'amelidiate' takes the output from
mediations and stacks the different vectors. Next it outputs these stacked vectors in the format of a
output from mediations that used the same models and variables but run on different datasets.
amelidiate is designed to help users process multiple datasets where missing values have been imputed. First create multiple datasets using your preferred imputation software.
Next pass the data sets, as shown in the example below, to the
mediations function. Finally pass the output of mediations through the
amelidiate function. This will output an object that can then be passed through the standard summary and plot commands.
This function is not completely developed. It does not support models for ordered outcomes, inherits the limitations of the mediations function, and does not pass the information required for calculation of p-values.
An object of class "mediate".
Dustin Tingley, Harvard University, [email protected]
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## Not run: # Hypothetical example ## To use mediations, must make list of multiple datasets. Then, ## must also repeat the treatment assignment list as many times ## as you have data sets. # datasets <- list(D1=D1, D2=D2) # list of multiply imputed data sets # mediators <- c("M1") # outcome <- c("Ycont1") # treatment <- c("T1","T1") # note how the treatment indicator is repeated # covariates <- c("X1+X2") # olsols <- mediations(datasets, treatment, mediators, outcome, covariates, # families=c("gaussian","gaussian"), interaction=FALSE, # conf.level=.90, sims=1000) # output <- amelidiate(olsols) # summary(output) # plot(output) ## End(Not run)
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