calculateLargeSampleRandomizedBlockDesignEffectSizes {reproducer} | R Documentation |
calculateLargeSampleRandomizedBlockDesignEffectSizes
Description
The function uses a 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 four group randomized blocks experiment for four different distributions: Normal (i.e. type='n'), log-normal (i.e. type='l'), gama (i.e. type='g') and Laplace (i.e., type='lap').
Usage
calculateLargeSampleRandomizedBlockDesignEffectSizes(
meanC = 0,
sdC = 1,
diff,
N = 5e+06,
type = "n",
Blockmean = 0,
StdAdj = 0
)
Arguments
meanC |
to act as the mean of the distribution (default 0) used to generate the control group data (note for the gamma distribution this is the rate parameter and must not be zero) |
sdC |
the variance/spread of the distribution (default 1) used to generate the control group data. |
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. One of: 'n' for normal (default), 'l' for log-normal, 'g' for gamma, and 'lap' for Laplace. |
Blockmean |
a value that can be added one of the blocks to represent a fixed block effect (default 0). |
StdAdj |
a value that can be added to sdC to introduce heterogeneity into the treatment group (default 0). |
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
calculateLargeSampleRandomizedBlockDesignEffectSizes(
meanC=0, sdC=1, diff=.5, N=100000, type='n',Blockmean=0.5,StdAdj = 0)
# MeanC SdC MeanT SdT BE Phat Cliffd UES Var StdES
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 0 1 0.5 1 0.5 0.638 0.277 0.501 0.998 0.502