calculateLargeSampleRandomizedDesignEffectSizes: calculateLargeSampleRandomizedDesignEffectSizes

In reproducer: Reproduce Statistical Analyses and Meta-Analyses

calculateLargeSampleRandomizedDesignEffectSizes

Description

The function simulates a large experiment to estimate the asymptotic values of the probability of superiority, Cliff's d and the standardized mean difference data for a two group randomized experiment for four different distributions: Normal (i.e. type="n"), log-normal (i.e. type="l"), gama (i.e. tyep="g") and Laplace (i.e., type="lap").

Usage

calculateLargeSampleRandomizedDesignEffectSizes(
  meanC = 0,
  sdC = 1,
  diff = 0,
  N = 5e+06,
  type = "n",
  StdAdj = 0,
  reporttrans = "No"
)

Arguments

meanC	to act as the mean of the distribution used to generate the control group data (default 0) (note for the gamma distribution this is the rate parameter and must not be zero)
sdC	the variance/spread of the distribution used to generate the control group data (default 1).
diff	a value added to meanC to generate the treatment group data (default 0).
N	the size of each group (default 5000000)
type	the distribution of the data to be generated (default "n").
StdAdj	a value that can be added to sdC to introduce heterogeneity into the treatment group (default 0).
reporttrans	If set to "Yes" AND type="l" the algorithm returns the values obtained by analysing applying the logarithmic transformation to the simulated data (default "No").

Value

A tibble identifying the sample statistics and the values of the probability of superiority, Cliff's d and StdMD (labelled StdES)

Author(s)

Barbara Kitchenham and Lech Madeyski

Examples

set.seed=400
calculateLargeSampleRandomizedDesignEffectSizes(meanC=0, sdC=1, diff=.5,
N=10000, type="n",StdAdj = 0) #N=100000, type="n",StdAdj = 0)
# A tibble: 1 x 9
#     MeanC   SdC MeanT   SdT  Phat Cliffd   UES   Var StdES
#     <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
#1  0.00642  1.00 0.519 0.995 0.642  0.284 0.513 0.996 0.514
#1     0     1   0.5     1 0.637  0.275 0.499  1.01 0.497
as.data.frame(calculateLargeSampleRandomizedDesignEffectSizes(meanC=0, sdC=1, diff=0.707104,
N=100000, type="l",StdAdj = 0,reporttrans="Yes"))
#N=1000000, type="l",StdAdj = 0,reporttrans="Yes"))
#     MeanC     StdC    MeanT     StdT      Phat    Cliffd      UES      Var
#1 1.647446 2.219114  3.33124 4.404537 0.6926779 0.3853558 1.683795 12.1622
#       StdES   MeanCTrans MeanTTrans StdCTrans StdTTrans PhatTrans CliffdTrans
#1  0.4828175 -0.004298487  0.7066049  1.001199 0.9963736 0.6926779   0.3853558
#   UESTrans VarTrans StdESTrans
#1 0.7109034  0.99758  0.7117651

reproducer documentation built on Oct. 18, 2023, 5:10 p.m.