lavaSearch2 (version 1.0.0)

calcDistMax: Adjust the p.values Using the Quantiles of the Max Statistic

Description

Adjust the p.values using the quantiles of the max statistic.

Usage

calcDistMaxIntegral(statistic, iid, df, iid.previous = NULL,
  quantile.previous = NULL,
  quantile.compute = lava.options()$search.calc.quantile.int, alpha,
  ncpus = 1, initCpus = TRUE, trace)
calcDistMaxBootstrap(statistic, iid, iid.previous = NULL,
  quantile.previous = NULL, method, alpha, ncpus = 1, initCpus = TRUE,
  n.sim, trace, n.repMax = 100)

Arguments

statistic

the observed statistic relative to the coefficients to test.

iid

zero-mean iid decomposition of the observed coefficients used to compute the statistic.

df

the degree of freedom for the t statistic.

iid.previous

zero-mean iid decomposition of the previous step to condition on.

quantile.previous

critical threshold of the previous step to condition on. If not NULL the values should correspond the variable in to the first column(s) of the argument iid.

quantile.compute

should the critical quantile be computed.

alpha

the significance threshold for retaining a new link

ncpus

the number of cpu to use for parellel computations

initCpus

should the cpus be initialized.

trace

should the execution of the function be traced.

method

the method used to compute the p.values. Can be "integration", "boot-wild", or "boot-norm". See the detail section.

n.sim

the total number of simulations.

n.repMax

the maximum number of rejection when using ""boot-wild" or "boot-norm".

Examples

# NOT RUN {
library(mvtnorm)

set.seed(10)
n <- 100
p <- 4
link <- letters[1:p]
n.sim <- 1e3 # number of bootstrap simulations 

#### test - not conditional ####
X.iid <- rmvnorm(n, mean = rep(0,p), sigma = diag(1,p))
colnames(X.iid) <- link
statistic <- setNames(1:p,link)


r1 <- calcDistMaxIntegral(statistic = statistic, iid = X.iid, 
            trace = FALSE, alpha = 0.05, df = 1e6) 

r2 <- calcDistMaxBootstrap(statistic = statistic, iid = X.iid,
            method = "naive",
            trace = FALSE, alpha = 0.05, n.sim = n.sim)

r3 <- calcDistMaxBootstrap(statistic = statistic, iid = X.iid,
            method = "residual",
            trace = FALSE, alpha = 0.05, n.sim = n.sim)

r4 <- calcDistMaxBootstrap(statistic = statistic, iid = X.iid,
            method = "wild",
            trace = FALSE, alpha = 0.05, initCpus = TRUE, n.sim = n.sim)

rbind(integration = c(r1$p.adjust, quantile = r1$z),
      bootNaive    = c(r2$p.adjust, quantile = r2$z),
      bootResidual = c(r3$p.adjust, quantile = r3$z),
      bootWild    = c(r4$p.adjust, quantile = r4$z))

#### test - conditional ####
# }
# NOT RUN {
Z.iid <- rmvnorm(n, mean = rep(0,p+1), sigma = diag(1,p+1))
seqQuantile <- qmvnorm(p = 0.95, delta = rep(0,p+1), sigma = diag(1,p+1), 
                    tail = "both.tails")$quantile

r1c <- calcDistMaxIntegral(statistic = statistic, iid = X.iid,
            iid.previous = Z.iid, quantile.previous =  seqQuantile, 
            trace = FALSE, alpha = 0.05, df = NULL)

r2c <- calcDistMaxBootstrap(statistic = statistic, iid = X.iid,
            iid.previous = Z.iid, quantile.previous =  seqQuantile, method = "naive",
            trace = FALSE, alpha = 0.05, n.sim = n.sim)

r3c <- calcDistMaxBootstrap(statistic = statistic, iid = X.iid,
            iid.previous = Z.iid, quantile.previous =  seqQuantile, method = "residual",
            trace = FALSE, alpha = 0.05, n.sim = n.sim)

r4c <- calcDistMaxBootstrap(statistic = statistic, iid = X.iid,
            iid.previous = Z.iid, quantile.previous =  seqQuantile, method = "wild",
            trace = FALSE, alpha = 0.05, n.sim = n.sim)

rbind(integration = c(r1c$p.adjust, quantile = r1c$z),
      bootNaive    = c(r2c$p.adjust, quantile = r2c$z),
      bootResidual = c(r3c$p.adjust, quantile = r3c$z),
      bootWild    = c(r4c$p.adjust, quantile = r4c$z))
# }
# NOT RUN {
# }