LinearLogit: Linear Regression or Logit Regression

In drcarlate: Improving Estimation Efficiency in CAR with Imperfect Compliance

Linear Regression or Logit Regression

Description

LinearLogit generates estimated pseudo true values for parametric models. Different estimation strategies are adopted according to different values of modelflag. See Jiang et al. (2022) for more details about different strategies.

Usage

LinearLogit(Y, D, A, X, S, s, modelflag, iridge)

Arguments

Y	The outcome vector. A nx1 vector.
D	A nx1 vector.
A	The treatment assignment. A nx1 vector.
X	Extra covariate matrix, A nxK matrix without constant.
S	The strata variable.
s	A particular stratum.
modelflag	Its value ranges from characters 1, 2, and 3, respectively declaring different estimation strategies. 1-L; 2-NL; 3-R.
iridge	A scalar. The penalization parameter in ridge regression.

Value

theta_0s, theta_1s, beta_0s, beta_1s are estimated coefficients vectors. The dimension is Kx1 if modelflag = 1; (K+1)x1 if modelflag = 2 or 3.

References

Jiang L, Linton O B, Tang H, Zhang Y. Improving estimation efficiency via regression-adjustment in covariate-adaptive randomizations with imperfect compliance [J]. 2022.

Examples

#' set.seed(1)
DGP <- FuncDGP(dgptype = 3, rndflag = 1, n = 10000, g = 4, pi = c(0.5, 0.5, 0.5, 0.5))
X <- DGP$X
Y <- DGP$Y
A <- DGP$A
S <- DGP$S
D <- DGP$D
LinearLogit(Y = Y, D = D, A = A, X = X, S = S, s = 1, modelflag = 1, iridge = 0.001)
LinearLogit(Y = Y, D = D, A = A, X = X, S = S, s = 2, modelflag = 2, iridge = 0.001)
LinearLogit(Y = Y, D = D, A = A, X = X, S = S, s = 3, modelflag = 3, iridge = 0.001)
LinearLogit(Y = Y, D = D, A = A, X = X, S = S, s = 4, modelflag = 3, iridge = 0.001)

drcarlate documentation built on July 9, 2023, 6:16 p.m.