## 1) Feed tidy() a summary.fixest object that has accepted these arguments
> gravity_summ <- summary(gravity, cluster = c("Product", "Year"))
> tidy(gravity_summ, conf.int = T)
# A tibble: 1 x 7
  term         estimate std.error statistic   p.value conf.low conf.high
  <chr>           <dbl>     <dbl>     <dbl>     <dbl>    <dbl>     <dbl>
1 log(dist_km)    -2.17    0.0743     -29.2 3.02e-187    -2.32     -2.02
>
> ## 2) Or, specify the arguments directly in the tidy() call
> tidy(gravity, conf.int = T, cluster = c("Product", "Year"))
# A tibble: 1 x 7
  term         estimate std.error statistic   p.value conf.low conf.high
  <chr>           <dbl>     <dbl>     <dbl>     <dbl>    <dbl>     <dbl>
1 log(dist_km)    -2.17    0.0743     -29.2 3.02e-187    -2.32     -2.02

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