Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
# S3 method for fixest tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
| x | A |
|---|---|
| conf.int | Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |
| conf.level | The confidence level to use for the confidence interval
if |
| ... | Additional arguments passed to |
The fixest package provides a family of functions for estimating
models with arbitrary numbers of fixed-effects, in both an OLS and a GLM
context. The package also supports robust (i.e. White) and clustered
standard error reporting via the generic summary.fixest() command. In a
similar vein, the tidy() method for these models allows users to specify
a desired standard error correction either 1) implicitly via the supplied
fixest object, or 2) explicitly as part of the tidy call. See examples
below.
Note that fixest confidence intervals are calculated assuming a normal distribution -- this assumes infinite degrees of freedom for the CI. (This assumption is distinct from the degrees of freedom used to calculate the standard errors. For more on degrees of freedom with clusters and fixed effects, see https://github.com/lrberge/fixest/issues/6 and https://github.com/sgaure/lfe/issues/1#issuecomment-530646990)
tidy(), fixest::feglm(), fixest::fenegbin(),
fixest::feNmlm(), fixest::femlm(), fixest::feols(), fixest::fepois()
Other fixest tidiers:
augment.fixest()
A tibble::tibble() with columns:
Upper bound on the confidence interval for the estimate.
Lower bound on the confidence interval for the estimate.
The estimated value of the regression term.
The two-sided p-value associated with the observed statistic.
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
The standard error of the regression term.
The name of the regression term.
# \donttest{ library(fixest) gravity <- feols(log(Euros) ~ log(dist_km) | Origin + Destination + Product + Year, trade) tidy(gravity)#> # A tibble: 1 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 log(dist_km) -2.17 0.154 -14.1 0.00000000119#> # A tibble: 1 x 9 #> r.squared adj.r.squared within.r.squared pseudo.r.squared sigma nobs AIC #> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> #> 1 0.706 0.705 0.219 NA 1.74 38325 1.51e5 #> # … with 2 more variables: BIC <dbl>, logLik <dbl>#> # A tibble: 38,325 x 9 #> .rownames Destination Origin Product Year dist_km Euros .fitted .resid #> <chr> <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 LU BE 1 2007 140. 2966697 14.1 0.812 #> 2 2 BE LU 1 2007 140. 6755030 13.0 2.75 #> 3 3 LU BE 2 2007 140. 57078782 16.9 0.924 #> 4 4 BE LU 2 2007 140. 7117406 15.8 -0.0470 #> 5 5 LU BE 3 2007 140. 17379821 16.3 0.378 #> 6 6 BE LU 3 2007 140. 2622254 15.2 -0.402 #> 7 7 LU BE 4 2007 140. 64867588 17.4 0.595 #> 8 8 BE LU 4 2007 140. 10731757 16.3 -0.0937 #> 9 9 LU BE 5 2007 140. 330702 14.1 -1.37 #> 10 10 BE LU 5 2007 140. 7706 13.0 -4.02 #> # … with 38,315 more rows## To get robust or clustered SEs, users can either: # 1) Or, specify the arguments directly in the tidy() call tidy(gravity, conf.int = TRUE, 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 0.0760 -28.5 3.88e-10 -2.32 -2.02#> # 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 0.175 -12.4 0.00000000608 -2.51 -1.83# 2) Feed tidy() a summary.fixest object that has already accepted these arguments gravity_summ <- summary(gravity, cluster = c("Product", "Year")) tidy(gravity_summ, conf.int = TRUE)#> # 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 0.0760 -28.5 3.88e-10 -2.32 -2.02# Approach (1) is preferred. ## The other fixest methods all work similarly. For example: gravity_pois <- feglm(Euros ~ log(dist_km) | Origin + Destination + Product + Year, trade) tidy(gravity_pois)#> # A tibble: 1 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 log(dist_km) -1.53 0.116 -13.2 7.89e-40#> # A tibble: 1 x 9 #> r.squared adj.r.squared within.r.squared pseudo.r.squared sigma nobs AIC #> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> #> 1 NA NA NA 0.764 NA 38325 1.40e12 #> # … with 2 more variables: BIC <dbl>, logLik <dbl>#> # A tibble: 38,325 x 9 #> .rownames Destination Origin Product Year dist_km Euros .fitted .resid #> <chr> <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 LU BE 1 2007 140. 2966697 16.0 -6.06e6 #> 2 2 BE LU 1 2007 140. 6755030 15.4 1.97e6 #> 3 3 LU BE 2 2007 140. 57078782 17.4 2.00e7 #> 4 4 BE LU 2 2007 140. 7117406 16.8 -1.26e7 #> 5 5 LU BE 3 2007 140. 17379821 16.7 -1.00e4 #> 6 6 BE LU 3 2007 140. 2622254 16.0 -6.60e6 #> 7 7 LU BE 4 2007 140. 64867588 17.5 2.64e7 #> 8 8 BE LU 4 2007 140. 10731757 16.8 -9.64e6 #> 9 9 LU BE 5 2007 140. 330702 14.5 -1.64e6 #> 10 10 BE LU 5 2007 140. 7706 13.9 -1.04e6 #> # … with 38,315 more rows# }