| addDirectPenalty {DiPs} | R Documentation |
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)