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#. Then, simulation dataset and added to the multiple imputation dataset. for(i in 1:4){ if(mode[i] == 'car'){simulations = c('05', '10', '15', '20', '25', '30')} if(mode[i] %in% c('walking', 'public')){simulations = seq(10,60,10)} if(mode[i] == 'biking'){simulations = c('050', '100', '150', '200', '250', '300')} for(sim in simulations){ # selection file for transportation mode pathin

}. Mean, pool(mi_rf, 'mvpa_day', 'original') mean.pool.g(mi_rf, 'mvpa_day', 'nivetude_sim

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