misim {clarify}  R Documentation 
Simulate model coefficients after multiple imputation
Description
misim()
simulates model parameters from multivariate normal or t distributions after multiple imputation that are then used by sim_apply()
to calculate quantities of interest.
Usage
misim(fitlist, n = 1000, vcov = NULL, coefs = NULL, dist = NULL)
Arguments
fitlist 
a list of model fits, one for each imputed dataset, or a 
n 
the number of simulations to run for each imputed dataset; default is 1000. More is always better but resulting calculations will take longer. 
vcov 
a square covariance matrix of the coefficient covariance estimates, a function to use to extract it from 
coefs 
a vector of coefficient estimates, a function to use to extract it from 
dist 
a character vector containing the name of the multivariate distribution(s) to use to draw simulated coefficients. Should be one of 
Details
misim()
essentially combines multiple sim()
calls applied to a list of model fits, each fit in an imputed dataset, into a single combined pool of simulated coefficients. When simulationbased inference is to be used with multiply imputed data, many imputations are required; see Zhou and Reiter (2010).
Value
A clarify_misim
object, which inherits from clarify_sim
and has the following components:
sim.coefs 
a matrix containing the simulated coefficients with a column for each coefficient and a row for each simulation for each imputation 
coefs 
a matrix containing the original coefficients extracted from 
fit 
the list of model fits supplied to 
imp 
a identifier of which imputed dataset each set of simulated coefficients corresponds to. 
The "dist"
attribute contains "normal"
if the coefficients were sampled from a multivariate normal distribution and "t({df})"
if sampled from a multivariate t distribution. The "clarify_hash"
attribute contains a unique hash generated by rlang::hash()
.
References
Zhou, X., & Reiter, J. P. (2010). A Note on Bayesian Inference After Multiple Imputation. The American Statistician, 64(2), 159–163. doi:10.1198/tast.2010.09109
See Also

sim()
for simulating model coefficients for a single dataset 
sim_apply()
for applying a function to each set of simulated coefficients 
sim_ame()
for computing average marginal effects in each simulation draw 
sim_setx()
for computing marginal predictions and first differences at typical values in each simulation draw
Examples
data("africa", package = "Amelia")
# Multiple imputation using Amelia
a.out < Amelia::amelia(x = africa, m = 10,
cs = "country",
ts = "year", logs = "gdp_pc",
p2s = 0)
fits < with(a.out, lm(gdp_pc ~ infl * trade))
# Simulate coefficients
s < misim(fits)
s