| simulateRandomizedDesignEffectSizes {reproducer} | R Documentation |
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
)
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). |
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.
Author(s)
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