findintercorr_cont

the method used to generate the continuous variables. "Fleishman" uses Fleishman's third-order polynomial transformation
and "Polynomial" uses Headrick's fifth-order transformation.

method

a matrix with either 2, 3, or 4 rows, each a vector of constants c0, c1, c2, c3 (if <code>method</code> = "Fleishman") or
c0, c1, c2, c3, c4, c5 (if <code>method</code> = "Polynomial"), like that returned by
<code><a rd-options="SimMultiCorrData" href="/link/find_constants?package=SimMultiCorrData&version=0.2.2&to=SimMultiCorrData" data-mini-rdoc="SimMultiCorrData::find_constants">find_constants</a></code>

constants

a matrix of target correlations among continuous variables; if <code>nrow(rho_cont) = 1</code>, it represents a pairwise
correlation; if <code>nrow(rho_cont) = 2, 3, or 4</code>, it represents a correlation matrix between two, three, or four variables

rho_cont

This function finds the roots to the equations in <code><a rd-options="SimMultiCorrData" href="/link/intercorr_fleish?package=SimMultiCorrData&version=0.2.2&to=SimMultiCorrData" data-mini-rdoc="SimMultiCorrData::intercorr_fleish">intercorr_fleish</a></code> or
 <code><a rd-options="SimMultiCorrData" href="/link/intercorr_poly?package=SimMultiCorrData&version=0.2.2&to=SimMultiCorrData" data-mini-rdoc="SimMultiCorrData::intercorr_poly">intercorr_poly</a></code> using <code><a rd-options="nleqslv" href="/link/nleqslv?package=SimMultiCorrData&version=0.2.2&to=nleqslv" data-mini-rdoc="nleqslv::nleqslv">nleqslv</a></code>. It is used in
 <code><a rd-options="SimMultiCorrData" href="/link/findintercorr?package=SimMultiCorrData&version=0.2.2&to=SimMultiCorrData" data-mini-rdoc="SimMultiCorrData::findintercorr">findintercorr</a></code> and
 <code><a rd-options="SimMultiCorrData" href="/link/findintercorr2?package=SimMultiCorrData&version=0.2.2&to=SimMultiCorrData" data-mini-rdoc="SimMultiCorrData::findintercorr2">findintercorr2</a></code> to find the intermediate correlation for standard normal random variables
 which are used in Fleishman's Third-Order (10.1007/BF02293811) or Headrick's Fifth-Order
 (10.1016/S0167-9473(02)00072-5) Polynomial Transformation. It works for two or three
 variables in the case of <code>method</code> = "Fleishman", or two, three, or four variables in the case of <code>method</code> = "Polynomial".
 Otherwise, Headrick &amp; Sawilowsky (1999, 10.1007/BF02294317) recommend using the technique of Vale &amp; Maurelli (1983,
 10.1007/BF02293687), in which
 the intermediate correlations are found pairwise and then eigen value decomposition is used on the intermediate
 correlation matrix. This function would not ordinarily be called by the user.

correlation

continuous

Fleishman

Headrick

intermediate

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.

Allison Fialkowski

SimMultiCorrData

Simulation of Correlated Data with Multiple Variable Types

findintercorr_cont function

a matrix with either 2, 3, or 4 rows, each a vector of constants c0, c1, c2, c3 (if <code>method</code> = "Fleishman") or
c0, c1, c2, c3, c4, c5 (if <code>method</code> = "Polynomial"), like that returned by
<code><a rd-options='SimMultiCorrData' href='find_constants'>find_constants</a></code>

This function finds the roots to the equations in <code><a rd-options='SimMultiCorrData' href='intercorr_fleish'>intercorr_fleish</a></code> or
 <code><a rd-options='SimMultiCorrData' href='intercorr_poly'>intercorr_poly</a></code> using <code><a rd-options='nleqslv' href='nleqslv'>nleqslv</a></code>. It is used in
 <code><a rd-options='SimMultiCorrData' href='findintercorr'>findintercorr</a></code> and
 <code><a rd-options='SimMultiCorrData' href='findintercorr2'>findintercorr2</a></code> to find the intermediate correlation for standard normal random variables
 which are used in Fleishman's Third-Order (10.1007/BF02293811) or Headrick's Fifth-Order
 (10.1016/S0167-9473(02)00072-5) Polynomial Transformation. It works for two or three
 variables in the case of <code>method</code> = "Fleishman", or two, three, or four variables in the case of <code>method</code> = "Polynomial".
 Otherwise, Headrick &amp; Sawilowsky (1999, 10.1007/BF02294317) recommend using the technique of Vale &amp; Maurelli (1983,
 10.1007/BF02293687), in which
 the intermediate correlations are found pairwise and then eigen value decomposition is used on the intermediate
 correlation matrix. This function would not ordinarily be called by the user.

findintercorr_cont: Calculate Intermediate MVN Correlation for Continuous Variables Generated by Polynomial Transformation

Description

Usage

Arguments

Value

References

See Also