Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/weight_binary_nwt.R

This is a Newton Raphson based algorithm to compute weight from the ranks probability for the binary effect sizes.

1 | ```
weight_binary_nwt(alpha, m1, et, ranksProb, x0 = NULL)
``` |

`alpha` |
Numeric, significance level of the hypothesis test |

`m1` |
Integer, number of true alternative hypothesis |

`et` |
Numeric, mean effect size of the test statistics |

`ranksProb` |
Numeric vector of ranks probability of the tests given the effect size |

`x0` |
Numeric, a initial value for the Newton-Raphson mehod. |

If one wants to test

*H_0: epsilon_i = 0 vs. H_a: ε_i = epsilon,*

then `et`

should be median of the test effect sizes.
This is called hypothesis testing for the binary effect sizes.

For the Newton-Raphson mehthod the initial value `x0`

is very important.
One may need to supply externally if the function shows error. Newton-Raphson
method may not work in some cases. In that situations, use `weight_binary`

function, which is a general but slow approach to compute weights beased on the
grid search algorithm.

`w`

Numeric vector of weights of the tests for the binary case

`lambda`

Numeric value of the LaGrange multiplier

`n`

Integer, number of iteration needed to obtain the initial value

`k`

Integer, number of iteration needed to obtain the optimal
value of `lambda`

Mohamad S. Hasan, sha[email protected]

`prob_rank_givenEffect`

`weight_binary`

1 2 3 4 5 6 7 8 9 | ```
# compute the probabilities of the ranks of a test being rank 1 to 100 if the
# targeted test effect is 2 and the overall mean covariate effect is 1.
ranks <- 1:100
prob <- sapply(ranks, prob_rank_givenEffect, et = 2, ey = 1, nrep = 10000,
m0 = 50, m1 = 50)
# compute weight for the binary case
results = weight_binary_nwt(alpha = .05, m1 = 50, et = 2,
ranksProb = prob)
``` |

mshasan/OPWeight documentation built on Aug. 22, 2017, 4:09 p.m.

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