Package: SimRepeat
Type: Package
Title: Simulation of Correlated Systems of Equations with Multiple
Variable Types
Version: 0.1.0
Author: Allison Cynthia Fialkowski
Maintainer: Allison Cynthia Fialkowski <allijazz@uab.edu>
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, <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 the '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 simulated
systems of equations.
Depends: R (>= 3.4.0), SimMultiCorrData (>= 0.2.1), SimCorrMix (>=
0.1.0)
License: GPL-2
Imports: BB, nleqslv, MASS, Matrix, VGAM, triangle, ggplot2, grid,
stats, utils
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown, printr, bookdown, nlme, reshape2, testthat
VignetteBuilder: knitr
URL: https://github.com/AFialkowski/SimRepeat
NeedsCompilation: no
Packaged: 2018-04-16 11:30:02 UTC; Allison
Repository: CRAN
Date/Publication: 2018-04-16 14:09:07 UTC