View source: R/SansSouciStruct-class.R
fit.SansSouciStruct | R Documentation |
Fit SansSouciStruct object
## S3 method for class 'SansSouciStruct'
fit(
object,
alpha,
p.values,
family = c("DKWM", "HB", "trivial", "Simes", "Oracle"),
flavor = c("tree", "partition"),
...
)
object |
An object of class |
alpha |
Target risk (JER) level |
p.values |
A vector of length |
family |
A character value describing how the number of true nulls in a set is estimated. Can be either:
|
flavor |
A character value which can be
|
... |
Not used |
In the particular case where family=="Simes"
or family=="Oracle"
, the return value is actually of class SansSouci
and not SansSouciStruct
A 'fitted' object of class 'SansSouciStruct'. It is a list of three elements
input: see SansSouciStruct
param: the input parameters, given as a list
output: a list of two elements
p.values: the input argument 'p.values'
ZL: the output of the "zeta function" associated to the input parameter 'family', see e.g. zeta.DKWM
Durand, G., Blanchard, G., Neuvial, P., & Roquain, E. (2020). Post hoc false positive control for structured hypotheses. Scandinavian Journal of Statistics, 47(4), 1114-1148.
Dvoretzky, A., Kiefer, J., and Wolfowitz, J. (1956). Asymptotic minimax character of the sample distribution function and of the classical multinomial estimator. The Annals of Mathematical Statistics, pages 642-669.
Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6 (1979), pp. 65-70.
Massart, P. (1990). The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality. The Annals of Probability, pages 1269-1283.
zeta.DKWM zeta.HB, zeta.tricial
s <- 100
q <- 7
m <- s*2^q
obj <- SansSouciDyadic(m, leaf_size = s, direction = "top-down")
mu <- gen.mu.leaves(m = m, K1 = 8, d = 0.9, grouped = TRUE,
setting = "const", barmu = 3, leaf_list = obj$input$leaves)
pvalues <- gen.p.values(m = m, mu = mu, rho = 0)
alpha <- 0.05
S1 <- which(mu != 0)
res_DKWM <- fit(obj, alpha, pvalues, "DKWM")
predict(res_DKWM, S = S1, what = "FP")
res_Simes <- fit(obj, alpha, pvalues, "Simes")
predict(res_Simes, S = S1, what = "FP")
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