Display methods for deli estimators

Methods for base::print() and base::summary() so that a fitted estimator shows its coefficients at the console and reports its parameter-level inference as a table.

Usage

# S3 method for class '`deli::deli_estimator`'
print(x, ..., subset = NULL)

# S3 method for class '`deli::deli_estimator`'
summary(object, alpha = 0.05, subset = NULL, ...)

Arguments

x, object

A fitted MEstimator or GMMEstimator object. Named x for print() and object for summary(), because base::print() and base::summary() name their first argument that.

...

Not used. Must be empty, so that a name neither method recognizes is an error rather than silently ignored: a misspelled alpha would report limits at the default width and a misspelled subset would display every parameter, each while the call still read as the one that was meant.

subset

Integer vector of parameter indices to display, or NULL (default) to display all of them.

alpha

Numeric significance level for the confidence limits reported by summary(), between 0 and 1. Default 0.05 for 95% limits.

Value

• print(): its input, invisibly.

• summary(): an object holding the estimates, standard errors, Z-scores, confidence limits, P-values, and S-values, which prints as a table.

Details

print() reports the parameter and observation counts followed by the estimates, rounded to four decimal places. An estimator that has not been through estimate() has no estimates to show, so it reports its parameter count and says so.

summary() collects the estimates, their standard errors, Z-scores, confidence limits, P-values, and S-values into one object, which prints as a table with one row per parameter. The values are those confint(), z_scores(), p_values(), and s_values() return individually, so alpha here means what it means there: 0.05 gives 95% limits.

An over-identified GMMEstimator fit reports Hansen's J-statistic above the table, with its degrees of freedom and its P-value, because it judges the fit as a whole rather than any one parameter. See GMMEstimator() for what it means and where its reference distribution holds. No other fit has one, so no other output carries the line.

The S column reads Inf when a P-value underflows to exactly zero, for the reason s_values() gives. The P column reports that same underflow as <2e-16: base::format.pval() stops printing digits below the eps it is given, and the table gives it 2.2e-16. tidy() returns the literal 0 instead.

subset restricts which parameters are displayed and nothing else. The reported parameter count and every reported value are computed from the whole fit, so displaying a subset of a stacked estimator is a way to read the parameters of interest without the nuisance parameters, not a way to refit without them. Row labels keep the names the full fit gave them.

See also

deli-generics for the accessors that return these quantities as plain vectors and matrices, and deli-tidiers for the same results as a data frame.

Examples

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

fit
#> <MEstimator>
#>   Parameters: 3
#>   Observations: 32
#> Coefficients:
#> (Intercept): 37.2273
#> wt: -3.8778
#> hp: -0.0318

# `subset` is an argument of the method rather than of the fit, so showing it
# here means calling the generic by name.
print(fit, subset = 2:3)
#> <MEstimator>
#>   Parameters: 3
#>   Observations: 32
#> Coefficients:
#> wt: -3.8778
#> hp: -0.0318

summary(fit)
#> ── MEstimator Results ──────────────────────────────────────────────────────────
#> Observations: 32
#> Parameters: 3
#> 
#>               Estimate    Std.Err    Z-score    95% LCL    95% UCL    P-value    S-value
#> (Intercept)    37.2273     1.9389    19.2000    33.4271    41.0275     <2e-16   270.5102
#> wt             -3.8778     0.6199    -6.2553    -5.0929    -2.6628   3.97e-10    31.2310
#> hp             -0.0318     0.0066    -4.7807    -0.0448    -0.0187   1.75e-06    19.1270

# `summary()` takes `subset` as well, alongside `alpha` for the width of the
# reported limits.
summary(fit, subset = 2:3, alpha = 0.1)
#> ── MEstimator Results ──────────────────────────────────────────────────────────
#> Observations: 32
#> Parameters: 3
#> 
#>         Estimate    Std.Err    Z-score    90% LCL    90% UCL    P-value    S-value
#> wt       -3.8778     0.6199    -6.2553    -4.8975    -2.8581   3.97e-10    31.2310
#> hp       -0.0318     0.0066    -4.7807    -0.0427    -0.0208   1.75e-06    19.1270