fdr-controls: External false discovery rate ('FDR') control methods
In dipterix/focr: A False Overlapped-Cluster Rate Control ('FOCR') Framework

Description Usage Arguments Value References

Procedures used by focr to control the FDR in the second stage.

Benjamini-Hochberg procedure ('BH'): doi: 10.1111/j.2517-6161.1995.tb02031.x
Benjamini-Yekutieli procedure ('BY'): https://www.jstor.org/stable/2674075
Structure-Adaptive Benjamini–Hochberg Algorithm ('SABHA'): doi: 10.1111/rssb.12298
Locally Adaptive Weighting and Screening ('LAWS'): doi: 10.1080/01621459.2020.1859379

LAWS(
  pv,
  bandwidth,
  dimension = c("one", "two", "three"),
  alpha = 0.05,
  initial_filter = 0.9,
  verbose = FALSE
)

SABHA(
  pv,
  bandwidth,
  dimension = c("one", "two", "three"),
  alpha = 0.05,
  initial_filter = 0.9,
  verbose = FALSE
)

BH(pvals, alpha = 0.05, filter = 1)

BY(pvals, alpha = 0.05, filter = 1)

`pv, pvals`	p-values
`bandwidth`	kernel smoothing bandwidth
`dimension`	the spatial dimension of underlying data. Current implementation only supports 1-3 dimensions
`alpha`	'FDR' level
`verbose`	whether to print out debug messages; default is false
`filter, initial_filter`	initial p-value filters, the goal is to remove large p-values when true signals are sparse. For `LAWS` and `SABHA`, `initial_filter` helps determine sparsity levels.

A list of rejection results:

rejs: integer indices of rejected hypotheses;
nrejs: total number of rejections;
method: characters of method name;
filter,initial_filter: passed from arguments, used to calculate "purity" values;
pis_hat: estimated sparsity level (LAWS and SABHA only);
tau: p-value cutoff value (BH and BY only);
order,qvals: other variable from BH and BY;
bandwidth,dimension,details: other variable from LAWS and SABHA.

[1] Benjamini, Y. and Hochberg, Y., 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological), 57(1), pp.289-300.

[2] Benjamini, Y. and Yekutieli, D., 2001. The control of the false discovery rate in multiple testing under dependency. Annals of statistics, pp.1165-1188.

[3] Li, A. and Barber, R.F., 2019. Multiple testing with the structure‐adaptive Benjamini–Hochberg algorithm. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 81(1), pp.45-74.

[4] Cai, T.T., Sun, W. and Xia, Y., 2020. LAWS: A Locally Adaptive Weighting and Screening Approach To Spatial Multiple Testing. Journal of the American Statistical Association, pp.1-30.

dipterix/focr documentation built on Dec. 20, 2021, 12:03 a.m.