addDirectPenalty: Add a directional penalty to a distance object

In DiPs: Directional Penalties for Optimal Matching in Observational Studies

Add a directional penalty to a distance object

Description

Add a directional penalty to a distance object.

Usage

addDirectPenalty(dist, z, dx, positive = TRUE, penalty = 1)

Arguments

dist	A distance object with three components: d, start, end, typically created by maha_dense or maha_sparse. d[i] gives the distance between the (start[i])th treated and the (end[i]-sum(z))th control.
z	A vector whose ith coordinate is 1 for a treated unit and is 0 for a control. Must have treated subjects (z = 1) before controls (z = 0).
dx	A vector of with length(z) = length(dx) giving the variable used to define the caliper. For instance, dx might be the propensity score.
positive	If positive = TRUE, a treated-minus-control difference in dx that is positive is increased by penalty, but if positive = FALSE a a treated-minus-control difference in dx that is negative is increased by penalty. Zero differences are never penalized.
penalty	The number added to a distance when the desired direction is violated.

Value

Returns a new distance matrix that is the sum of dmat and the penalties for direction violations.

References

Yu, R., & Rosenbaum, P. R. (2019). Directional penalties for optimal matching in observationals tudies. Biometrics, 75(4), 1380-1390.

Examples

data("nh0506Homocysteine")
attach(nh0506Homocysteine)
X<-cbind(female, age, black, education, povertyr, bmi)
p<-glm(z ~ female + age + black + education + povertyr + bmi,
        family=binomial)$fitted.values
d<-cbind(nh0506Homocysteine, p)
detach(nh0506Homocysteine)
dist<-maha_dense(d$z, X)
head(dist$d)
dist<-addDirectPenalty(dist, d$z, d$p, positive=TRUE, penalty = 1)
head(dist$d)

DiPs documentation built on Aug. 7, 2022, 5:13 p.m.