reproducer (version 0.5.3)

simulate4GExperimentData: simulate4GExperimentData

Description

The function returns a four group data set based on one of four different distributions.

Usage

simulate4GExperimentData(
  mean,
  sd,
  diff,
  GroupSize,
  type = "n",
  ExpAdj = 0,
  StdAdj = 0,
  BlockEffect = 0,
  BlockStdAdj = 0
)

Value

A table with four columns (BaselineData.B1, AlternativeData.B1,BaselineData.B2, AlternativeData.B2,) holding the data for each group and block. For lognormal data an additional four columns are added which return the log transformed data for each group.

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 distrubtion used to simulate the data: 'n' for normal, 'l' for log-normal, 'g' for gamma, 'lap' for Laplace.

ExpAdj

An additional adjument factor that is added to both the mean values. Defaults to zero.

StdAdj

An aditional adjustment factor that is added to the second group variance (or rate for gamma data). Defaults to zero.

BlockEffect

An additional factor that is added to the mean of the second group groups (shape for the gamma distribution). Defaults to zero.

BlockStdAdj

An additional factor that is added to the variance of the second group (shape for the gamma distribution). Defaults to zero.

Author

Barbara Kitchenham and Lech Madeyski

Examples

set.seed(246)
simulate4GExperimentData(mean = 0, sd = 1, diff = 0.5, GroupSize = 5,
  type = "n", ExpAdj = 0, StdAdj = 0, BlockEffect = 0.5, BlockStdAdj = 0)
# A tibble: 5 x 4
#  BaselineData.B1 AlternativeData.B1 BaselineData.B2 AlternativeData.B2
#                                                   
# 1          0.533               1.84            0.749              3.98
# 2          0.251               2.03            1.56               1.09
# 3         -0.290               0.929           0.213              3.94
# 4         -1.48                1.17            1.13               0.106
# 5          0.0340              0.895           0.399              0.879
as.data.frame(
  simulate4GExperimentData(
    mean=0, sd=1, diff=0.5, GroupSize=5, type='l', ExpAdj=0, StdAdj=0,
    BlockEffect = 0.5, BlockStdAdj = 0))
#  BaselineData.B1 AlternativeData.B1 transBaselineData.B1 transAlternativeData.B1
#1       1.4019869           1.049158            0.3378905               0.0479875
#2       3.8514120           0.769227            1.3484398              -0.2623692
#3       6.5162726           1.574126            1.8743025               0.4537002
#4       1.3309218           1.082774            0.2858718               0.0795259
#5       0.2772234           1.630194           -1.2829316               0.4886992
#  BaselineData.B2 AlternativeData.B2 transBaselineData.B2 transAlternativeData.B2
#1       5.4656049          4.6095688            1.6984748               1.5281343
#2       1.6149559          2.0244244            0.4793077               0.7052854
#3       1.7718620          0.5504016            0.5720310              -0.5971070
#4       0.6774067          1.5434812           -0.3894834               0.4340404
#5       0.4507284          5.4987830           -0.7968903               1.7045268