gen.PoisBinNonNor {PoisBinNonNor}

Simulates a sample of size n from a set of multivariate Poisson, binary, and continuous data

Description

This function simulates a sample of size n from a set of multivariate Poisson, binary, and continuous data with pre-specified marginals and a correlation matrix.

Usage

gen.PoisBinNonNor(n, n.P, n.B, n.C, lambda.vec = NULL, prop.vec = NULL, 
mean.vec=NULL, variance.vec=NULL, coef.mat = NULL, final.corr.mat)

Arguments

n

Number of variates.

n.P

Number of Poisson variables.

n.B

Number of binary variables.

n.C

Number of continuous variables.

lambda.vec

Rate vector for Poisson variables.

prop.vec

Proportion vector for binary variables.

mean.vec

Mean vector of continuous variables.

variance.vec

Variance vector of continuous variables.

coef.mat

Matrix of coefficients produced from fleishman.coef.

final.corr.mat

Final correlation matrix produced from overall.corr.mat

Value

A matrix of size n*(n.P + n.B + n.C), of which the first n.P columns are Poisson variables, the next n.B columns are binary variables, and the last n.C columns are continuous variables.

Examples

## Not run: 
n=100000
n.P<-2
n.B<-2
n.C<-2
lambda.vec<-c(2,3)
prop.vec<-c(0.3,0.5)
mean.vec<-c(0,0)
variance.vec<-c(1,1)
coef.mat=matrix(rep(c(0,1,0,0), each=2),4,2,byrow=T)
corr.mat=matrix(0.4,6,6)
diag(corr.mat)=1
final.corr.mat=overall.corr.mat(n.P,n.B,n.C,lambda.vec,prop.vec, 
coef.mat,corr.vec=NULL,corr.mat)

mymixdata=gen.PoisBinNonNor(n,n.P,n.B,n.C,lambda.vec,prop.vec, 
mean.vec,variance.vec,coef.mat,final.corr.mat)

#Check marginals
#apply(mymixdata,2,mean)
#cor(mymixdata)

n=100000
n.P<-2
n.B<-2
n.N<-2
lambda.vec<-c(2,3)
prop.vec<-c(0.3,0.5)
mean.vec=c(1,0.5)
variance.vec=c(1,0.02777778)
skewness.vec=c(2,0)
kurtosis.vec=c(6,-0.5455)
coef.mat=fleishman.coef(2,skewness.vec, kurtosis.vec)
corr.mat=matrix(0.3,6,6)
diag(corr.mat)=1
final.corr.mat=overall.corr.mat(n.P,n.B,n.N,lambda.vec,prop.vec,
coef.mat,corr.vec=NULL,corr.mat)
mymixdata=gen.PoisBinNonNor(n,n.P,n.B,n.N,lambda.vec,prop.vec,
mean.vec, variance.vec,coef.mat,final.corr.mat)

#Check marginals
#apply(mymixdata,2,mean)[4:5] 
#apply(mymixdata,2,var)[4:5]  
#cor(mymixdata)

## End(Not run)