ICSKATO {ICSKAT}

ICSKATO.R

Description

Calculate SKATO test for ICSKAT.

Usage

ICSKATO(
  rhoVec = c(0, 0.01, 0.04, 0.09, 0.25, 0.5, 1),
  icskatOut,
  useMixtureKurt = FALSE,
  liu = TRUE,
  liuIntegrate = FALSE,
  bootstrapOut = NULL,
  alwaysCentral = FALSE,
  ACAT = FALSE
)

Arguments

rhoVec

Vector of rhos to search over.

icskatOut

The output list from ICSKAT().

useMixtureKurt

Boolean for whether to use the mixture formula to estimate the kurtosis of Qrho when we have bootstrap results. Default is false, instead we just use the bootstraped kurtosis of Qrho.

liu

Boolean for whether to use Liu moment matching approximation for p-value of each Qrho (as opposed to Davies). If Davies, cannot use bootstrapped moments of Qrho.

liuIntegrate

Boolean for whether to use Liu moment matching approximation integration in SKATO p-value (as opposed to Davies).

bootstrapOut

Output list from call to ICSKATO_bootstrap().

alwaysCentral

A boolean, if TRUE, follow SKAT package practice of always setting delta=0 in chi-square moment matching.

ACAT

Uses the ACAT method to perform ICSKATO, will result in a conservative test but is much faster.

Value

A list with the elements:

pval

SKATO p-value.

correctedP

Corrected SKATO p-value, which will be the same as pval when not all Qrho values produce a p-value between 0 and 1 (e.g. sometimes it will be 0). Correction is same as SKAT package correction..

QrhoDF

Data frame containing the distribution and p-value for each Qrho.

r

The rank of the cholesky decomposition of the sig_mat returned from ICSKAT(), i.e. V^-1/2 or Z.

intDavies

Boolean denoting whether integration was with Davies (true) or Liu method (false).

err

0 is no error, 1 is early error like possibly only one eigenvalue/issue with sigmat/issue with kappaMat/issue with QrhoDF, 2 is corrected p-value (fine), 3 is integration error, 9 is no positive p-values (so SKATOp should be 0 unless burden is 1).

lambdaKurtK1

Kurtosis of kappa term minus zeta using eigenvalues, we use it to approximate the kurtosis of the entire kappa.

lambdaSigmaK1

Standard deviation of kappa term, including zeta, using eigenvalues.

lambdaMuK1

Mean of kappa term using eigenvalues.

bootKurtKappaAll

Kurtosis of entire kappa term, including zeta, using bootstrap data

bootSigmaKappaAll

Standard deviation of entire kappa term using bootstrap data.

bootMuKappaAll

Mean of entire kappa term using bootstrap data.

mixDFVec

Degrees of freedom of Qrho if useMixtureKurt is true, only here to match SKAT package, not really used.

Examples

set.seed(1)
gMat <- matrix(data=rbinom(n=2000, size=2, prob=0.3), nrow=100)
xMat <- matrix(data=rnorm(200), nrow=100)
bhFunInv <- function(x) {x}
obsTimes <- 1:5
etaVec <- rep(0, 100)
outcomeDat <- gen_IC_data(bhFunInv = bhFunInv, obsTimes = obsTimes, windowHalf = 0.1,
probMiss = 0.1, etaVec = etaVec)
lt <- outcomeDat$leftTimes
rt <- outcomeDat$rightTimes
tpos_ind <- as.numeric(lt > 0)
obs_ind <- as.numeric(rt != Inf)
dmats <- make_IC_dmat(xMat, lt, rt, obs_ind, tpos_ind)
nullFit <- ICSKAT_fit_null(init_beta = rep(0, 5), left_dmat = dmats$left_dmat,
right_dmat=dmats$right_dmat, obs_ind = obs_ind, tpos_ind = tpos_ind,
lt = lt, rt = rt)
icskatOut <- ICskat(left_dmat = dmats$left_dmat, right_dmat=dmats$right_dmat,
lt = lt, rt = rt, obs_ind = obs_ind, tpos_ind = tpos_ind, gMat = gMat,
null_beta = nullFit$beta_fit, Itt = nullFit$Itt)
ICSKATO(icskatOut = icskatOut)