simico_gen_dat: simico_gen_dat()

In SIMICO: Set-Based Inference for Multiple Interval-Censored Outcomes

simico_gen_dat()

Description

Generate multiple interval-censored data under proportional hazards model.

Usage

simico_gen_dat(bhFunInv, obsTimes = 1:3, windowHalf = 0.1,
   n, p, k, tauSq, gMatCausal, xMat, effectSizes)

Arguments

bhFunInv	The inverse of the baseline hazard function.
obsTimes	Vector of the intended observation times.
windowHalf	The amount of time before or after the intended obsTimes that a visit might take place.
n	Total number of observations.
p	Total number of covariates.
k	Total number of outcomes.
tauSq	Variance of the subject specific random effect.
gMatCausal	Matrix of subsetted genetic information for only a select causal SNPs.
xMat	Matrix of covariates.
effectSizes	Vector of genetic effect sizes. Should be entered as a vector the same length as the number of outcomes.

Value

exactTimesMat	n x k matrix containing the simulated exact times that the event occurred.
leftTimesMat	n x k matrix containing the left time interval that is observed.
rightTimesMat	n x k matrix containing the right time interval that is observed.
obsInd	n x k matrix containing a indictor for whether that time is right-censored or not.
tposInd	n x k matrix containing a indictor for whether that time is left-censored or not.
fullDat	Data in complete form to enter into SIMICO functions.

Examples

# Set number of outcomes
k = 2

# Set number of observations
n = 100

# Set number of covariates
p = 2

# Set number of SNPs
q = 50

# Set number of causal SNPs
num = 5

# Set number of quadrature nodes
d = 100

# Variance of subject-specific random effect
tauSq = 1

# Define the effect sizes
effectSizes <- c(.03, .15)

# Set MAF cutoff for causal SNPs
Causal.MAF.Cutoff = 0.1

# the baseline cumulative hazard function
bhFunInv <- function(x) {x}

set.seed(1)

# Generate covariate matrix
xMat <- cbind(rnorm(n), rbinom(n=n, size=1, prob=0.5))

# Generate genetic matrix
gMat <- matrix(data=rbinom(n=n*q, size=2, prob=0.1), nrow=n)

# Get indices to specific select causal variants
idx <- Get_CausalSNPs_bynum(gMat, num, Causal.MAF.Cutoff)

# Subset the gMat
gMatCausal <- gMat[,idx]

# Generate the multiple outcomes
exampleDat <- simico_gen_dat(bhFunInv = bhFunInv, obsTimes = 1:3,
                             windowHalf = 0.1, n, p, k, tauSq, gMatCausal,
                             xMat, effectSizes)

SIMICO documentation built on Nov. 26, 2022, 1:06 a.m.