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, ...)

Arguments

x

A fixest object returned from any of the fixest estimators

conf.int

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.

conf.level

The confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.

...

Additional arguments passed to summary and confint. Important arguments are se and cluster. Other arguments are dof, exact_dof, forceCovariance, and keepBounded. See summary.fixest.

Details

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)

See also

Value

A tibble::tibble() with columns:

conf.high

Upper bound on the confidence interval for the estimate.

conf.low

Lower bound on the confidence interval for the estimate.

estimate

The estimated value of the regression term.

p.value

The two-sided p-value associated with the observed statistic.

statistic

The value of a T-statistic to use in a hypothesis that the regression term is non-zero.

std.error

The standard error of the regression term.

term

The name of the regression term.

Examples

# \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 8.15e-45
glance(gravity)
#> # 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>
augment(gravity, trade)
#> # 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 0.0743 -29.2 2.40e-185 -2.32 -2.02
tidy(gravity, conf.int = TRUE, se = "threeway")
#> # 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 0.178 -12.2 3.44e-34 -2.52 -1.82
# 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 0.0743 -29.2 2.40e-185 -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
glance(gravity_pois)
#> # 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>
augment(gravity_pois, trade)
#> # 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
# }