SuperAdap | R Documentation |
SuperAdap
is the main function for performing a one-sided test
under the Rosenbaum bounds sensitivity analysis framework via the two-stage
programming method, which is an adaptive approach with a nice asymptotic
property called "super-adaptivity". For details of the two-stage programming
method, see the paper "Increasing Power for Observational Studies of Aberrant
Response: An Adaptive Approach" by Heng, Kang, Small, and Fogarty.
SuperAdap(Q, Z, index, alpha, Gamma, alternative)
Q |
An N by 2 matrix of scores of the two component sum test statistics,
where N is the total number of units in the study. That is, the n-th
entry of the first column of |
Z |
An N-length vector of the treatment indicators of all N units in the
study: 1 if treated and 0 if not. The n-th entry of |
index |
An N-length vector of the matched set indexes of all N units in
the study, taking value from 1 to I, where I is the total number of
matched sets. That is, the n-th entry of |
alpha |
The level of the one-sided test. |
Gamma |
The sensitivity parameter in the Rosenbaum bounds sensitivity analysis, which is a prespecified number that is greater than or equal to 1. |
alternative |
The direction of the alternative in a one-sided test, can be either "greater", i.e., greater than, or "less", i.e., less than. |
An indicator of the null hypothesis to be rejected or not: "reject" or "failed to reject".
#We randomly generate a dataset with I matched sets along with #the scores of the two component test statistics I=200 n<-rep(0, I) for (i in 1:I){ n[i]=sample(c(2:6), size = 1) } N=sum(n) index_1<-rep(0, N) Z_1<-rep(0, N) Q_1<-matrix(0, nrow = N, ncol = 2) S<-rep(0, I) for (i in 1:I){ S[i]=sum(n[1:i]) } index_1[1:S[1]]=1 Z_1[1]=1 Z_1[1:S[1]]=sample(Z_1[1:S[1]], size = S[1]) if (I>1){ for (i in 2:I){ index_1[(S[i-1]+1):S[i]]=i Z_1[(S[i-1]+1)]=1 Z_1[(S[i-1]+1):S[i]]=sample(Z_1[(S[i-1]+1):S[i]], size = n[i]) } } for (s in 1:N){ if (Z_1[s]==1){ Q_1[s, 1]=rnorm(1, mean = 0.5, sd=1) Q_1[s, 2]=rnorm(1, mean = 0.6, sd=1) } else { Q_1[s, 1]=rnorm(1, mean = 0.1, sd=1) Q_1[s, 2]=rnorm(1, mean = 0, sd=1) } } #We then run the SuperAdap function with the generated dataset result_1=SuperAdap(Q = Q_1, Z = Z_1, index = index_1, alpha = 0.05, Gamma = 1.5, alternative = "greater") result_2=SuperAdap(Q = Q_1, Z = Z_1, index = index_1, alpha = 0.05, Gamma = 5, alternative = "greater")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.