| simulate2GExperimentData {reproducer} | R Documentation |
simulate2GExperimentData
Description
The function returns a two group data set based on one of four different distributions.
Usage
simulate2GExperimentData(
mean,
sd,
diff,
GroupSize,
type = "n",
ExpAdj = 0,
StdAdj = 0,
BlockEffect = 0,
BlockStdAdj = 0
)
Arguments
mean |
The mean (or rate for gamma data) of the baseline distribution |
sd |
The standard deviation (or shape for gamma data) of the baseline distribution |
diff |
The adjustment to the baseline mean for the alternative distribution. |
GroupSize |
An integer defining the number of data items in each group. |
type |
A string identifying the distribution used to simulate the data: 'n' for normal, 'ln' for log-normal, 'g' for gamma, 'lap' for Laplace. |
ExpAdj |
An additional adjustment factor that is added to both the mean value. Defaults to zero. |
StdAdj |
An additional adjustment factor that is added to both group variance (or rate for gamma data). Defaults to zero. |
BlockEffect |
An additional factor that is added to the mean of the both groups (shape for the gamma distribution). Defaults to zero. |
BlockStdAdj |
An additional factor that is added to the variance of both groups (shape for the gamma distribution). Defaults to zero. |
Value
A table with two columns (BaselineData and AlternativeData) holding the data for each group. For lognormal data an additional two columns are added which return the log transformed data.
Author(s)
Barbara Kitchenham and Lech Madeyski
Examples
set.seed(236)
simulate2GExperimentData(mean = 0, sd = 1, diff = 0.5, GroupSize = 10,
type = "n", ExpAdj = 0, StdAdj = 0, BlockEffect = 0, BlockStdAdj = 0)
# A tibble: 10 x 2
# BaselineData AlternativeData
# <dbl> <dbl>
# <dbl> <dbl>
# 1 -0.285 -0.255
# 2 -0.972 0.112
# 3 -0.549 1.36
# 4 1.05 1.47
# 5 -0.267 0.107
# 6 -0.137 0.395
# 7 1.30 1.27
# 8 -0.722 1.70
# 9 -0.525 0.264
# 10 -0.0222 0.787
set.seed(345)
simulate2GExperimentData(mean = 0, sd = 1, diff = 0.5, GroupSize = 10,
type = "l", ExpAdj = 0, StdAdj = 0, BlockEffect = 0, BlockStdAdj = 0)
# A tibble: 10 x 4
# BaselineData AlternativeData transBaselineData transAlternativeData
# <dbl> <dbl> <dbl> <dbl>
# 1 0.456 10.7 -0.785 2.37
# 2 0.756 0.407 -0.280 -0.900
# 3 0.851 0.705 -0.161 -0.350
# 4 0.748 2.27 -0.291 0.818
# 5 0.935 4.07 -0.0675 1.40
# 6 0.531 0.405 -0.634 -0.903
# 7 0.395 2.91 -0.928 1.07
# 8 5.53 4.69 1.71 1.55
# 9 5.23 0.602 1.65 -0.508
# 10 6.11 2.23 1.81 0.802