Shiny + mclapply + mvrnorm

shiny_mv_mc.R

library(MASS)
library(shiny)
library(mrgsolve)
library(dplyr)
library(magrittr)
library(parallel)

RNGkind("L'Ecuyer-CMRG")
set.seed(101)
mc.reset.stream()


##' UI ###########################################
ui<- fluidPage(
  titlePanel("Shiny MC"),
  
  sidebarLayout(
    sidebarPanel(
      sliderInput("OM1", "OMEGA 1",0,4,1,0.25),
      sliderInput("OM2", "OMEGA 2",0,4,1,.25),
      sliderInput("n", "N rep", 1,100,10,1),
      sliderInput("N", "N subj", 1, 100,10,1),
      sliderInput("mccores", "mc.cores", 1,4,2,1)
    ),
    mainPanel(tableOutput("table")
    )
  )
)

##' Simulate
##' if(fix) the seed will be set based on replicate number
sim <- function(i,N,om1,om2) {
  mvrnorm(N,c(ETA1=0,ETA2=0),dmat(om1,om2)) %>% as.data.frame
}

##' Summarize simulations
smry <- function(x) {
  x %>% summarise(nETA1 = n_distinct(ETA1),
                  vETA1 = var(ETA1),
                  sETA1 = sum(ETA1),
                  nETA2 = n_distinct(ETA2),
                  vETA2 = var(ETA2),
                  sETA2 = sum(ETA2),
                  n=n())
}

##' SERVER ###########################################
server<-function(input, output) {
  
  output$table <- renderTable({
    
    mclapply(1:input$n,
             mc.cores=input$mccores,
             sim,input$N,input$OM1,input$OM2) %>% bind_rows %>% smry
  })
}

##' Run the shiny app
shinyApp(ui = ui, server = server)