gen.mu.leaves: Generate the signal in the leaves of the tree

In pneuvial/sanssouci: Post Hoc Multiple Testing Inference

Generate the signal in the leaves of the tree

Description

Generate the signal in the leaves of the tree

Usage

gen.mu.leaves(m, K1, d, grouped, setting, barmu, leaf_list)

Arguments

m	An integer value, the number of hypotheses
K1	An integer value, the number of non-null leaves
d	A numeric value in [0,1], the proportion of non-null signals in each non-null leaf
grouped	A boolean value, whether the non-null leaves are contiguous or not
setting	A character value specifying the shape of the signal in each non-null leaf
barmu	A numeric value, the strength of the signal
leaf_list	A list of leaves as generated by dyadic.from.Nnn functions

Details

If setting == "const" the non-null signal is constant equal to barmu. If setting == "gauss" and d=1 the signal in an active leaf has the shape of a Gaussian bell with mean barmu, see the gauss_bloc function which generates it. If d<1 it has the same shape but randomly pruned. If setting == "poisson" the non-null signal is randomly drawn according to a Poisson distribution of mean barmu (slightly modified to not yield 0). If setting == "rgauss" the non-null signal is randomly drawn according to a Normal distribution of mean barmu and variance barmu.

Value

A numeric vector of length m, the signal values for each hypothesis

See Also

gauss_bloc

Examples

m <- 160
s <- 10
K1 <- floor(m/(s * 4))
d <- 1
barmu <- 4
dd <- dyadic.from.window.size(m, s, method = 2)
leaf_list <- dd$leaf_list
muC <- gen.mu.leaves(m = m, K1 = K1, d = d, grouped = FALSE, 
                    setting = "const", barmu = barmu, leaf_list =leaf_list)
muC <- gen.mu.leaves(m = m, K1 = K1, d = d, grouped = TRUE, 
                    setting = "const", barmu = barmu, leaf_list =leaf_list)
muG <- gen.mu.leaves(m = m, K1 = K1, d = d, grouped = FALSE, 
                    setting = "gauss", barmu = barmu, leaf_list =leaf_list)
muP <- gen.mu.leaves(m = m, K1 = K1, d = d, grouped = FALSE, 
                    setting = "poisson", barmu = barmu, leaf_list =leaf_list)
mu <- cbind(constant = muC, Gaussian = muG, Poisson = muP)
matplot(mu, t = 's')

pneuvial/sanssouci documentation built on Feb. 12, 2024, 4:18 a.m.