resampling | R Documentation |

`resampling`

performs randomization test to calculate P-value and
S-value.

```
resampling(v1, v0, nrand = 1999, seed = 1)
```

`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 |

`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. |

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")}.

`A1`

.

```
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))
```

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