reproducer (version 0.5.3)

simulateRandomizedDesignEffectSizes: simulateRandomizedDesignEffectSizes

Description

This simulates one of four data distributions (normal, log-normal, gamma and Laplace), and finds the values of phat and Cliffs d and their variances. It assumes equal group sizes. It returns values of the effect sizes and their variance for a simulated randomized experiment with two treatments. It returns whether or not each non-parametric effect size was significant. It also returns the parametric (standardized and unstandardized) Effect Size and the whether the t-test was significant.

Usage

simulateRandomizedDesignEffectSizes(
  mean,
  sd,
  diff,
  N,
  type = "n",
  StdAdj = 0,
  alpha = 0.05,
  AlwaysTwoSidedTests = FALSE,
  Return.Data = FALSE
)

Value

data frame incl. the non-parametric and parametric effect sizes and whether the effect sizes are significant at the specified alpha level. For log-normal data the function returns the effect sizes for the transformed data.

Arguments

mean

The mean used for one of the treatment groups (this is the rate for the gamma data)

sd

The spread used for both treatment groups. It mus be a real value greater than 0 (this is the shape for the gamma data).

diff

This is added to the parameter mean, to define the mean of the other treatment group. 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.

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.

StdAdj

this specifies the extent of variance instability to be introduced.

alpha

the level for all statistical tests (default 0.05)

AlwaysTwoSidedTests

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

Return.Data

if set to true the algorithm returns the data not the effect sizes (default FALSE).

Author

Barbara Kitchenham and Lech Madeyski

Examples

set.seed(123)
as.data.frame(
  simulateRandomizedDesignEffectSizes(
    mean = 0, sd = 1, diff = 0.8, N = 10, type = "n", StdAdj = 0))
#   phat    varphat   dfphat sigphat   d       vard sigd       cor     varcor sigCVt  t.value
# 1 0.75 0.01522222 17.46405    TRUE 0.5 0.06237576 TRUE 0.2631579 0.01754995   TRUE 2.095142
#      t.se     t.df      t.lb t.ub t.sig        ES  Variance     StdES  MedDiff
# 1 0.4457915 17.87244 0.1606665  Inf  TRUE 0.9339963 0.9936502 0.9369759 1.260127
set.seed(123)
as.data.frame(
  simulateRandomizedDesignEffectSizes(
    mean = 0, sd = 1, diff = 0.8, N = 10, type = "n", StdAdj = 0,
    AlwaysTwoSidedTests = TRUE))
#  phat    varphat   dfphat sigphat   d       vard  sigd       cor
# 1 0.75 0.01522222 17.46405   FALSE 0.5 0.06237576 FALSE 0.2631579
#      varcor sigCVt  t.value      t.se     t.df         t.lb     t.ub t.sig
# 1 0.01754995  FALSE 2.095142 0.4457915 17.87244 -0.003056196 1.871049 FALSE
#         ES  Variance     StdES  MedDiff
# 1 0.9339963 0.9936502 0.9369759 1.260127
set.seed(456)
as.data.frame(
  simulateRandomizedDesignEffectSizes(
    mean = 0, sd = 1, diff = 0.8, N = 10, type = "l", StdAdj = 0))
# phat     varphat  dfphat sigphat    d      vard sigd       cor     varcor
# 1 0.87 0.008466667 11.1111    TRUE 0.74 0.0350497 TRUE 0.3894737 0.01039674
#  sigCVt  t.value     t.se     t.df     t.lb t.ub t.sig       ES Variance
# 1   TRUE 3.599375 2.148297 9.312472 3.809448  Inf  TRUE 7.732529 23.07591
#    StdES MedDiff transttest  EStrans StdEStrans VarTrans
# 1 1.60969 7.77893   0.998772 1.731323   1.598065 1.173728

set.seed(123)
as.data.frame(
  simulateRandomizedDesignEffectSizes(
    mean = 0, sd = 1, diff = 0.8, N = 10, type = "n", StdAdj = 0,
    Return.Data = TRUE))
#   BaselineData AlternativeData
# 1   -0.69470698       1.0533185
# 2   -0.20791728       0.7714532
# 3   -1.26539635       0.7571295
# 4    2.16895597       2.1686023
# 5    1.20796200       0.5742290
# 6   -1.12310858       2.3164706
# 7   -0.40288484      -0.7487528
# 8   -0.46665535       1.3846137
# 9    0.77996512       0.9238542
# 10  -0.08336907       1.0159416