Broom tidiers for deli estimators

tidy() and glance() methods for MEstimator and GMMEstimator objects. These allow deli results to flow into tidyverse pipelines.

Usage

# S3 method for class '`deli::deli_estimator`'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

# S3 method for class '`deli::deli_estimator`'
glance(x, ...)

Arguments

x

A fitted MEstimator or GMMEstimator object.

conf.int

Logical. Include confidence intervals? Default FALSE.

conf.level

Numeric confidence level for intervals. Default 0.95.

...

Not used. tidy() requires them to be empty, so that a misspelled conf.int or conf.level is an error rather than a table silently returned without intervals or at the default level. glance() has no optional argument for a wrong name to displace and ignores them.

Value

• tidy(): A data.frame with columns term, estimate, std.error, statistic, p.value, s.value. If conf.int = TRUE, also includes conf.low and conf.high. A p.value that underflows to exactly zero is reported as 0 alongside an infinite s.value; see s_values().

• glance(): A single-row data.frame with model-level summaries: nobs, npar, estimator, finite_correction, and the Hansen J-statistic of an over-identified GMM fit in j_statistic, j_df and j_p_value. The three J columns are present on every fit and hold the typed missing value of their own type where there is no such statistic, which is every M-estimation fit and every just-identified or subset GMM fit; see GMMEstimator() for what the statistic reads and where it is left unset.

See also

deli-augment, the third broom generic, which returns the observation-level fitted values, intervals, and residuals, and reexports for the generics themselves, which deli re-exports so that tidy() and glance() resolve with deli alone attached.

Examples

fit <- m_estimate(mpg ~ wt + hp, data = mtcars, .ee = ee_regression,
                  model = "linear")

tidy(fit, conf.int = TRUE)
#>          term    estimate   std.error statistic      p.value   s.value
#> 1 (Intercept) 37.22727012 1.938920511 19.199998 3.701056e-82 270.51017
#> 2          wt -3.87783074 0.619927275 -6.255299 3.967539e-10  31.23104
#> 3          hp -0.03177295 0.006646057 -4.780721 1.746674e-06  19.12696
#>      conf.low   conf.high
#> 1 33.42705575 41.02748449
#> 2 -5.09286587 -2.66279561
#> 3 -0.04479898 -0.01874691

glance(fit)
#>   nobs npar  estimator finite_correction j_statistic j_df j_p_value
#> 1   32    3 MEstimator              <NA>          NA   NA        NA