SimRepeat: Simulation of Correlated Systems of Statistical Equations...

In SimRepeat: Simulation of Correlated Systems of Equations with Multiple Variable Types

Description

Generate correlated systems of statistical equations which represent repeated measurements or clustered data. These systems contain either: a) continuous normal, non-normal, and mixture variables based on the techniques of Headrick and Beasley (2004, doi: 10.1081/SAC-120028431) or b) continuous (normal, non-normal and mixture), ordinal, and count (regular or zero-inflated, Poisson and Negative Binomial) variables based on the hierarchical linear models (HLM) approach. Headrick and Beasley's method for continuous variables calculates the beta (slope) coefficients based on the target correlations between independent variables and between outcomes and independent variables. The package provides functions to calculate the expected correlations between outcomes, between outcomes and error terms, and between outcomes and independent variables, extending Headrick and Beasley's equations to include mixture variables. These theoretical values can be compared to the simulated correlations. The HLM approach requires specification of the beta coefficients, but permits group and subject-level independent variables, interactions among independent variables, and fixed and random effects, providing more flexibility in the system of equations. Both methods permit simulation of 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 techniques extend those found in the SimMultiCorrData and SimCorrMix packages. 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). Simulation occurs at the component-level for continuous mixture distributions. These components are transformed into the desired mixture variables using random multinomial variables based on the mixing probabilities. The target correlation matrices are specified in terms of correlations with components of continuous mixture variables. Binary and ordinal variables are simulated by discretizing the normal variables at quantiles defined by the marginal distributions. Count variables are simulated using the inverse CDF method.

There are two simulation pathways for the multi-variable type systems which differ by intermediate correlations involving count variables. 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. There are three methods available for correcting non-positive definite correlation matrices. The optional error loop may be used to improve the accuracy of the final correlation matrices. The package also provides function to check parameter inputs and summarize the generated systems of equations.

Vignettes

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

1) Theory and Equations for Correlated Systems of Continuous Variables describes the system of continuous variables generated with nonnormsys and derives the equations used in calc_betas, calc_corr_y, calc_corr_ye, and calc_corr_yx.

2) Correlated Systems of Statistical Equations with Non-Mixture and Mixture Continuous Variables provides examples of using nonnormsys.

3) The Hierarchical Linear Models Approach for a System of Correlated Equations with Multiple Variable Types describes the system of ordinal, continuous, and count variables generated with corrsys and corrsys2.

4) Correlated Systems of Statistical Equations with Multiple Variable Types provides examples of using corrsys and corrsys2.

Functions

This package contains 3 simulation functions:

nonnormsys, corrsys, corrsys2

4 support functions for nonnormsys:

calc_betas, calc_corr_y, calc_corr_ye, calc_corr_yx

1 parameter check function:

checkpar

1 summary function:

summary_sys

1 correction function for non-PD correlation matrices:

adj_grad

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See Also

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

SimRepeat documentation built on May 2, 2019, 9:32 a.m.