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 p will contain k-1 probabilities. The k-th element is implicitly 1.

nor.mean

A vector of means for the normal variables

nor.var

A vector of variances for the normal variables

Value

data

A 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.rows

Number of rows in the simulated data

prob.bin

A vector of probabilities for the binary variables

prob.ord

A vector of probabilities for the ordinal variables

nor.mean

A vector of means for the normal variables

nor.var

A vector of variances for the normal variables

lamvec

A vector of rate parameters for the count variables

n.pois

Number of count variables

n.bin

Number of binary variables

n.ord

Number of ordinal variables

n.norm

Number of normal variables

final.corr

The final correlation matrix for the simulated data

Aliases
  • genPBONdata
Examples
# NOT RUN {
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 1.6.1, License: GPL-2 | GPL-3

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