singleSNPalt {ICSKAT}

singleSNPalt.R

Description

Take a matrix of SNPs and get the interval-censored regression p-value for each one separately using either survreg() or coxph() with midpoint approximation.

Usage

singleSNPalt(
  lt,
  rt,
  tpos_ind,
  obs_ind,
  xMat,
  gMat,
  coxph = TRUE,
  survreg = TRUE
)

Arguments

lt

n*1 vector of left side of time interval.

rt

n*1 vector of right side of time interval.

tpos_ind

n*1 binary vector of whether the event was observed after follow-up started (takes value 1 if t>0, 0 otherwise).

obs_ind

n*1 vector of whether the event was observed or right-censored (takes value 1 if observed or 0 if right-censored).

xMat

non-SNP covariates matrix.

gMat

n*q genotype matrix.

coxph

Boolean, whether to fit Cox PH model.

survreg

Boolean, whether to fit survreg() Wiibull model.

Value

A list with the elements:

pvalCox

q*1 vector of marginal SNP p-values with Cox model

pvalSurv

q*1 vector of marginal SNP p-values with survreg Weibull model

Examples

set.seed(2)
gMat <- matrix(data=rbinom(n=200, 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)
singleSNPalt(lt = lt, rt = rt,tpos_ind = tpos_ind, obs_ind = obs_ind, xMat = xMat, gMat = gMat)