resampling: Measure Statistical Uncertainty

In mbRes: Exploration of Multiple Biomarker Responses using Effect Size

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 set.seed for more details.

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. \Sexpr[results=rd]{tools:::Rd_expr_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). \Sexpr[results=rd]{tools:::Rd_expr_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))

mbRes documentation built on March 31, 2023, 8:14 p.m.