SIMICO (version 0.2.0)

simico_gen_dat: 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)

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.

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.

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)