# genPBONdata

0th

Percentile

##### Generates correlated data with multiple count, binary, ordinal and normal variables

This function simulates a multivariate data set that is composed of count, binary, ordinal and normal variables with specified marginals and a correlation matrix.

##### Usage
genPBONdata(n, no_pois, no_bin, no_ord, no_norm, inter.mat, lamvec, prop_vec_bin,
prop_vec_ord, nor.mean, nor.var)
##### Arguments
n
Number of rows
no_pois
Number of count variables
no_bin
Number of binary variables
no_ord
Number of ordinal variables
no_norm
Number of normal variables
inter.mat
The intermediate correlation matrix obtained from function intermat
lamvec
A vector of marginal rates for the count variables
prop_vec_bin
A vector of probabilities for the binary variables
prop_vec_ord
A vector of probabilities for the ordinal variables. For each of the variable, the i-th element of the pvec is the cumulative probability defining the marginal distribution of the ordinal variable. If the variable has k categories, the i-th element of
nor.mean
A vector of means for the normal variables
nor.var
A vector of variances for the normal variables
##### Value

• dataA simulated data matrix of size nx(no_pois + no_bin + no_ord + no_norm), of which the first no_pois are count variables, followed by no_bin binary variables, no_ord ordinal variables, and lastly no_norm normal variables.
• n.rowsNumber of rows in the simulated data
• prob.binA vector of probabilities for the binary variables
• prob.ordA vector of probabilities for the ordinal variables
• nor.meanA vector of means for the normal variables
• nor.varA vector of variances for the normal variables
• lamvecA vector of rate parameters for the count variables
• n.poisNumber of count variables
• n.binNumber of binary variables
• n.ordNumber of ordinal variables
• n.normNumber of normal variables
• final.corrThe final correlation matrix for the simulated data

• genPBONdata
##### Examples
ss=10000
num_pois<-2
num_bin<-1
num_ord<-2
num_norm<-1

lamvec=sample(10,2)
pbin=runif(1)
pord=list(c(0.1, 0.9), c(0.2, 0.3, 0.5))
nor.mean=3.1
nor.var=0.85
M=c(-0.05, 0.26, 0.14, 0.09, 0.14, 0.12, 0.13, -0.02, 0.17, 0.29,
-0.04, 0.19, 0.10, 0.35, 0.39)
N=diag(6)
N[lower.tri(N)]=M
TV=N+t(N)
diag(TV)<-1
intmat<-intermat(num_pois,num_bin,num_ord,num_norm,corr_mat=TV,pbin,pord,lamvec,
nor.mean,nor.var)

genPBONdata(ss,num_pois,num_bin,num_ord,num_norm,intmat,lamvec,pbin,pord,nor.mean,nor.var)
Documentation reproduced from package PoisBinOrdNor, version 1.1, License: GPL (>= 2)

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