calculate_weights: Calculate regression weights

View source: R/calculate_weights.R

calculate_weightsR Documentation

Calculate regression weights


Given a model and a term of interest, calculate the Aronow and Samii (2015) doi: 10.1111/ajps.12185 regression weights and return an object which can be used to diagnose these implicit weights.


calculate_weights(mod, term)



The linear model object from lm or lm_robust.


String indicating the term for which to calculate the implicit regression weights. This must uniquely match a coefficient name (i.e. it must be a string which appears in only one element of coef(mod)).


This calculates the implicit regression weights for a particular term in a given regression model.

In short, this calculates the weights for a coefficient β such that:

\frac{\mathrm{E}[w_i β_i]}{\mathrm{E}[w_i]} \to β

where β_i is the unit level effect. The expectation of w_i is the conditional variance of the variable of interest.

For details and examples, view the vignette: vignette("example-usage", package = "regweight")


An object of class regweight containing:

term The term in the regression for which weights were calculated.
model The partial regression model object.
weights The implicit regression weights.


Aronow, P.M. and Samii, C. (2016), "Does Regression Produce Representative Estimates of Causal Effects?". American Journal of Political Science, 60: 250-267. doi: 10.1111/ajps.12185


y <- rnorm(100)
a <- rbinom(100, 1, 0.5)
x <- rnorm(100)
m1 <- stats::lm(y ~ a + x)

w1 <- calculate_weights(m1, "a")

regweight documentation built on March 18, 2022, 7:53 p.m.