kernel | R Documentation |
Kernel functions transform observed p-values or their support according to
[HLR], [PB] and [HGR]. The output is used by discrete.LR
,
discrete.PB
and discrete.GR
, respectively.
For each procedure, there is a kernel for fast computation and one for
calculation of critical values. Kernels followed by ".crit", e.g.
kernel.DGR.crit
, compute and return these critical values, while
kernels ending in ".fast" only transform p-values and are therefore faster.
The end user should not use these functions directly.
kernel_DLR_fast( pCDFlist, pvalues, adaptive = TRUE, alpha = 0.05, stepUp = FALSE, zeta = 0.5, support = 0L ) kernel_DLR_crit( pCDFlist, pvalues, sorted_pv, adaptive = TRUE, alpha = 0.05, zeta = 0.5, stepUp = FALSE ) kernel_DGR_fast(pCDFlist, pvalues, adaptive = TRUE, alpha = 0.05) kernel_DGR_crit( pCDFlist, pvalues, sorted_pv, adaptive = TRUE, alpha = 0.05, zeta = 0.5 ) kernel_DPB_fast(pCDFlist, pvalues, adaptive = TRUE, alpha = 0.05, exact = TRUE) kernel_DPB_crit( pCDFlist, pvalues, sorted_pv, adaptive = TRUE, alpha = 0.05, zeta = 0.5, exact = TRUE ) kernel_wLR_fast(qvalues, weights, alpha = 0.05, geom_weighting = FALSE) kernel_wGR_fast(qvalues, weights, alpha = 0.05, geom_weighting = FALSE) kernel_wPB_fast( qvalues, weights, alpha = 0.05, geom_weighting = FALSE, exact = TRUE )
pCDFlist |
a list of the supports of the CDFs of the p-values. Each support is represented by a vector that must be in increasing order. |
pvalues |
a numeric vector. Contains all values of the p-values supports if we search for the critical constants. If not, contains only the observed p-values. Must be sorted in increasing order! |
adaptive |
a boolean specifying whether to conduct an adaptive procedure or not. |
alpha |
the target FDP, a number strictly between 0 and 1. For |
stepUp |
a numeric vector. Identical to |
zeta |
the target probability of not exceeding the desired FDP, a number strictly between 0 and 1. If |
support |
a numeric vector. Contains all values of the p-values supports. Ignored, if |
sorted_pv |
a vector of observed p-values, in increasing order. |
exact |
a boolean specifying whether to compute the Poisson-Binomial distribution exactly or by a normal approximation. |
qvalues |
a numeric vector. Contains weighted raw p-values. |
weights |
a numeric vector. Contains the weights of the p-values. |
geom_weighting |
a boolean specifying whether to conduct geometric
( |
For ".fast" kernels, a vector of transformed p-values is returned; ".crit"
kernels return a list object with critical constants ($crit.consts
)
and transformed p-values ($pval.transf
).
FDX-package
, discrete.LR
discrete.GR
, discrete.PB
,
weighted.LR
, weighted.GR
,
discrete.PB
X1 <- c(4, 2, 2, 14, 6, 9, 4, 0, 1) X2 <- c(0, 0, 1, 3, 2, 1, 2, 2, 2) N1 <- rep(148, 9) N2 <- rep(132, 9) Y1 <- N1 - X1 Y2 <- N2 - X2 df <- data.frame(X1, Y1, X2, Y2) df # Construction of the p-values and their supports (fisher.pvalues.support # is from 'DiscreteFDR' package!) df.formatted <- fisher.pvalues.support(counts = df, input = "noassoc") raw.pvalues <- df.formatted$raw pCDFlist <- df.formatted$support alpha <- 0.05 # If not searching for critical constants, we use only the observed p-values sorted.pvals <- sort(raw.pvalues) y.DLR.fast <- kernel_DLR_fast(pCDFlist, sorted.pvals, TRUE) y.NDGR.fast <- kernel_DGR_fast(pCDFlist, sorted.pvals, FALSE)$pval.transf # transformed values y.DLR.fast y.NDGR.fast # compute support pv.list <- sort(unique(unlist(pCDFlist))) y.DGR.crit <- kernel_DGR_crit(pCDFlist, pv.list, sorted.pvals, TRUE) y.NDPB.crit <- kernel_DPB_crit(pCDFlist, pv.list, sorted.pvals, FALSE) # critical constants y.DGR.crit$crit.consts y.NDPB.crit$crit.consts # transformed values y.DGR.crit$pval.transf y.NDPB.crit$pval.transf
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.