Computes the influence function values for individual observations: $$IF(O_i; \theta) = B_n(\theta)^{-1} \psi(O_i; \theta)$$
This function mirrors m.influence_functions() in Python delicatessen, so
code translated from Python can keep its shape. There is no base R accessor
for influence function values, so this is the interface for them in deli as
well.
Arguments
- object
A fitted
MEstimatorobject (after callingestimate()).- allow_pinv
Logical. Use pseudo-inverse if bread is singular? Default
TRUE.- ...
Not used. Must be empty, so a name that is not one of the documented arguments is an error rather than silently ignored.
Value
An n-by-p matrix of influence function values, where n is the
number of observations and p is the number of parameters. Columns are named
for the parameters, as in coef(). The rows take whatever
labels the estimating function put on the columns of its own return, so a
fit whose estimating function collapses its contributions with
aggregate_efuncs() has one row per group, labeled with the group value,
and every row is that group's influence rather than an observation's.
Examples
fit <- m_estimate(mpg ~ wt + hp, data = mtcars, .ee = ee_regression,
model = "linear")
# One row per observation, showing its contribution to each estimate
head(influence_functions(fit))
#> (Intercept) wt hp
#> [1,] -7.88511089 1.236467576 0.009099131
#> [2,] -3.44007774 -0.007985771 0.012831874
#> [3,] -10.16026284 1.851173715 0.011785054
#> [4,] 0.13254570 0.088097892 -0.001915625
#> [5,] 0.08947257 -0.030823617 0.002607096
#> [6,] -0.28620413 -2.869668817 0.048707825