mi.boot {CICI} | R Documentation |
Obtaining estimates from multiply imputed data
Description
Combines gformula
estimates obtained from multiple imputed data sets according to the MI Boot and MI Boot pooled methods decribed in Schomaker and Heumann (2018, see reference section below)
Usage
mi.boot(x, fun, cond=NULL, pooled=FALSE, ...)
Arguments
x |
A list of objects of |
fun |
A function to be applied to the outcome(s) of the counterfactual data set. For expected outcome, use |
cond |
A string containing a condition to be applied to the counterfactual datasets. |
pooled |
Logical. If TRUE, confidence interval estimation is based on the MI Boot pooled from Schomaker and Heumann (2018), otherwise on MI Boot. |
... |
additional arguments to be passed on to |
Value
An object of class gformula
. See gformula
for details.
Author(s)
Michael Schomaker
References
Schomaker, M., Heumann, C. (2018) Bootstrap inference when using multiple imputation, Statistics in Medicine, 37:2252-2266
Examples
data(EFV)
# suppose the following subsets were actually multiply imputed data (M=2)
EFV_1 <- EFV[1:2500,]
EFV_2 <- EFV[2501:5000,]
# first: conduct analysis on each imputed data set. Set ret=T.
m1 <- gformula(X=EFV_1,
Lnodes = c("adherence.1","weight.1",
"adherence.2","weight.2",
"adherence.3","weight.3",
"adherence.4","weight.4"
),
Ynodes = c("VL.0","VL.1","VL.2","VL.3","VL.4"),
Anodes = c("efv.0","efv.1","efv.2","efv.3","efv.4"),
abar=seq(0,5,1), verbose=FALSE, ret=TRUE
)
m2 <- gformula(X=EFV_2,
Lnodes = c("adherence.1","weight.1",
"adherence.2","weight.2",
"adherence.3","weight.3",
"adherence.4","weight.4"
),
Ynodes = c("VL.0","VL.1","VL.2","VL.3","VL.4"),
Anodes = c("efv.0","efv.1","efv.2","efv.3","efv.4"),
abar=seq(0,5,1), verbose=FALSE, ret=TRUE
)
# second combine results
m_imp <- mi.boot(list(m1,m2), mean) # uses MI rules & returns 'gformula' object
plot(m_imp)
# custom estimand: evaluate probability of suppression (Y=0), among females
m_imp2 <- mi.boot(list(m1,m2), prop, categ=0, cond="sex==1")
plot(m_imp2)