simulate_zero_inflated_nb_random_effect_data {twosigma}

Simulated zero-inflated negative binomial data with random effects

Description

Simulated zero-inflated negative binomial data with random effects

Usage

simulate_zero_inflated_nb_random_effect_data(
  ncellsper,
  X,
  Z,
  alpha,
  beta,
  phi,
  sigma.a,
  sigma.b,
  id.levels = NULL,
  sim.seed = NULL
)

Arguments

ncellsper

Vector giving the number of cells per individual. Length of the vector is taken as the number of individuals.

X

Covariate matrix (without intercept) for the (conditional) mean model.

Z

Covariate matrix (without intercept) for the zero-inflation model.

alpha

Column vector of true parameters from the zero-inflation model. Number of rows must match number of columns in Z.

beta

Column vector of true parameters from the (conditional) mean model. Number of rows must match number of columns in X.

phi

Overdispersion parameter for the negative binomial distribution (see details for more about parameterization).

sigma.a

Standard deviation for the zero-inflation model random intercept.

sigma.b

Standard deviation for the (conditional) mean random intercept.

id.levels

Individual-level IDs. If NULL set as 1,2,... up to the number of individuals.

sim.seed

Random seed to be used. If NULL one will be randomly chosen.

Value

Y Simulated counts

X Covariate matrix (without intercept) for the (conditional) mean model.

Z Covariate matrix (without intercept) for the zero-inflation model.

a Random effects for the zero-inflation model.

b Random effects for the (conditional) mean model.

alpha Column vector of true parameters from the zero-inflation model. Number of rows must match number of columns in Z.

beta Column vector of true parameters from the (conditional) mean model. Number of rows must match number of columns in X.

phi Overdispersion parameter for the negative binomial distribution (see details for more about parameterization).

sigma.a Standard deviation for the zero-inflation model random intercept.

sigma.b Standard deviation for the (conditional) mean random intercept.

nind Number of individuals.

ncellsper Vector giving the number of cells per individual.

id.levels Individual-level IDs.

Examples

# Set Parameters to Simulate Some Data

nind<-10;ncellsper<-rep(50,nind)
sigma.a<-.5;sigma.b<-.5;phi<-.1
alpha<-c(1,0,-.5,-2);beta<-c(2,0,-.1,.6)
beta2<-c(2,1,-.1,.6)
id.levels<-1:nind;nind<-length(id.levels)
id<-rep(id.levels,times=ncellsper)
sim.seed<-1234

# Simulate individual level covariates

t2d_sim<-rep(rbinom(nind,1,p=.4),times=ncellsper)
cdr_sim<-rbeta(sum(ncellsper),3,6)
age_sim<-rep(sample(c(20:60),size=nind,replace = TRUE),times=ncellsper)

# Construct design matrices

Z<-cbind(scale(t2d_sim),scale(age_sim),scale(cdr_sim))
colnames(Z)<-c("t2d_sim","age_sim","cdr_sim")
X<-cbind(scale(t2d_sim),scale(age_sim),scale(cdr_sim))
colnames(X)<-c("t2d_sim","age_sim","cdr_sim")

# Simulate Data

sim_dat<-matrix(nrow=2,ncol=sum(ncellsper))
for(i in 1:nrow(sim_dat)){
   sim_dat[i,]<-simulate_zero_inflated_nb_random_effect_data(ncellsper,X,Z,alpha,beta2
   ,phi,sigma.a,sigma.b,id.levels=NULL)$Y
}
rownames(sim_dat)<-paste("Gene",1:2)