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SimMultiCorrData  

Simulation of Correlated Data with Multiple Variable Types
View on CRAN: Click here


Download and install SimMultiCorrData package within the R console
Install from CRAN:
install.packages("SimMultiCorrData")

Install from Github:
library("remotes")
install_github("cran/SimMultiCorrData")

Install by package version:
library("remotes")
install_version("SimMultiCorrData", "0.2.2")



Attach the package and use:
library("SimMultiCorrData")
Maintained by
Allison Cynthia Fialkowski
[Scholar Profile | Author Map]
First Published: 2017-06-30
Latest Update: 2018-06-28
Description:
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.
How to cite:
Allison Cynthia Fialkowski (2017). SimMultiCorrData: Simulation of Correlated Data with Multiple Variable Types. R package version 0.2.2, https://cran.r-project.org/web/packages/SimMultiCorrData. Accessed 29 Mar. 2025.
Previous versions and publish date:
0.1.0 (2017-06-30 00:44), 0.2.0 (2017-10-25 18:38), 0.2.1 (2017-11-09 19:12)
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Complete documentation for SimMultiCorrData
Functions, R codes and Examples using the SimMultiCorrData R package
Some associated functions: H_params . Headrick.dist . SimMultiCorrData . calc_final_corr . calc_fisherk . calc_lower_skurt . calc_moments . calc_theory . cdf_prob . chat_nb . chat_pois . denom_corr_cat . error_loop . error_vars . find_constants . findintercorr . findintercorr2 . findintercorr_cat_nb . findintercorr_cat_pois . findintercorr_cont . findintercorr_cont_cat . findintercorr_cont_nb . findintercorr_cont_nb2 . findintercorr_cont_pois . findintercorr_cont_pois2 . findintercorr_nb . findintercorr_pois . findintercorr_pois_nb . fleish . fleish_Hessian . fleish_skurt_check . intercorr_fleish . intercorr_poly . max_count_support . nonnormvar1 . ordnorm . pdf_check . plot_cdf . plot_pdf_ext . plot_pdf_theory . plot_sim_cdf . plot_sim_ext . plot_sim_pdf_ext . plot_sim_pdf_theory . plot_sim_theory . poly . poly_skurt_check . power_norm_corr . rcorrvar . rcorrvar2 . separate_rho . sim_cdf_prob . stats_pdf . valid_corr . valid_corr2 . var_cat . 
Some associated R codes: H_params-data.R . Headrick.dist-data.R . SimMultiCorrData.R . calc_final_corr.R . calc_fisherk.R . calc_lower_skurt.R . calc_moments.R . calc_theory.R . cdf_prob.R . chat_nb.R . chat_pois.R . denom_corr_cat.R . error_loop.R . error_vars.R . find_constants.R . findintercorr.R . findintercorr2.R . findintercorr_cat_nb.R . findintercorr_cat_pois.R . findintercorr_cont.R . findintercorr_cont_cat.R . findintercorr_cont_nb.R . findintercorr_cont_nb2.R . findintercorr_cont_pois.R . findintercorr_cont_pois2.R . findintercorr_nb.R . findintercorr_pois.R . findintercorr_pois_nb.R . fleish.R . fleish_Hessian.R . fleish_skurt_check.R . intercorr_fleish.R . intercorr_poly.R . max_count_support.R . nonnormvar1.R . ordnorm.R . pdf_check.R . plot_cdf.R . plot_pdf_ext.R . plot_pdf_theory.R . plot_sim_cdf.R . plot_sim_ext.R . plot_sim_pdf_ext.R . plot_sim_pdf_theory.R . plot_sim_theory.R . poly.R . poly_skurt_check.R . power_norm_corr.R . rcorrvar.R . rcorrvar2.R . separate_rho.R . sim_cdf_prob.R . stats_pdf.R . valid_corr.R . valid_corr2.R . var_cat.R .  Full SimMultiCorrData package functions and examples
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