resampling {mbRes} | R Documentation |
Measure Statistical Uncertainty
Description
resampling
performs randomization test to calculate P-value and
S-value.
Usage
resampling(v1, v0, nrand = 1999, seed = 1)
Arguments
v1 |
a vector, biomarker values from the treatment group. |
v0 |
a vector, biomarker values from the control group. |
nrand |
an integer, the number of randomization samples. The default value is 1999. |
seed |
an integer, the seed for random number generation. Setting a seed
ensures the reproducibility of the result. See |
Value
resampling
returns a one-row data frame with 3 numerics:
delta |
the Cliff's delta of the treatment group. |
pval |
the observed P-value p under the null hypothesis. |
sval |
the S-value s calculated from P-value p. |
References
Greenland, S. (2019). Valid P-Values Behave Exactly as They
Should: Some Misleading Criticisms of P-Values and Their Resolution With
S-Values. The American Statistician, 73(sup1), 106–114.
doi:10.1080/00031305.2018.1529625.
Phipson, B., & Smyth, G. K.
(2010). Permutation P-values Should Never Be Zero: Calculating Exact
P-values When Permutations Are Randomly Drawn. Statistical Applications in
Genetics and Molecular Biology, 9(1). doi:10.2202/1544-6115.1585.
See Also
A1
.
Examples
set.seed(1)
setting <- setpop()
temp <- simul(setting$pop_mean)
resampling(subset(temp$sam, Site == "S1", Bmk1, drop = TRUE),
subset(temp$sam, Site == "S0", Bmk1, drop = TRUE))