gen.PoisBinNonNor
(Package: PoisBinNonNor) :
Simulates a sample of size n from a set of multivariate Poisson, binary, and
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.
● Data Source:
CranContrib
● Keywords:
● Alias: gen.PoisBinNonNor
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validation.corr
(Package: PoisBinNonNor) :
Validates the specified correlation matrix
This function validates the specified correlation vector and/or matrix for appropriate dimension, symmetry, range, and positive definiteness. If both correlation matrix and correlation vector were supplied, it checks whether the matrix and vector are conformable.
● Data Source:
CranContrib
● Keywords:
● Alias: validation.corr
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intermediate.corr.PP
(Package: PoisBinNonNor) :
Computes an intermediate normal correlation matrix for Poisson variables given the specified correlation matrix
This function computes the intermediate normal correlation matrix for Poisson-Poisson combinations before inverse cdf matching as formulated in Amatya and Demirtas (2015).
● Data Source:
CranContrib
● Keywords:
● Alias: intermediate.corr.PP
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Checks whether the marginal specification of the continuous part is valid and consistent.
● Data Source:
CranContrib
● Keywords:
● Alias: validation.skewness.kurtosis
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overall.corr.mat
(Package: PoisBinNonNor) :
Computes the final intermediate correlation matrix
This function computes the final correlation matrix by combining pairwise intermediate correlation matrix entries for Poisson-Poisson, Poisson-binary, Poisson-continuous, binary-binary, binary-continuous, and continuous-continuous combinations. If the resulting correlation matrix is not positive definite, a nearest positive matrix will be used.
● Data Source:
CranContrib
● Keywords:
● Alias: overall.corr.mat
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intermediate.corr.CC
(Package: PoisBinNonNor) :
Computes an intermediate correlation matrix for continuous variables given the specified correlation
This function computes the intermediate correlation matrix for continuous-continuous combinations as formulated in Demirtas et al. (2012).
● Data Source:
CranContrib
● Keywords:
● Alias: intermediate.corr.CC
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intermediate.corr.PC
(Package: PoisBinNonNor) :
Computes the pairwise entries of the intermediate normal correlation matrix for all Poisson-continuous combinations
This function computes the pairwise entries of the intermediate normal correlation matrix for all Poisson-continuous combinations given the specified correlation matrix as formulated in Amatya and Demirtas (2015).
● Data Source:
CranContrib
● Keywords:
● Alias: intermediate.corr.PC
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PoisBinNonNor-package
(Package: PoisBinNonNor) :
Data Generation with Count, Binary and Continuous Components
Provides R functions for generation of multiple count, binary and continuous variables simultaneously given the marginal characteristics and association structure. Continuous variables can be of any nonnormal shape allowed by the Fleishman polynomials, taking the normal distribution as a special case.
● Data Source:
CranContrib
● Keywords: concurrent generation of Poisson, binary and continuous variables, generating multivariate Poisson variables, generating multivariate binary variables, generating multivariate continuous variables
● Alias: PoisBinNonNor, PoisBinNonNor-package
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validation.bin
(Package: PoisBinNonNor) :
Validates the marginal specification of the binary variables
Checks whether the marginal specification of the binary part is valid and consistent.
● Data Source:
CranContrib
● Keywords:
● Alias: validation.bin
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correlation.limits
(Package: PoisBinNonNor) :
Computes lower and upper correlation bounds for each pair of variables
This function computes lower and upper limits for pairwise correlations of Poisson-Poisson, Poisson-binary, Poisson-continuous, binary-binary, binary-continuous, and continuous-continuous combinations.
● Data Source:
CranContrib
● Keywords:
● Alias: correlation.limits
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