reproducer (version 0.5.2)

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 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,
  alpha = 0.05,
  returnData = FALSE,
  AlwaysTwoSidedTests = 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 avd 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 specifies the extent of variance instability introduced by the treatment and it must be non-negative but can be 0.

alpha

This specifies the level of significance used for statistical tests (default 0.05).

returnData

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

AlwaysTwoSidedTests

If set to FALSE (default) the algorithms uses one-sided tests if diff!=0 and two-sided tests if diff=0. If set to TRUE the algorithm always uses two-sided tests.

Author

Barbara Kitchenham and Lech Madeyski

Examples

as.data.frame(
  RandomExperimentSimulations(
    mean = 0, sd = 1, diff = 0.5, N = 20, reps = 50, type = "n",
    seed = 123, StdAdj = 0, alpha = 0.05))
#        phat     phatvar sigphat emp.phat.var       d       dvar sigd
# 1  0.636675 0.007980072    0.38  0.006413391 0.27335 0.03257962 0.36
#    emp.d.var   tpower        ES Variance     StdES   MedDiff
#1  0.02565356     0.41 0.4849609 0.988889 0.4982554 0.4666802
#as.data.frame(
 # RandomExperimentSimulations(
 #   mean = 0, sd = 1, diff = 0.5, N = 20, reps = 500, type = "n",
 #   seed = 123, StdAdj = 0, alpha = 0.05))
#     phat     phatvar sigphat emp.phat.var      d       dvar  sigd  emp.d.var
# 1 0.63915 0.007925803   0.444  0.007904962 0.2783 0.03235111 0.414 0.03161985
#     tpower        ES Variance
# 1     0.444 0.4999034 1.002012
# 1      StdES   MedDiff
# 1 0.5099792 0.4901394

#as.data.frame(
#   RandomExperimentSimulations(
#     mean = 0, sd = 1, diff = 0.2, N = 20, reps = 500, type = "n",
#     seed = 123, StdAdj = 0, alpha = 0.05, AlwaysTwoSidedTests = TRUE))
#     phat     phatvar sigphat emp.phat.var       d       dvar  sigd emp.d.var
# 1 0.55762 0.008596555   0.092  0.008457325 0.11524 0.03505528 0.076 0.0338293
#     tpower        ES Variance     StdES   MedDiff
# 1       0.1 0.1999034 1.002012 0.2043908 0.1901394

#as.data.frame(
#   RandomExperimentSimulations(
#     mean = 0, sd = 1, diff = 0.2, N = 20, reps = 500, type = "n",
#     seed = 123, StdAdj = 0, alpha = 0.05, AlwaysTwoSidedTests = FALSE))
#     phat     phatvar sigphat emp.phat.var       d       dvar  sigd emp.d.var
# 1 0.55762 0.008596555   0.154  0.008457325 0.11524 0.03505528 0.146 0.0338293
#        tpower        ES Variance
# 1         0.16 0.1999034 1.002012
#      StdES   MedDiff
# 1 0.2043908 0.1901394

RandomExperimentSimulations(
  mean = 0, sd = 1, diff = 0.5, N = 20, reps = 10, type = "l", seed = 456,
  StdAdj = 0, alpha = 0.05, returnData = TRUE, AlwaysTwoSidedTests = FALSE)
# A tibble: 10 x 6
#   Cliffd CliffdSig  PHat PHatSig  StdES ESSig
#                
# 1 -0.185         0 0.407       0 -0.246     0
# 2 -0.08          0 0.46        0  0.185     0
# 3  0.1           0 0.55        0  0.149     0
# 4  0.42          1 0.71        1  0.885     1
# 5  0.51          1 0.755       1  0.827     1
# 6  0.185         0 0.592       0  0.628     1
# 7  0.465         1 0.732       1  0.818     1
# 8  0.42          1 0.71        1  0.341     0
# 9  0.37          1 0.685       1  0.419     0
# 10  0.115         0 0.557       0  0.273     0