SimCorrMix: Simulation of Correlated Data with Multiple Variable Types...

In SimCorrMix: Simulation of Correlated Data with Multiple Variable Types Including Continuous and Count Mixture Distributions

Description

SimCorrMix generates continuous (normal, non-normal, or mixture distributions), binary, ordinal, and count (Poisson or Negative Binomial, regular or zero-inflated) variables with a specified correlation matrix, or one continuous variable with a mixture distribution. This package can be used to simulate data sets that mimic real-world clinical or genetic data sets (i.e. plasmodes, as in Vaughan et al., 2009, doi: 10.1016/j.csda.2008.02.032). The methods extend those found in the SimMultiCorrData package. Standard normal variables with an imposed intermediate correlation matrix are transformed to generate the desired distributions. Continuous variables are simulated using either Fleishman's third-order (doi: 10.1007/BF02293811) or Headrick's fifth-order (doi: 10.1016/S0167-9473(02)00072-5) power method transformation (PMT). Non-mixture distributions require the user to specify mean, variance, skewness, standardized kurtosis, and standardized fifth and sixth cumulants. Mixture distributions require these inputs for the component distributions plus the mixing probabilities. Simulation occurs at the component-level for continuous mixture distributions. The target correlation matrix is specified in terms of correlations with components of continuous mixture variables. These components are transformed into the desired mixture variables using random multinomial variables based on the mixing probabilities. However, the package provides functions to approximate expected correlations with continuous mixture variables given target correlations with the components. Binary and ordinal variables are simulated using a modification of GenOrd-package's ordsample function. Count variables are simulated using the inverse CDF method. There are two simulation pathways which calculate intermediate correlations involving count variables differently. Correlation Method 1 adapts Yahav and Shmueli's 2012 method (doi: 10.1002/asmb.901) and performs best with large count variable means and positive correlations or small means and negative correlations. Correlation Method 2 adapts Barbiero and Ferrari's 2015 modification of GenOrd-package (doi: 10.1002/asmb.2072) and performs best under the opposite scenarios. The optional error loop may be used to improve the accuracy of the final correlation matrix. The package also provides functions to calculate the standardized cumulants of continuous mixture distributions, check parameter inputs, calculate feasible correlation boundaries, and summarize and plot simulated variables.

Vignettes

There are several vignettes which accompany this package to help the user understand the simulation and analysis methods.

1) Comparison of Correlation Methods 1 and 2 describes the two simulation pathways that can be followed for generation of correlated data.

2) Continuous Mixture Distributions demonstrates how to simulate one continuous mixture variable using contmixvar1 and gives a step-by-step guideline for comparing a simulated distribution to the target distribution.

3) Expected Cumulants and Correlations for Continuous Mixture Variables derives the equations used by the function calc_mixmoments to find the mean, standard deviation, skew, standardized kurtosis, and standardized fifth and sixth cumulants for a continuous mixture variable. The vignette also explains how the functions rho_M1M2 and rho_M1Y approximate the expected correlations with continuous mixture variables based on the target correlations with the components.

4) Overall Workflow for Generation of Correlated Data gives a step-by-step guideline to follow with an example containing continuous non-mixture and mixture, ordinal, zero-inflated Poisson, and zero-inflated Negative Binomial variables. It executes both correlated data simulation functions with and without the error loop.

5) Variable Types describes the different types of variables that can be simulated in SimCorrMix, details the algorithm involved in the optional error loop that helps to minimize correlation errors, and explains how the feasible correlation boundaries are calculated for each of the two simulation pathways.

Functions

This package contains 3 simulation functions:

contmixvar1, corrvar, and corrvar2

4 data description (summary) function:

calc_mixmoments, summary_var, rho_M1M2, rho_M1Y

2 graphing functions:

plot_simpdf_theory, plot_simtheory

3 support functions:

validpar, validcorr, validcorr2

and 16 auxiliary functions (should not normally be called by the user, but are called by other functions):

corr_error, intercorr, intercorr2, intercorr_cat_nb, intercorr_cat_pois,
intercorr_cont_nb, intercorr_cont_nb2, intercorr_cont_pois, intercorr_cont_pois2,
intercorr_cont, intercorr_nb, intercorr_pois, intercorr_pois_nb, maxcount_support, ord_norm, norm_ord

References

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See Also

Useful link: https://github.com/AFialkowski/SimMultiCorrData, https://github.com/AFialkowski/SimCorrMix

SimCorrMix documentation built on May 2, 2019, 1:24 p.m.