Structure for Organizing Monte Carlo Simulation Designs


[Up] [Top]

Documentation for package ‘SimDesign’ version 1.13

Help Pages

SimDesign-package Structure for Organizing Monte Carlo Simulation Designs
add_missing Add missing values to a vector given a MCAR, MAR, or MNAR scheme
aggregate_simulations Collapse separate simulation files into a single result
Analyse Compute estimates and statistics
as.data.frame.SimDesign Run a Monte Carlo simulation given a data.frame of conditions and simulation functions
Attach Attach the simulation conditions for easier reference
BF_sim Example simulation from Brown and Forsythe (1974)
BF_sim_alternative (Alternative) Example simulation from Brown and Forsythe (1974)
bias Compute (relative/standardized) bias summary statistic
boot_predict Compute prediction estimates for the replication size using bootstrap MSE estimates
ECR Compute the empirical coverage rate for Type I errors and Power
EDR Compute the empirical detection rate for Type I errors and Power
extract_error_seeds Run a Monte Carlo simulation given a data.frame of conditions and simulation functions
extract_results Run a Monte Carlo simulation given a data.frame of conditions and simulation functions
Generate Generate data
head.SimDesign Run a Monte Carlo simulation given a data.frame of conditions and simulation functions
IRMSE Compute the integrated root mean-square error
MAE Compute the mean absolute error
MSRSE Compute the relative performance behavior of collections of standard errors
print.SimDesign Run a Monte Carlo simulation given a data.frame of conditions and simulation functions
quiet Suppress function messages and Concatenate and Print (cat)
RD Compute the relative difference
RE Compute the relative efficiency of multiple estimators
rejectionSampling Rejection sampling (i.e., accept-reject method) to draw samples from difficult probability density functions
rHeadrick Generate non-normal data with Headrick's (2002) method
rint Generate integer values within specified range
rinvWishart Generate data with the inverse Wishart distribution
rmgh Generate data with the multivariate g-and-h distribution
RMSE Compute the (normalized) root mean square error
rmvnorm Generate data with the multivariate normal (i.e., Gaussian) distribution
rmvt Generate data with the multivariate t distribution
rtruncate Generate a random set of values within a truncated range
runSimulation Run a Monte Carlo simulation given a data.frame of conditions and simulation functions
rValeMaurelli Generate non-normal data with Vale & Maurelli's (1983) method
Serlin2000 Empirical detection robustness method suggested by Serlin (2000)
SimAnova Function for decomposing the simulation into ANOVA-based effect sizes
SimBoot Function to present bootstrap standard errors estimates for Monte Carlo simulation meta-statistics
SimClean Removes/cleans files and folders that have been saved
SimDesign Structure for Organizing Monte Carlo Simulation Designs
SimFunctions Skeleton functions for simulations
SimResults Function to read in saved simulation results
SimShiny Generate a basic Monte Carlo simulation GUI template
subset.SimDesign Subset method for SimDesign objects
Summarise Summarise simulated data using various population comparison statistics
summary.SimDesign Run a Monte Carlo simulation given a data.frame of conditions and simulation functions
tail.SimDesign Run a Monte Carlo simulation given a data.frame of conditions and simulation functions