acpme | R Documentation |
This function simulates the posterier exposure effect using the Bayesian adjustment for confounding in the presence of multivariate exposures (ACPME) meethod.
acpme(
Z,
C,
y,
niter,
burnin = round(niter/2),
pen.lambda = NA,
pen.type = "eigen"
)
Z |
Matrix of exposures. This should include any interactions of other functions of exposures. |
C |
A n x p matrix or data.frame of covaraites. |
y |
An n-vector of observed outcomes. |
niter |
Integer number of MCMC iterations to compute including burnin. |
burnin |
Integer number of MCMC iterations to discard as burning. |
pen.lambda |
Non-negative tuning parameter lambda to control the strength of confounder adjustment (strength of prior or size of penalty). A value of NA (defailt) uses BIC to choose the value. |
pen.type |
Choice of penalty. The default is "eigen." Other options are "correlation" and "projection." |
dat <- simregimes(scenario="acpme1", seed=1234, n=200, p=100)
fit <- acpme(Z=dat$Z,C=dat$C,y=dat$Y, niter=1000)
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