| addMagnitudePenalty {DiPs} | R Documentation |
Add a directional magnitude penalty to a distance matrix
Description
Adds a penalty to the distance component d of the distance object dist depending upon value of dx. The distance object dist has three components: d, start, end. d[i] gives the distance between the tth treated and the cth control, with t = start[i] and c = end[i]-sum(z). The value of dx for treated unit t, say dxt, is dx[z==1][t] and the value of dx for control c, say dxc, is dx[z==0][c]. Then, d[i] is adjusted using dxt-dxc. If positive=TRUE, the adjustment changes d[i] to d[i] + multiplier*(max(0, (dxt-dxc)-hstick)). That is, a penalty is imposed if dxt exceeds dxc by more than hstick. If positive = FALSE, the adjustment changes d[i] to d[i] + multiplier*(max(0, (dxc-dxt)-hstick)).
Usage
addMagnitudePenalty(dist, z, dx, positive = TRUE, hstick = 0,
multiplier = 2)
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 di in dx that is positive is increased by a multiple of |di|, but if positive = FALSE a a treated-minus-control difference in dx that is negative is increased by a multiple of |di|. |
hstick |
Hockey-stick value, which is a nonnegative number. See the description. |
multiplier |
The magnitide added is multiplier*|di|/s where s is an equally weighted, pooled within group standard deviation of dx. |
Value
Returns a new distance object whose distance component d is updated by the sum of dmat and the penalties.
References
Yu, R., & Rosenbaum, P. R. (2019). Directional penalties for optimal matching in observational studies. Biometrics, 75(4), 1380-1390.
Examples
## Not run:
library(MASS)
data("nh0506Homocysteine")
attach(nh0506Homocysteine)
# Select covariates
X<-cbind(female, age, black, education, povertyr, bmi)
#Propensity score
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$d)
# Impose a penalty when a treated individual has a higher propensity
# score than a control
dist<-addMagnitudePenalty(dist, d$z, d$p, positive=TRUE, multiplier = 20)
head(dist$d)
## End(Not run)