Description Details Author(s) References See Also Examples
In this package, you can find two functions proposed in Ding, Geng and Zhou (2011) to estimate direct and indirect causal effects with randomization and multiple-component intervention using instrumental variable method.
Package: | ImpactIV |
Type: | Package |
Version: | 1.0 |
Date: | 2010-12-12 |
License: | GPL (>=2) |
LazyLoad: | yes |
Maintainer: Peng Ding <dingyunyiqiu@163.com>
Ding, P., Geng, Z. and Zhou, X. H. (2011). Identifying Causal Effect for Multi-Component Intervention Using Instrumental Variable Method: with A Case Study of IMPACT Data. Technical Report.
homo_IV1, heter_IV2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | data(impact)
Z=impact$Z
A=impact$A
M=impact$M
Y=scale(impact$Y)
X=as.matrix(impact[,5:12])
##continuos variables of X
Xcon = X[, c(1,4,6,8)]
##discrete variables of X
Xdis = X[, c(2,3,5,7)]
##X^2
X2 = cbind(X, poly(Xcon, degree = 2, raw = TRUE),
Xcon*Xdis[,1], Xcon*Xdis[,2], Xcon*Xdis[,3], Xcon*Xdis[,4])
method1 = homo_IV1(Z = Z,A = A,M = M,Y = Y,X = X)
method2 = heter_IV2(Z = Z,A = A,M = M,Y = Y,X = X2,
polydegree = 1, step1 = method1,
truncate = 0.25, select ="AIC")
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