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SimDesign

Structure for Organizing Monte Carlo Simulation Designs

Installation

To install the latest stable version of the package from CRAN, please use the following in your R console:

install.packages('SimDesign')

To install the Github version of the package with devtools, type the following (assuming you have already installed the devtools package from CRAN).

library('devtools')
install_github('philchalmers/SimDesign')

Getting started

For a discription pertaining to the philosophy and general workflow of the package it is helpful to first read through the following: Chalmers, R. Philip, Adkins, Mark C. (2020) Writing Effective and Reliable Monte Carlo Simulations with the SimDesign Package, The Quantitative Methods for Psychology, 16(4), 248-280. doi: 10.20982/tqmp.16.4.p248

Coding examples found within this article range from relatively simple (e.g., a re-implementation of one of Hallgren's (2013) simulation study examples, as well as possible extensions to the simulation design) to more advanced real-world simulation experiments (e.g., Flora and Curran's (2004) simulation study). For additional information and instructions about how to use the package please refer to the examples in the associated Github wiki.

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Version

Install

install.packages('SimDesign')

Monthly Downloads

6,019

Version

2.14

License

GPL (>= 2)

Issues

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Repository

https://github.com/philchalmers/SimDesign

Homepage

https://github.com/philchalmers/SimDesign/wiki

Maintainer

Phil Chalmers

Last Published

January 9th, 2024

Functions in SimDesign (2.14)

Perform a test that indicates whether a given Analyse() function should be executed

Bradley's (1978) empirical robustness interval

Compute the integrated root mean-square error

Perform a test that indicates whether a given Generate() function should be executed

Probabilistic Bisection Algorithm

Compute the relative efficiency of multiple estimators

Compute the relative performance behavior of collections of standard errors

Compute the mean absolute error

Compute the (normalized) root mean square error

Compute the relative standard error ratio

Compute the relative difference

Compute the relative absolute bias of multiple estimators

Robbins-Monro (1951) stochastic root-finding algorithm

Structure for Organizing Monte Carlo Simulation Designs

Empirical detection robustness method suggested by Serlin (2000)

Function to extract extra information from SimDesign objects

Template-based generation of the Generate-Analyse-Summarise functions

Function to read in saved simulation results

Function for decomposing the simulation into ANOVA-based effect sizes

Surrogate Function Approximation via the Generalized Linear Model

Removes/cleans files and folders that have been saved

Check the status of the simulation's temporary results

convertWarnings

Wrapper to convert all/specific warning messages to errors

Generate a basic Monte Carlo simulation GUI template

aggregate_simulations

Collapse separate simulation files into a single result

Summarise simulated data using various population comparison statistics

Add missing values to a vector given a MCAR, MAR, or MNAR scheme

Compute prediction estimates for the replication size using bootstrap MSE estimates

Form Column Standard Deviation and Variances

Create the simulation Design object

rejectionSampling

Rejection sampling (i.e., accept-reject method)

Generate integer values within specified range

Generate non-normal data with Vale & Maurelli's (1983) method

Suppress function messages and Concatenate and Print (cat)

Auto-named Concatenation of Vector or List

Run a summarise step for results that have been saved to the hard drive

Compute (relative/standardized) bias summary statistic

Generate data with the multivariate g-and-h distribution

Generate non-normal data with Headrick's (2002) method

rbind.SimDesign

Combine two separate SimDesign objects by row

One Dimensional Root (Zero) Finding in Simulation Experiments

Generate data with the inverse Wishart distribution

Generate data with the multivariate t distribution

Generate a random set of values within a truncated range

Generate data with the multivariate normal (i.e., Gaussian) distribution

Run a Monte Carlo simulation given a data.frame of conditions and simulation functions

Compute the empirical detection/rejection rate for Type I errors and Power

Example simulation from Brown and Forsythe (1974)

Compute empirical coverage rates

Compute estimates and statistics

Attach objects for easier reference

Compute congruence coefficient

BF_sim_alternative

(Alternative) Example simulation from Brown and Forsythe (1974)