Register custom fit statistics
Enables the registration of custom fi statistics that can be easily summoned with the function fitstat
.
fitstat_register(type, fun, alias = NULL, subtypes = NULL)
type |
A character scalar giving the type-name. |
fun |
A function to be applied to a |
alias |
A (named) character vector. An alias to be used in lieu of the type name in the display methods (ie when used in |
subtypes |
A character vector giving the name of each element returned by the function |
If there are several components to the computed statistics (i.e. the function returns several elements), then using the argument subtypes
, giving the names of each of these components, is mandatory. This is to ensure that the statistic can be used as any other built-in statistic (and there are too many edge cases impeding automatic deduction).
Laurent Berge
# An estimation base = iris names(base) = c("y", "x1", "x2", "x3", "species") est = feols(y ~ x1 + x2 | species, base) # # single valued tests # # say you want to add the coefficient of variation of the dependent variable cv = function(est){ y = model.matrix(est, type = "lhs") sd(y)/mean(y) } # Now we register the routine fitstat_register("cvy", cv, "Coef. of Variation (dep. var.)") # now we can summon the registered routine with its type ("cvy") fitstat(est, "cvy") # # Multi valued tests # # Let's say you want a Wald test with an heteroskedasticiy robust variance # First we create the function hc_wald = function(est){ w = wald(est, keep = "!Intercept", print = FALSE, se = "hetero") head(w, 4) } # This test returns a vector of 4 elements: stat, p, df1 and df2 # Now we register the routine fitstat_register("hc_wald", hc_wald, "Wald (HC1)", "test2") # You can access the statistic, as before fitstat(est, "hc_wald") # But you can also access the sub elements fitstat(est, "hc_wald.p")
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