Description Usage Arguments Details Value Author(s) References See Also Examples
This function determines confidence bounds for the number of true discoveries, the true discovery proportion and the false discovery proportion within a set of interest. The bounds are simultaneous over all sets, and remain valid under post-hoc selection.
1 2 |
G |
numeric matrix of statistics, where columns correspond to variables, and rows to data transformations (e.g. permutations). The first transformation is the identity. |
S |
vector of indices for the variables of interest (if not specified, all variables). |
alternative |
direction of the alternative hypothesis ( |
alpha |
significance level. |
truncFrom |
truncation parameter: values less extreme than |
truncTo |
truncation parameter: truncated values are set to |
nMax |
maximum number of iterations. |
Truncation parameters should be such that truncTo
is not more extreme than truncFrom
.
The significance level alpha
should be in the interval [1/B
, 1), where
B
is the number of data transformations (rows in G
).
sumStats
returns an object of class sumObj
, containing
total
: total number of variables (columns in G
)
size
: size of S
alpha
: significance level
TD
: lower (1-alpha
)-confidence bound for the number of true discoveries in S
maxTD
: maximum value of TD
that could be found under convergence of the algorithm
iterations
: number of iterations of the algorithm
Anna Vesely.
Goeman, J. J. and Solari, A. (2011). Multiple testing for exploratory research. Statistical Science, 26(4):584-597.
Hemerik, J. and Goeman, J. J. (2018). False discovery proportion estimation by permutations: confidence for significance analysis of microarrays. JRSS B, 80(1):137-155.
Vesely, A., Finos, L., and Goeman, J. J. (2020). Permutation-based true discovery guarantee by sum tests. Pre-print arXiv:2102.11759.
True discovery guarantee using p-values: sumPvals
Access a sumObj
object: discoveries
, tdp
, fdp
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | # generate matrix of t-scores for 5 variables and 10 permutations
G <- simData(prop = 0.6, m = 5, B = 10, alpha = 0.4, p = FALSE, seed = 42)
# subset of interest (variables 1 and 2)
S <- c(1,2)
# create object of class sumObj
res <- sumStats(G, S, alpha = 0.4, truncFrom = 0.7, truncTo = 0)
res
summary(res)
# lower confidence bound for the number of true discoveries in S
discoveries(res)
# lower confidence bound for the true discovery proportion in S
tdp(res)
# upper confidence bound for the false discovery proportion in S
fdp(res)
|
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