simulation_output.rmd

title: "Simulation experimental design"
author: ""
date: "2023-01-05"
params:
  file: "Projects/SE_DRIVE/parameters_SE_DRIVE.R"
format:
  html:
    embed-resources: true

knitr::opts_chunk$set(echo = TRUE)

source("simulationcore_purrr.R" ,echo = TRUE)
         
# retrieve the values of the objects starting with "b" using mget()
# b_values <- mget(ls(pattern = "^b"))
# 
# # combine the values into a named vector using c()
# combined_vector <- c(b_values)
# 
# # add names to the vector based on the original object names
# names(combined_vector) <- names(b_values)
# 
# # print the resulting named vector
# print(combined_vector)

The simulation has r resps respondents and r nosim runs.

the parameters used for the simulation are:

designs_all <- readRDS("output/330_5000runs_4designs_mixl.RDS")

Statistics and power

Here you see the statistics of your parameters for the r nosim runs.

kable(summaryall ,digits = 3) %>% kable_styling()

powa

Illustration of simulated parameter values

To facilitate interpretation and judgement of the different designs, you can plot the densities of simulated parameter values from the different experimental designs.

map(p,print)


do.call(grid.arrange,p)