reproducer (version 0.4.2)

RandomExperimentSimulations: RandomExperimentSimulations

Description

This function performs multiple simulations of two-group balanced experiments for one of four distributions and a specific group size. It identifies the average value of phat, Cliff' d and Kendall's point biserial tau and their variances. It either returns the effect sizes for each non-parametric effect size or it reports the number of times the each non-parametric effect size is assessed to be significantly different from zero. We also present the values for the t-test as a comparison. For log-normal data the results of analysing the transformed data are also reported.

Usage

RandomExperimentSimulations(
  mean,
  sd,
  diff,
  N,
  reps,
  type = "n",
  seed = 123,
  StdAdj = 0,
  returnData = FALSE
)

Arguments

mean

The default mean used for both groups (one treatment group and one control group). It can be changed for the treatment group using the parameter diff

sd

This is the default spread for both groups. It must be a real value greater than 0. It can be adjusted for the treatment group using the parameter StdAdj

diff

This is added to the treatment group mean. It can be a real value and can take the value zero.

N

this is the number of observations in each group. It must be an integer greater than 3.

reps

this identifies the number of times each experiment simulation is replicated.

type

this specifies the underlying distribution used to generate the data. It takes the values "n" for a normal distribution, "l" for lognormal distribution,"g" for a gamma distribution, "lap" for a Laplace distribution.

seed

This specifies the initial seed for the set of replications (default 123).

StdAdj

this specifes the extent of variance instability introduced by the treatment and it must be non-negative but can be 0.

returnData

If TRUE, the function returns the individual effect sizes and their variances, otherwise it returns summary statistics (default FALSE).

Author

Barbara Kitchenham and Lech Madeyski

Examples

#RandomExperimentSimulations(mean=0,sd=1,diff=0.5,N=20, reps=500,type="n",seed=123,StdAdj=0)
#reps=500 may take a bit too for CRAN so let us use reps=50
RandomExperimentSimulations(mean=0,sd=1,diff=0.5,N=20, reps=50,type="n",seed=123,StdAdj=0)
# A tibble: 1 x 17
#   phat phatvar sigphat emp.phat.var     d   dvar  sigd emp.d.var  ktau ktauvar emp.tau.var
# kpowerCVt tpower    ES
#                                    
#    
#1 0.639 0.00793    0.33      0.00790 0.278 0.0324 0.306    0.0316 0.143 0.00854     0.00832
# 0.328  0.338 0.500
# … with 3 more variables: Variance , StdES , MedDiff 
#RandomExperimentSimulations(mean=0,sd=1,diff=0.5,N=20, reps=500,type="l",seed=123,StdAdj=0)
#reps=500 may take a bit too for CRAN so let us use reps=50
RandomExperimentSimulations(mean=0,sd=1,diff=0.5,N=20, reps=50,type="l",seed=123,StdAdj=0)
# A tibble: 1 x 20
#   phat phatvar sigphat emp.phat.var     d   dvar  sigd emp.d.var  ktau ktauvar emp.tau.var
# kpowerCVt tpower    ES
#                                    
#    
#1 0.639 0.00793    0.33      0.00790 0.278 0.0324 0.306    0.0316 0.143 0.00854     0.00832
# 0.328  0.218  1.05
# … with 6 more variables: Variance , StdES , MedDiff , ESLog ,
# StdESLog , VarLog 
RandomExperimentSimulations(mean=0,sd=1,diff=0.5,N=20, reps=10,type="n",seed=123,StdAdj=0,
returnData=TRUE)
#  A tibble: 10 x 3
#    Cliffd  PHat   StdES
#         
#  1  0.175 0.588  0.340
#  2  0.19  0.595  0.283
#  3 -0.125 0.438 -0.305
#  4  0.195 0.597  0.345
#  5  0.15  0.575  0.200
#  6  0.3   0.65   0.430
#  7  0.455 0.728  0.762
#  8  0.015 0.507  0.0622
#  9  0.16  0.58   0.340
# 10  0.2   0.6    0.336