Simulation of Correlated Data with Multiple Variable Types
Calculate Final Correlation Matrix
Find Standardized Cumulants of Data based on Fisher's k-statistics
Find Lower Boundary of Standardized Kurtosis for Polynomial Transforma...
Find Standardized Cumulants of Data by Method of Moments
Find Theoretical Standardized Cumulants for Continuous Distributions
Calculate Theoretical Cumulative Probability for Continuous Variables
Calculate Upper Frechet-Hoeffding Correlation Bound: Negative Binomial...
Calculate Upper Frechet-Hoeffding Correlation Bound: Poisson - Normal ...
Calculate Denominator Used in Intercorrelations Involving Ordinal Vari...
Error Loop to Correct Final Correlation of Simulated Variables
Generate Variables for Error Loop
Find Power Method Transformation Constants
Calculate Intermediate MVN Correlation for Ordinal, Continuous, Poisso...
Calculate Intermediate MVN Correlation for Ordinal - Negative Binomial...
Calculate Intermediate MVN Correlation for Ordinal - Poisson Variables...
Calculate Intermediate MVN Correlation for Continuous Variables Genera...
Calculate Intermediate MVN Correlation for Continuous - Ordinal Variab...
Calculate Intermediate MVN Correlation for Continuous - Negative Binom...
Calculate Intermediate MVN Correlation for Continuous - Negative Binom...
Calculate Intermediate MVN Correlation for Continuous - Poisson Variab...
Calculate Intermediate MVN Correlation for Continuous - Poisson Variab...
Calculate Intermediate MVN Correlation for Negative Binomial Variables...
Calculate Intermediate MVN Correlation for Poisson Variables: Correlat...
Calculate Intermediate MVN Correlation for Poisson - Negative Binomial...
Calculate Intermediate MVN Correlation for Ordinal, Continuous, Poisso...
Fleishman's Third-Order Polynomial Transformation Equations
Fleishman's Third-Order Transformation Hessian Calculation for Lower B...
Fleishman's Third-Order Transformation Lagrangean Constraints for Lowe...
Fleishman's Third-Order Polynomial Transformation Intermediate Correla...
Headrick's Fifth-Order Polynomial Transformation Intermediate Correlat...
Calculate Maximum Support Value for Count Variables: Correlation Metho...
Generation of One Non-Normal Continuous Variable Using the Power Metho...
Calculate Intermediate MVN Correlation to Generate Variables Treated a...
Check Polynomial Transformation Constants for Valid Power Method PDF
Plot Theoretical Power Method Cumulative Distribution Function for Con...
Plot Theoretical Power Method Probability Density Function and Target ...
Plot Theoretical Power Method Probability Density Function and Target ...
Plot Simulated (Empirical) Cumulative Distribution Function for Contin...
Plot Simulated Data and Target External Data for Continuous or Count V...
Plot Simulated Probability Density Function and Target PDF of External...
Plot Simulated Probability Density Function and Target PDF by Distribu...
Plot Simulated Data and Target Distribution Data by Name or Function f...
Headrick's Fifth-Order Polynomial Transformation Equations
Headrick's Fifth-Order Transformation Lagrangean Constraints for Lower...
Calculate Power Method Correlation
Generation of Correlated Ordinal, Continuous, Poisson, and/or Negative...
Generation of Correlated Ordinal, Continuous, Poisson, and/or Negative...
Separate Target Correlation Matrix by Variable Type
Calculate Simulated (Empirical) Cumulative Probability
Simulation of Correlated Data with Multiple Variable Types
Calculate Theoretical Statistics for a Valid Power Method PDF
Determine Correlation Bounds for Ordinal, Continuous, Poisson, and/or ...
Determine Correlation Bounds for Ordinal, Continuous, Poisson, and/or ...
Calculate Variance of Binary or Ordinal Variable
Generate continuous (normal or non-normal), binary, ordinal, and count (Poisson or Negative Binomial) variables with a specified correlation matrix. It can also produce a single continuous variable. This package can be used to simulate data sets that mimic real-world situations (i.e. clinical or genetic data sets, plasmodes). All variables are generated from standard normal variables with an imposed intermediate correlation matrix. Continuous variables are simulated by specifying mean, variance, skewness, standardized kurtosis, and fifth and sixth standardized cumulants using either Fleishman's third-order (<DOI:10.1007/BF02293811>) or Headrick's fifth-order (<DOI:10.1016/S0167-9473(02)00072-5>) polynomial transformation. Binary and ordinal variables are simulated using a modification of the ordsample() function from 'GenOrd'. Count variables are simulated using the inverse cdf method. There are two simulation pathways which differ primarily according to the calculation of the intermediate correlation matrix. In Correlation Method 1, the intercorrelations involving count variables are determined using a simulation based, logarithmic correlation correction (adapting Yahav and Shmueli's 2012 method, <DOI:10.1002/asmb.901>). In Correlation Method 2, the count variables are treated as ordinal (adapting Barbiero and Ferrari's 2015 modification of GenOrd, <DOI:10.1002/asmb.2072>). There is an optional error loop that corrects the final correlation matrix to be within a user-specified precision value of the target matrix. The package also includes functions to calculate standardized cumulants for theoretical distributions or from real data sets, check if a target correlation matrix is within the possible correlation bounds (given the distributions of the simulated variables), summarize results (numerically or graphically), to verify valid power method pdfs, and to calculate lower standardized kurtosis bounds.