simico_gen_dat {SIMICO}

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)