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R/rcorrvar.R
In SimMultiCorrData: Simulation of Correlated Data with Multiple Variable Types
Defines functions
rcorrvar
Documented in
rcorrvar
#' @title Generation of Correlated Ordinal, Continuous, Poisson, and/or Negative Binomial Variables: Correlation Method 1
#'
#' @description This function simulates \code{k_cat} ordinal, \code{k_cont} continuous, \code{k_pois} Poisson, and/or \code{k_nb}
#' Negative Binomial variables with
#' a specified correlation matrix \code{rho}. The variables are generated from multivariate normal variables with intermediate correlation
#' matrix \code{Sigma}, calculated by \code{\link[SimMultiCorrData]{findintercorr}}, and then transformed. The \emph{ordering} of the
#' variables in \code{rho} must be \emph{ordinal} (r >= 2 categories), \emph{continuous}, \emph{Poisson}, and \emph{Negative Binomial}
#' (note that it is possible for \code{k_cat}, \code{k_cont}, \code{k_pois}, and/or \code{k_nb} to be 0). The vignette
#' \bold{Overall Workflow for Data Simulation} provides a detailed example discussing the step-by-step simulation process and comparing
#' correlation methods 1 and 2.
#'
#' @section Variable Types and Required Inputs:
#' 1) \bold{Continuous Variables:} Continuous variables are simulated using either Fleishman's third-order (\code{method} = "Fleishman",
#' \doi{10.1007/BF02293811}) or Headrick's fifth-order (\code{method} = "Polynomial", \doi{10.1016/S0167-9473(02)00072-5}) power method
#' transformation. This is a computationally efficient algorithm that simulates continuous distributions through the method of moments.
#' It works by matching standardized cumulants -- the first four (mean, variance, skew, and standardized kurtosis) for Fleishman's method, or
#' the first six (mean, variance, skew, standardized kurtosis, and standardized fifth and sixth cumulants) for Headrick's method. The
#' transformation is expressed as follows:
#'
#' \eqn{Y = c_{0} + c_{1} * Z + c_{2} * Z^2 + c_{3} * Z^3 + c_{4} * Z^4 + c_{5} * Z^5},
#'
#' where \eqn{Z ~ N(0,1)}, and \eqn{c_{4}} and \eqn{c_{5}} both equal \eqn{0} for Fleishman's method. The real constants are calculated by
#' \code{\link[SimMultiCorrData]{find_constants}}. All variables are simulated with mean \eqn{0} and variance \eqn{1}, and then transformed
#' to the specified mean and variance at the end.
#'
#' The required parameters for simulating continuous variables include: mean, variance, skewness, standardized kurtosis (kurtosis - 3), and
#' standardized fifth and sixth cumulants (for \code{method} = "Polynomial"). If the goal is to simulate a theoretical distribution
#' (i.e. Gamma, Beta, Logistic, etc.), these values can be obtained using \code{\link[SimMultiCorrData]{calc_theory}}. If the goal is to
#' mimic an empirical data set, these values can be found using \code{\link[SimMultiCorrData]{calc_moments}} (using the method of moments) or
#' \code{\link[SimMultiCorrData]{calc_fisherk}} (using Fisher's k-statistics). If the standardized cumulants
#' are obtained from \code{calc_theory}, the user may need to use rounded values as inputs (i.e. \code{skews = round(skews, 8)}). Due to the nature
#' of the integration involved in \code{calc_theory}, the results are approximations. Greater accuracy can be achieved by increasing the number of
#' subdivisions (\code{sub}) used in the integration process. For example, in order to ensure that skew is exactly 0 for symmetric distributions.
#'
#' For some sets of cumulants, it is either not possible to find
#' power method constants or the calculated constants do not generate valid power method pdfs. In these situations, adding a value to the
#' sixth cumulant may provide solutions (see \code{\link[SimMultiCorrData]{find_constants}}). When using Headrick's fifth-order approximation,
#' if simulation results indicate that a
#' continuous variable does not generate a valid pdf, the user can try \code{\link[SimMultiCorrData]{find_constants}} with various sixth
#' cumulant correction vectors to determine if a valid pdf can be found.
#'
#' 2) \bold{Binary and Ordinal Variables:} Ordinal variables (\eqn{r \ge 2} categories) are generated by discretizing the standard normal variables
#' at quantiles. These quantiles are determined by evaluating the inverse standard normal cdf at the cumulative probabilities defined by each
#' variable's marginal distribution. The required inputs for ordinal variables are the cumulative marginal probabilities and support values
#' (if desired). The probabilities should be combined into a list of length equal to the number of ordinal variables. The \eqn{i^{th}} element
#' is a vector of the cumulative probabilities defining the marginal distribution of the \eqn{i^{th}} variable. If the variable can take
#' \eqn{r} values, the vector will contain \eqn{r - 1} probabilities (the \eqn{r^{th}} is assumed to be \eqn{1}).
#'
#' \emph{Note for binary variables:} the user-suppled probability should be the probability of the \eqn{1^{st}} (lower) support value. This would
#' ordinarily be considered the probability of \emph{failure} (\eqn{q}), while the probability of the \eqn{2^{nd}} (upper) support value would be
#' considered the probability of \emph{success} (\eqn{p = 1 - q}). The support values should be combined into a separate list. The \eqn{i^{th}}
#' element is a vector containing the \eqn{r} ordered support values.
#'
#' 3) \bold{Count Variables:} Count variables are generated using the inverse cdf method. The cumulative distribution function of a standard
#' normal variable has a uniform distribution. The appropriate quantile function \eqn{F_{Y}^{-1}} is applied to this uniform variable with the
#' designated parameters to generate the count variable: \eqn{Y = F_{y}^{-1}(\Phi(Z))}. For Poisson variables, the lambda (mean) value should be
#' given. For Negative Binomial variables, the size (target number of successes) and either the success probability or the mean should be given.
#' The Negative Binomial variable represents the number of failures which occur in a sequence of Bernoulli trials before the target number of
#' successes is achieved.
#'
#' More details regarding the variable types can be found in the \bold{Variable Types} vignette.
#'
#' @section Overview of Correlation Method 1:
#' The intermediate correlations used in correlation method 1 are more simulation based than those in method 2, which means that accuracy
#' increases with sample size and the number of repetitions. In addition, specifying the seed allows for reproducibility. In
#' addition, method 1 differs from method 2 in the following ways:
#'
#' 1) The intermediate correlation for \bold{count variables} is based on the method of Yahav & Shmueli (2012, \doi{10.1002/asmb.901}), which
#' uses a simulation based, logarithmic transformation of the target correlation. This method becomes less accurate as the variable mean
#' gets closer to zero.
#'
#' 2) The \bold{ordinal - count variable} correlations are based on an extension of the method of Amatya & Demirtas (2015,
#' \doi{10.1080/00949655.2014.953534}), in which
#' the correlation correction factor is the product of the upper Frechet-Hoeffding bound on the correlation between the count
#' variable and the normal variable used to generate it and a simulated upper bound on the correlation between an ordinal variable
#' and the normal variable used to generate it (see Demirtas & Hedeker, 2011, \doi{10.1198/tast.2011.10090}).
#'
#' 3) The \bold{continuous - count variable} correlations are based on an extension of the methods of Amatya & Demirtas (2015) and
#' Demirtas et al. (2012, \doi{10.1002/sim.5362}), in which the correlation correction factor is the product of the upper Frechet-Hoeffding bound
#' on the correlation between the count variable and the normal variable used to generate it and the power method correlation
#' between the continuous variable and the normal variable used to generate it (see Headrick & Kowalchuk, 2007, \doi{10.1080/10629360600605065}).
#' The intermediate correlations are the ratio of the target correlations to the correction factor.
#'
#' Please see the \bold{Comparison of Method 1 and Method 2} vignette for more information and an step-by-step overview of the
#' simulation process.
#'
#' @section Choice of Fleishman's third-order or Headrick's fifth-order method:
#' Using the fifth-order approximation allows additional control over the fifth and sixth moments of the generated distribution, improving
#' accuracy. In addition, the range of feasible standardized kurtosis values, given skew and standardized fifth (\eqn{\gamma_{3}}) and sixth
#' (\eqn{\gamma_{4}}) cumulants, is larger than with Fleishman's method (see \code{\link[SimMultiCorrData]{calc_lower_skurt}}).
#' For example, the Fleishman method can not be used to generate a
#' non-normal distribution with a ratio of \eqn{\gamma_{3}^2/\gamma_{4} > 9/14} (see Headrick & Kowalchuk, 2007).
#' This eliminates the
#' Chi-squared family of distributions, which has a constant ratio of \eqn{\gamma_{3}^2/\gamma_{4} = 2/3}. However, if the fifth and
#' sixth cumulants do not exist, the Fleishman approximation should be used.
#'
#' @section Reasons for Function Errors:
#' 1) The most likely cause for function errors is that no solutions to \code{\link[SimMultiCorrData]{fleish}} or
#' \code{\link[SimMultiCorrData]{poly}} converged when using \code{\link[SimMultiCorrData]{find_constants}}. If this happens,
#' the simulation will stop. It may help to first use \code{\link[SimMultiCorrData]{find_constants}} for each continuous variable to
#' determine if a vector of sixth cumulant correction values is needed. The solutions can be used as starting values (see \code{cstart} below).
#' If the standardized cumulants are obtained from \code{calc_theory}, the user may need to use rounded values as inputs (i.e.
#' \code{skews = round(skews, 8)}).
#'
#' 2) In addition, the kurtosis may be outside the region of possible values. There is an associated lower boundary for kurtosis associated
#' with a given skew (for Fleishman's method) or skew and fifth and sixth cumulants (for Headrick's method). Use
#' \code{\link[SimMultiCorrData]{calc_lower_skurt}} to determine the boundary for a given set of cumulants.
#'
#' 3) As mentioned above, the feasibility of the final correlation matrix rho, given the
#' distribution parameters, should be checked first using \code{\link[SimMultiCorrData]{valid_corr}}. This function either checks
#' if a given \code{rho} is plausible or returns the lower and upper final correlation limits. It should be noted that even if a target
#' correlation matrix is within the "plausible range," it still may not be possible to achieve the desired matrix. This happens most
#' frequently when generating ordinal variables (r >= 2 categories). The error loop frequently fixes these problems.
#'
#' @param n the sample size (i.e. the length of each simulated variable; default = 10000)
#' @param k_cont the number of continuous variables (default = 0)
#' @param k_cat the number of ordinal (r >= 2 categories) variables (default = 0)
#' @param k_pois the number of Poisson variables (default = 0)
#' @param k_nb the number of Negative Binomial variables (default = 0)
#' @param method the method used to generate the k_cont continuous variables. "Fleishman" uses Fleishman's third-order polynomial transformation
#' and "Polynomial" uses Headrick's fifth-order transformation.
#' @param means a vector of means for the k_cont continuous variables (i.e. = rep(0, k_cont))
#' @param vars a vector of variances (i.e. = rep(1, k_cont))
#' @param skews a vector of skewness values (i.e. = rep(0, k_cont))
#' @param skurts a vector of standardized kurtoses (kurtosis - 3, so that normal variables have a value of 0; i.e. = rep(0, k_cont))
#' @param fifths a vector of standardized fifth cumulants (not necessary for \code{method} = "Fleishman"; i.e. = rep(0, k_cont))
#' @param sixths a vector of standardized sixth cumulants (not necessary for \code{method} = "Fleishman"; i.e. = rep(0, k_cont))
#' @param Six a list of vectors of correction values to add to the sixth cumulants if no valid pdf constants are found,
#' ex: \code{Six = list(seq(0.01, 2,by = 0.01), seq(1, 10,by = 0.5))}; if no correction is desired for variable Y_i, set set the i-th list
#' component equal to NULL
#' @param marginal a list of length equal to \code{k_cat}; the i-th element is a vector of the cumulative
#' probabilities defining the marginal distribution of the i-th variable;
#' if the variable can take r values, the vector will contain r - 1 probabilities (the r-th is assumed to be 1; default = list());
#' for binary variables, these should be input the same as for ordinal variables with more than 2 categories (i.e. the user-specified
#' probability is the probability of the 1st category, which has the smaller support value)
#' @param support a list of length equal to \code{k_cat}; the i-th element is a vector containing the r ordered support values;
#' if not provided (i.e. \code{support = list()}), the default is for the i-th element to be the
#' vector 1, ..., r
#' @param nrand the number of random numbers to generate in calculating intermediate correlations (default = 10000)
#' @param lam a vector of lambda (> 0) constants for the Poisson variables (see \code{\link[stats]{Poisson}})
#' @param size a vector of size parameters for the Negative Binomial variables (see \code{\link[stats]{NegBinomial}})
#' @param prob a vector of success probability parameters
#' @param mu a vector of mean parameters (*Note: either \code{prob} or \code{mu} should be supplied for all Negative Binomial variables,
#' not a mixture; default = NULL)
#' @param Sigma an intermediate correlation matrix to use if the user wants to provide one (default = NULL)
#' @param rho the target correlation matrix (\emph{must be ordered ordinal, continuous, Poisson, Negative Binomial}; default = NULL)
#' @param cstart a list containing initial values for root-solving algorithm used in \code{\link[SimMultiCorrData]{find_constants}}
#' (see \code{\link[BB]{multiStart}} for \code{method} = "Fleishman" or \code{\link[nleqslv]{nleqslv}} for \code{method} = "Polynomial").
#' If user specified, each list element must be input as a matrix. If no starting values are specified for a given continuous
#' variable, that list element should be NULL. If NULL and all 4 standardized cumulants (rounded to 3 digits) are within
#' 0.01 of those in Headrick's common distribution table (see \code{\link[SimMultiCorrData]{Headrick.dist}}
#' data), uses his constants as starting values; else, generates n sets of random starting values from
#' uniform distributions.
#' @param seed the seed value for random number generation (default = 1234)
#' @param errorloop if TRUE, uses \code{\link[SimMultiCorrData]{error_loop}} to attempt to correct the final correlation
#' (default = FALSE)
#' @param epsilon the maximum acceptable error between the final and target correlation matrices (default = 0.001)
#' in the calculation of ordinal intermediate correlations with \code{\link[SimMultiCorrData]{ordnorm}} or in the error loop
#' @param maxit the maximum number of iterations to use (default = 1000) in the calculation of ordinal
#' intermediate correlations with \code{\link[SimMultiCorrData]{ordnorm}} or in the error loop
#' @param extra_correct if TRUE, within each variable pair, if the maximum correlation error is still greater than 0.1, the intermediate
#' correlation is set equal to the target correlation (with the assumption that the calculated final correlation will be
#' less than 0.1 away from the target)
#' @importFrom psych describe
#' @import stats
#' @import utils
#' @importFrom Matrix nearPD
#' @import GenOrd
#' @import BB
#' @import nleqslv
#' @export
#' @keywords simulation, continuous, ordinal, Poisson, Negative Binomial, Fleishman, Headrick, method1
#' @seealso \code{\link[SimMultiCorrData]{find_constants}}, \code{\link[SimMultiCorrData]{findintercorr}},
#' \code{\link[BB]{multiStart}}, \code{\link[nleqslv]{nleqslv}}
#' @return A list whose components vary based on the type of simulated variables. Simulated variables are returned as data.frames:
#' @return If \bold{ordinal variables} are produced:
#'
#' \code{ordinal_variables} the generated ordinal variables,
#'
#' \code{summary_ordinal} a list, where the i-th element contains a data.frame with column 1 = target cumulative probabilities and
#' column 2 = simulated cumulative probabilities for ordinal variable Y_i
#' @return If \bold{continuous variables} are produced:
#'
#' \code{constants} a data.frame of the constants,
#'
#' \code{continuous_variables} the generated continuous variables,
#'
#' \code{summary_continuous} a data.frame containing a summary of each variable,
#'
#' \code{summary_targetcont} a data.frame containing a summary of the target variables,
#'
#' \code{sixth_correction} a vector of sixth cumulant correction values,
#'
#' \code{valid.pdf} a vector where the i-th element is "TRUE" if the constants for the i-th continuous variable generate a valid pdf,
#' else "FALSE"
#' @return If \bold{Poisson variables} are produced:
#'
#' \code{Poisson_variables} the generated Poisson variables,
#'
#' \code{summary_Poisson} a data.frame containing a summary of each variable
#' @return If \bold{Negative Binomial variables} are produced:
#'
#' \code{Neg_Bin_variables} the generated Negative Binomial variables,
#'
#' \code{summary_Neg_Bin} a data.frame containing a summary of each variable
#' @return Additionally, the following elements:
#'
#' \code{correlations} the final correlation matrix,
#'
#' \code{Sigma1} the intermediate correlation before the error loop,
#'
#' \code{Sigma2} the intermediate correlation matrix after the error loop,
#'
#' \code{Constants_Time} the time in minutes required to calculate the constants,
#'
#' \code{Intercorrelation_Time} the time in minutes required to calculate the intermediate correlation matrix,
#'
#' \code{Error_Loop_Time} the time in minutes required to use the error loop,
#'
#' \code{Simulation_Time} the total simulation time in minutes,
#'
#' \code{niter} a matrix of the number of iterations used for each variable in the error loop,
#'
#' \code{maxerr} the maximum final correlation error (from the target rho).
#'
#' If a particular element is not required, the result is NULL for that element.
#' @references
#' Amatya A & Demirtas H (2015). Simultaneous generation of multivariate mixed data with Poisson and normal marginals.
#' Journal of Statistical Computation and Simulation, 85(15): 3129-39. \doi{10.1080/00949655.2014.953534}.
#'
#' Barbiero A, Ferrari PA (2015). GenOrd: Simulation of Discrete Random Variables with Given
#' Correlation Matrix and Marginal Distributions. R package version 1.4.0. \url{https://CRAN.R-project.org/package=GenOrd}
#'
#' Demirtas H & Hedeker D (2011). A practical way for computing approximate lower and upper correlation bounds.
#' American Statistician, 65(2): 104-109. \doi{10.1198/tast.2011.10090}.
#'
#' Demirtas H, Hedeker D, & Mermelstein RJ (2012). Simulation of massive public health data by power polynomials.
#' Statistics in Medicine, 31(27): 3337-3346. \doi{10.1002/sim.5362}.
#'
#' Ferrari PA, Barbiero A (2012). Simulating ordinal data. Multivariate Behavioral Research, 47(4): 566-589.
#' \doi{10.1080/00273171.2012.692630}.
#'
#' Fleishman AI (1978). A Method for Simulating Non-normal Distributions. Psychometrika, 43, 521-532. \doi{10.1007/BF02293811}.
#'
#' Frechet M. Sur les tableaux de correlation dont les marges sont donnees. Ann. l'Univ. Lyon SectA. 1951;14:53-77.
#'
#' Hasselman B (2018). nleqslv: Solve Systems of Nonlinear Equations. R package version 3.3.2.
#' \url{https://CRAN.R-project.org/package=nleqslv}
#'
#' Headrick TC (2002). Fast Fifth-order Polynomial Transforms for Generating Univariate and Multivariate
#' Non-normal Distributions. Computational Statistics & Data Analysis, 40(4):685-711. \doi{10.1016/S0167-9473(02)00072-5}.
#' (\href{http://www.sciencedirect.com/science/article/pii/S0167947302000725}{ScienceDirect})
#'
#' Headrick TC (2004). On Polynomial Transformations for Simulating Multivariate Nonnormal Distributions.
#' Journal of Modern Applied Statistical Methods, 3(1), 65-71. \doi{10.22237/jmasm/1083370080}.
#'
#' Headrick TC, Kowalchuk RK (2007). The Power Method Transformation: Its Probability Density Function, Distribution
#' Function, and Its Further Use for Fitting Data. Journal of Statistical Computation and Simulation, 77, 229-249. \doi{10.1080/10629360600605065}.
#'
#' Headrick TC, Sawilowsky SS (1999). Simulating Correlated Non-normal Distributions: Extending the Fleishman Power
#' Method. Psychometrika, 64, 25-35. \doi{10.1007/BF02294317}.
#'
#' Headrick TC, Sheng Y, & Hodis FA (2007). Numerical Computing and Graphics for the Power Method Transformation Using
#' Mathematica. Journal of Statistical Software, 19(3), 1 - 17. \doi{10.18637/jss.v019.i03}.
#'
#' Higham N (2002). Computing the nearest correlation matrix - a problem from finance; IMA Journal of Numerical Analysis 22: 329-343.
#'
#' Hoeffding W. Scale-invariant correlation theory. In: Fisher NI, Sen PK, editors. The collected works of Wassily Hoeffding.
#' New York: Springer-Verlag; 1994. p. 57-107.
#'
#' Olsson U, Drasgow F, & Dorans NJ (1982). The Polyserial Correlation Coefficient. Psychometrika, 47(3): 337-47.
#' \doi{10.1007/BF02294164}.
#'
#' Vale CD & Maurelli VA (1983). Simulating Multivariate Nonnormal Distributions. Psychometrika, 48, 465-471. \doi{10.1007/BF02293687}.
#'
#' Varadhan R, Gilbert P (2009). BB: An R Package for Solving a Large System of Nonlinear Equations and for
#' Optimizing a High-Dimensional Nonlinear Objective Function, J. Statistical Software, 32(4). \doi{10.18637/jss.v032.i04}.
#' \url{http://www.jstatsoft.org/v32/i04/}
#'
#' Yahav I & Shmueli G (2012). On Generating Multivariate Poisson Data in Management Science Applications. Applied Stochastic
#' Models in Business and Industry, 28(1): 91-102. \doi{10.1002/asmb.901}.
#'
#' @examples
#' Sim1 <- rcorrvar(n = 1000, k_cat = 1, k_cont = 1, method = "Polynomial",
#' means = 0, vars = 1, skews = 0, skurts = 0, fifths = 0, sixths = 0,
#' marginal = list(c(1/3, 2/3)), support = list(0:2),
#' rho = matrix(c(1, 0.4, 0.4, 1), 2, 2))
#'
#' \dontrun{
#'
#' # Binary, Ordinal, Continuous, Poisson, and Negative Binomial Variables
#'
#' options(scipen = 999)
#' seed <- 1234
#' n <- 10000
#'
#' Dist <- c("Logistic", "Weibull")
#' Params <- list(c(0, 1), c(3, 5))
#' Stcum1 <- calc_theory(Dist[1], Params[[1]])
#' Stcum2 <- calc_theory(Dist[2], Params[[2]])
#' Stcum <- rbind(Stcum1, Stcum2)
#' rownames(Stcum) <- Dist
#' colnames(Stcum) <- c("mean", "sd", "skew", "skurtosis", "fifth", "sixth")
#' Stcum
#' Six <- list(seq(1.7, 1.8, 0.01), seq(0.10, 0.25, 0.01))
#' marginal <- list(0.3)
#' lam <- 0.5
#' size <- 2
#' prob <- 0.75
#'
#' Rey <- matrix(0.4, 5, 5)
#' diag(Rey) <- 1
#'
#' # Make sure Rey is within upper and lower correlation limits
#' valid <- valid_corr(k_cat = 1, k_cont = 2, k_pois = 1, k_nb = 1,
#' method = "Polynomial", means = Stcum[, 1],
#' vars = Stcum[, 2]^2, skews = Stcum[, 3],
#' skurts = Stcum[, 4], fifths = Stcum[, 5],
#' sixths = Stcum[, 6], Six = Six, marginal = marginal,
#' lam = lam, size = size, prob = prob, rho = Rey,
#' seed = seed)
#'
#' # Simulate variables without error loop
#' Sim1 <- rcorrvar(n = n, k_cat = 1, k_cont = 2, k_pois = 1, k_nb = 1,
#' method = "Polynomial", means = Stcum[, 1],
#' vars = Stcum[, 2]^2, skews = Stcum[, 3],
#' skurts = Stcum[, 4], fifths = Stcum[, 5],
#' sixths = Stcum[, 6], Six = Six, marginal = marginal,
#' lam = lam, size = size, prob = prob, rho = Rey,
#' seed = seed)
#' names(Sim1)
#'
#' # Look at the maximum correlation error
#' Sim1$maxerr
#'
#' Sim1_error = round(Sim1$correlations - Rey, 6)
#'
#' # interquartile-range of correlation errors
#' quantile(as.numeric(Sim1_error), 0.25)
#' quantile(as.numeric(Sim1_error), 0.75)
#'
#' # Simulate variables with error loop
#' Sim1_EL <- rcorrvar(n = n, k_cat = 1, k_cont = 2,
#' k_pois = 1, k_nb = 1, method = "Polynomial",
#' means = Stcum[, 1], vars = Stcum[, 2]^2,
#' skews = Stcum[, 3], skurts = Stcum[, 4],
#' fifths = Stcum[, 5], sixths = Stcum[, 6],
#' Six = Six, marginal = marginal, lam = lam,
#' size = size, prob = prob, rho = Rey,
#' seed = seed, errorloop = TRUE)
#' # Look at the maximum correlation error
#' Sim1_EL$maxerr
#'
#' EL_error = round(Sim1_EL$correlations - Rey, 6)
#'
#' # interquartile-range of correlation errors
#' quantile(as.numeric(EL_error), 0.25)
#' quantile(as.numeric(EL_error), 0.75)
#'
#' # Look at results
#' # Ordinal variables
#' Sim1_EL$summary_ordinal
#'
#' # Continuous variables
#' round(Sim1_EL$constants, 6)
#' round(Sim1_EL$summary_continuous, 6)
#' round(Sim1_EL$summary_targetcont, 6)
#' Sim1_EL$valid.pdf
#'
#' # Count variables
#' Sim1_EL$summary_Poisson
#' Sim1_EL$summary_Neg_Bin
#'
#' # Generate Plots
#'
#' # Logistic (1st continuous variable)
#' # 1) Simulated Data CDF (find cumulative probability up to y = 0.5)
#' plot_sim_cdf(Sim1_EL$continuous_variables[, 1], calc_cprob = TRUE,
#' delta = 0.5)
#'
#' # 2) Simulated Data and Target Distribution PDFs
#' plot_sim_pdf_theory(Sim1_EL$continuous_variables[, 1], Dist = "Logistic",
#' params = c(0, 1))
#'
#' # 3) Simulated Data and Target Distribution
#' plot_sim_theory(Sim1_EL$continuous_variables[, 1], Dist = "Logistic",
#' params = c(0, 1))
#'
#' }
rcorrvar <- function(n = 10000, k_cont = 0, k_cat = 0, k_pois = 0, k_nb = 0,
method = c("Fleishman", "Polynomial"),
means = NULL, vars = NULL, skews = NULL,
skurts = NULL, fifths = NULL, sixths = NULL,
Six = list(), marginal = list(), support = list(),
nrand = 100000, lam = NULL,
size = NULL, prob = NULL, mu = NULL, Sigma = NULL,
rho = NULL, cstart = NULL, seed = 1234,
errorloop = FALSE, epsilon = 0.001, maxit = 1000,
extra_correct = TRUE) {
start.time <- Sys.time()
k <- k_cat + k_cont + k_pois + k_nb
if (k_cat > 0 & k_cat != length(marginal))
stop("Length of marginal does not match the number of Ordinal variables!")
if (k_cont > 0 & k_cont != length(means))
stop("Length of means does not match the number of Continuous variables!")
if (k_cont > 0 & k_cont != length(vars))
stop("Length of variances does not match the number of Continuous
variables!")
if (k_pois > 0 & k_pois != length(lam))
stop("Length of lam does not match the number of Poisson variables!")
if (k_pois > 0 & sum(lam < 0) > 0)
stop("Lambda values cannnot be negative!")
if (k_nb > 0 & k_nb != length(size))
stop("Length of size does not match the number of Negative Binomial
variables!")
if (k_nb > 0 & length(prob) > 1 & length(mu) > 1)
stop("Either give success probabilities or means for Negative Binomial
variables!")
SixCorr <- numeric(k_cont)
Valid.PDF <- numeric(k_cont)
if (ncol(rho) != k)
stop("Correlation matrix dimension does not match the number of
variables!")
if (!isSymmetric(rho) | min(eigen(rho, symmetric = TRUE)$values) < 0 |
!all(diag(rho) == 1)) stop("Correlation matrix not valid!")
start.time.constants <- Sys.time()
csame.dist <- NULL
if (k_cont > 1) {
for (i in 2:length(skews)) {
if (skews[i] %in% skews[1:(i - 1)]) {
csame <- which(skews[1:(i - 1)] == skews[i])
for (j in 1:length(csame)) {
if (method == "Polynomial") {
if ((skurts[i] == skurts[csame[j]]) &
(fifths[i] == fifths[csame[j]]) &
(sixths[i] == sixths[csame[j]])) {
csame.dist <- rbind(csame.dist, c(csame[j], i))
break
}
}
if (method == "Fleishman") {
if (skurts[i] == skurts[csame[j]]) {
csame.dist <- rbind(csame.dist, c(csame[j], i))
break
}
}
}
}
}
}
if (k_cont >= 1) {
if (method == "Fleishman") {
constants <- matrix(NA, nrow = k_cont, ncol = 4)
colnames(constants) <- c("c0", "c1", "c2", "c3")
}
if (method == "Polynomial") {
constants <- matrix(NA, nrow = k_cont, ncol = 6)
colnames(constants) <- c("c0", "c1", "c2", "c3", "c4", "c5")
}
for (i in 1:k_cont) {
if (!is.null(csame.dist)) {
rind <- which(csame.dist[, 2] == i)
if (length(rind) > 0) {
constants[i, ] <- constants[csame.dist[rind, 1], ]
SixCorr[i] <- SixCorr[csame.dist[rind, 1]]
Valid.PDF[i] <- Valid.PDF[csame.dist[rind, 1]]
}
}
if (sum(is.na(constants[i, ])) > 0) {
if (length(Six) == 0) Six2 <- NULL else
Six2 <- Six[[i]]
if (length(cstart) == 0) cstart2 <- NULL else
cstart2 <- cstart[[i]]
cons <-
suppressWarnings(find_constants(method = method, skews = skews[i],
skurts = skurts[i],
fifths = fifths[i],
sixths = sixths[i], Six = Six2,
cstart = cstart2, n = 25,
seed = seed))
if (length(cons) == 1 | is.null(cons)) {
stop(paste("Constants can not be found for continuous variable ", i,
".", sep = ""))
}
con_solution <- cons$constants
SixCorr[i] <- ifelse(is.null(cons$SixCorr1), NA, cons$SixCorr1)
Valid.PDF[i] <- cons$valid
constants[i, ] <- con_solution
}
cat("\n", "Constants: Distribution ", i, " \n")
}
}
stop.time.constants <- Sys.time()
if (k_cat > 0) {
if (!all(unlist(lapply(marginal, function(x) (sort(x) == x & min(x) > 0 &
max(x) < 1)))))
stop("Error in given marginal distributions!")
len <- length(support)
kj <- numeric(k_cat)
for (i in 1:k_cat) {
kj[i] <- length(marginal[[i]]) + 1
if (len == 0) {
support[[i]] <- 1:kj[i]
}
}
}
start.time.intercorr <- Sys.time()
if (is.null(Sigma)) {
Sigma <- findintercorr(n = n, k_cont = k_cont, k_cat = k_cat,
k_pois = k_pois, k_nb = k_nb, method = method,
constants = constants, marginal = marginal,
support = support, nrand = nrand, lam = lam,
size = size, prob = prob, mu = mu, rho = rho,
seed = seed, epsilon = epsilon, maxit = maxit)
}
if (min(eigen(Sigma, symmetric = TRUE)$values) < 0) {
warning("Intermediate correlation matrix is not positive definite.
Nearest positive definite matrix is used!")
Sigma <- as.matrix(nearPD(Sigma, corr = T, keepDiag = T)$mat)
}
if (!isSymmetric(Sigma) | min(eigen(Sigma, symmetric = TRUE)$values) < 0 |
!all(diag(Sigma) == 1))
stop("Calculated intermediate correlation matrix not valid!")
stop.time.intercorr <- Sys.time()
eig <- eigen(Sigma, symmetric = TRUE)
sqrteigval <- diag(sqrt(eig$values), nrow = nrow(Sigma), ncol = ncol(Sigma))
eigvec <- eig$vectors
fry <- eigvec %*% sqrteigval
set.seed(seed)
X <- matrix(rnorm(k * n), n)
X <- scale(X, TRUE, FALSE)
X <- X %*% svd(X, nu = 0)$v
X <- scale(X, FALSE, TRUE)
X <- fry %*% t(X)
X <- t(X)
if (k_cat > 0) {
X_cat <- matrix(X[, 1:k_cat], nrow = n, ncol = k_cat, byrow = F)
}
if (k_cont > 0) {
X_cont <- matrix(X[, (k_cat + 1):(k_cat + k_cont)], nrow = n,
ncol = k_cont, byrow = F)
}
if (k_pois > 0) {
X_pois <- matrix(X[, (k_cat + k_cont + 1):(k_cat + k_cont + k_pois)],
nrow = n, ncol = k_pois, byrow = F)
}
if (k_nb > 0) {
X_nb <- matrix(X[, (k_cat + k_cont + k_pois + 1):(k_cat + k_cont +
k_pois + k_nb)],
nrow = n, ncol = k_nb, byrow = F)
}
Y_cat <- NULL
Y <- NULL
Yb <- NULL
Y_pois <- NULL
Y_nb <- NULL
if (k_cat > 0) {
Y_cat <- matrix(1, nrow = n, ncol = k_cat)
for (i in 1:length(marginal)) {
Y_cat[, i] <- as.integer(cut(X_cat[, i], breaks = c(min(X_cat[, i]) - 1,
qnorm(marginal[[i]]), max(X_cat[, i]) + 1)))
Y_cat[, i] <- support[[i]][Y_cat[, i]]
}
}
if (k_cont > 0) {
Y <- matrix(1, nrow = n, ncol = k_cont)
Yb <- matrix(1, nrow = n, ncol = k_cont)
for (i in 1:k_cont) {
if (method == "Fleishman") {
Y[, i] <- constants[i, 1] + constants[i, 2] * X_cont[, i] +
constants[i, 3] * X_cont[, i]^2 + constants[i, 4] * X_cont[, i]^3
}
if (method == "Polynomial") {
Y[, i] <- constants[i, 1] + constants[i, 2] * X_cont[, i] +
constants[i, 3] * X_cont[, i]^2 + constants[i, 4] * X_cont[, i]^3 +
constants[i, 5] * X_cont[, i]^4 + constants[i, 6] * X_cont[, i]^5
}
Yb[, i] <- means[i] + sqrt(vars[i]) * Y[, i]
}
}
if (k_pois > 0) {
Y_pois <- matrix(1, nrow = n, ncol = k_pois)
for (i in 1:k_pois) {
Y_pois[, i] <- qpois(pnorm(X_pois[, i]), lam[i])
}
}
if (k_nb > 0) {
Y_nb <- matrix(1, nrow = n, ncol = k_nb)
if (length(prob) > 0) {
for (i in 1:k_nb) {
Y_nb[, i] <- qnbinom(pnorm(X_nb[, i]), size[i], prob[i])
}
}
if (length(mu) > 0) {
for (i in 1:k_nb) {
Y_nb[, i] <- qnbinom(pnorm(X_nb[, i]), size[i], mu = mu[i])
}
}
}
rho_calc <- calc_final_corr(k_cat = k_cat, k_cont = k_cont, k_pois = k_pois,
k_nb = k_nb, Y_cat = Y_cat, Yb = Yb,
Y_pois = Y_pois, Y_nb = Y_nb)
Sigma1 <- Sigma
start.time.error <- Sys.time()
rho0 <- rho
niter <- diag(0, k, k)
emax <- max(abs(rho_calc - rho0))
if (emax > epsilon & errorloop == TRUE) {
EL <- error_loop(k_cat = k_cat, k_cont = k_cont, k_pois = k_pois,
k_nb = k_nb, Y_cat = Y_cat, Y = Y, Yb = Yb,
Y_pois = Y_pois, Y_nb = Y_nb, marginal = marginal,
support = support, method = method, means = means,
vars = vars, constants = constants,
lam = lam, size = size, prob = prob, mu = mu,
n = n, seed = seed, epsilon = epsilon, maxit = maxit,
rho0 = rho0, Sigma = Sigma, rho_calc = rho_calc,
extra_correct = extra_correct)
Sigma <- EL$Sigma
rho_calc <- EL$rho_calc
Y_cat <- EL$Y_cat
Y <- EL$Y
Yb <- EL$Yb
Y_pois <- EL$Y_pois
Y_nb <- EL$Y_nb
niter <- EL$niter
}
stop.time.error <- Sys.time()
Sigma2 <- Sigma
emax <- max(abs(rho_calc - rho0))
niter <- as.data.frame(niter)
rownames(niter) <- c(1:k)
colnames(niter) <- c(1:k)
stop.time <- Sys.time()
Time.constants <- round(difftime(stop.time.constants, start.time.constants,
units = "min"), 3)
cat("\nConstants calculation time:", Time.constants, "minutes \n")
Time.intercorr <- round(difftime(stop.time.intercorr, start.time.intercorr,
units = "min"), 3)
cat("Intercorrelation calculation time:", Time.intercorr, "minutes \n")
Time.error <- round(difftime(stop.time.error, start.time.error,
units = "min"), 3)
cat("Error loop calculation time:", Time.error, "minutes \n")
Time <- round(difftime(stop.time, start.time, units = "min"), 3)
cat("Total Simulation time:", Time, "minutes \n")
result <- list()
if (k_cat > 0) {
summary_cat <- list()
for (i in 1:k_cat) {
summary_cat[[i]] <- as.data.frame(cbind(append(marginal[[i]], 1),
cumsum(table(Y_cat[, i]))/n))
colnames(summary_cat[[i]]) <- c("Target", "Simulated")
}
result <- append(result, list(ordinal_variables = as.data.frame(Y_cat),
summary_ordinal = summary_cat))
}
if (k_cont > 0) {
cont_sum <- describe(Yb, type = 1)
sim_fifths <- rep(NA, k_cont)
sim_sixths <- rep(NA, k_cont)
for (i in 1:k_cont) {
sim_fifths[i] <- calc_moments(Yb[, i])[5]
sim_sixths[i] <- calc_moments(Yb[, i])[6]
}
cont_sum <- as.data.frame(cbind(c(1:k_cont),
cont_sum[, -c(1, 6, 7, 10, 13)],
sim_fifths, sim_sixths))
colnames(cont_sum) <- c("Distribution", "n", "mean", "sd", "median",
"min", "max", "skew", "skurtosis", "fifth",
"sixth")
if (method == "Fleishman") {
target_sum <- as.data.frame(cbind(c(1:k_cont), means, sqrt(vars), skews,
skurts))
colnames(target_sum) <- c("Distribution", "mean", "sd", "skew",
"skurtosis")
} else {
target_sum <- as.data.frame(cbind(c(1:k_cont), means, sqrt(vars), skews,
skurts, fifths, sixths))
colnames(target_sum) <- c("Distribution", "mean", "sd", "skew",
"skurtosis", "fifth", "sixth")
}
result <- append(result, list(constants = as.data.frame(constants),
continuous_variables = as.data.frame(Yb),
summary_continuous = cont_sum,
summary_targetcont = target_sum,
sixth_correction = SixCorr,
valid.pdf = Valid.PDF))
}
if (k_pois > 0) {
summary_pois <- describe(Y_pois, type = 1)
summary_pois <- as.data.frame(cbind(summary_pois$vars, summary_pois$n,
summary_pois$mean, lam,
(summary_pois[, 4])^2, lam,
summary_pois$median, summary_pois$min,
summary_pois$max, summary_pois$skew,
summary_pois$kurtosis))
colnames(summary_pois) <- c("Distribution", "n", "mean", "Exp_mean",
"var", "Exp_var", "median", "min", "max",
"skew", "skurtosis")
result <- append(result, list(Poisson_variables = as.data.frame(Y_pois),
summary_Poisson = summary_pois))
}
if (k_nb > 0) {
summary_nb <- describe(Y_nb, type = 1)
if (length(prob) > 0) {
mu <- size * (1 - prob)/prob
}
if (length(mu) > 0) {
prob <- size/(mu + size)
}
summary_nb <- as.data.frame(cbind(summary_nb$vars, summary_nb$n, prob,
summary_nb$mean, mu,
(summary_nb[, 4])^2,
size * (1 - prob)/prob^2,
summary_nb$median, summary_nb$min,
summary_nb$max,
summary_nb$skew, summary_nb$kurtosis))
colnames(summary_nb) <- c("Distribution", "n", "success_prob", "mean",
"Exp_mean", "var", "Exp_var", "median",
"min", "max", "skew", "skurtosis")
result <- append(result, list(Neg_Bin_variables = as.data.frame(Y_nb),
summary_Neg_Bin = summary_nb))
}
result <- append(result, list(correlations = rho_calc, Sigma1 = Sigma1,
Sigma2 = Sigma2,
Constants_Time = Time.constants,
Intercorrelation_Time = Time.intercorr,
Error_Loop_Time = Time.error,
Simulation_Time = Time,
niter = niter, maxerr = emax))
result
}
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Defines functions rcorrvar
Documented in rcorrvar
#' @title Generation of Correlated Ordinal, Continuous, Poisson, and/or Negative Binomial Variables: Correlation Method 1
#'
#' @description This function simulates \code{k_cat} ordinal, \code{k_cont} continuous, \code{k_pois} Poisson, and/or \code{k_nb}
#' Negative Binomial variables with
#' a specified correlation matrix \code{rho}. The variables are generated from multivariate normal variables with intermediate correlation
#' matrix \code{Sigma}, calculated by \code{\link[SimMultiCorrData]{findintercorr}}, and then transformed. The \emph{ordering} of the
#' variables in \code{rho} must be \emph{ordinal} (r >= 2 categories), \emph{continuous}, \emph{Poisson}, and \emph{Negative Binomial}
#' (note that it is possible for \code{k_cat}, \code{k_cont}, \code{k_pois}, and/or \code{k_nb} to be 0). The vignette
#' \bold{Overall Workflow for Data Simulation} provides a detailed example discussing the step-by-step simulation process and comparing
#' correlation methods 1 and 2.
#'
#' @section Variable Types and Required Inputs:
#' 1) \bold{Continuous Variables:} Continuous variables are simulated using either Fleishman's third-order (\code{method} = "Fleishman",
#' \doi{10.1007/BF02293811}) or Headrick's fifth-order (\code{method} = "Polynomial", \doi{10.1016/S0167-9473(02)00072-5}) power method
#' transformation. This is a computationally efficient algorithm that simulates continuous distributions through the method of moments.
#' It works by matching standardized cumulants -- the first four (mean, variance, skew, and standardized kurtosis) for Fleishman's method, or
#' the first six (mean, variance, skew, standardized kurtosis, and standardized fifth and sixth cumulants) for Headrick's method. The
#' transformation is expressed as follows:
#'
#' \eqn{Y = c_{0} + c_{1} * Z + c_{2} * Z^2 + c_{3} * Z^3 + c_{4} * Z^4 + c_{5} * Z^5},
#'
#' where \eqn{Z ~ N(0,1)}, and \eqn{c_{4}} and \eqn{c_{5}} both equal \eqn{0} for Fleishman's method. The real constants are calculated by
#' \code{\link[SimMultiCorrData]{find_constants}}. All variables are simulated with mean \eqn{0} and variance \eqn{1}, and then transformed
#' to the specified mean and variance at the end.
#'
#' The required parameters for simulating continuous variables include: mean, variance, skewness, standardized kurtosis (kurtosis - 3), and
#' standardized fifth and sixth cumulants (for \code{method} = "Polynomial"). If the goal is to simulate a theoretical distribution
#' (i.e. Gamma, Beta, Logistic, etc.), these values can be obtained using \code{\link[SimMultiCorrData]{calc_theory}}. If the goal is to
#' mimic an empirical data set, these values can be found using \code{\link[SimMultiCorrData]{calc_moments}} (using the method of moments) or
#' \code{\link[SimMultiCorrData]{calc_fisherk}} (using Fisher's k-statistics). If the standardized cumulants
#' are obtained from \code{calc_theory}, the user may need to use rounded values as inputs (i.e. \code{skews = round(skews, 8)}). Due to the nature
#' of the integration involved in \code{calc_theory}, the results are approximations. Greater accuracy can be achieved by increasing the number of
#' subdivisions (\code{sub}) used in the integration process. For example, in order to ensure that skew is exactly 0 for symmetric distributions.
#'
#' For some sets of cumulants, it is either not possible to find
#' power method constants or the calculated constants do not generate valid power method pdfs. In these situations, adding a value to the
#' sixth cumulant may provide solutions (see \code{\link[SimMultiCorrData]{find_constants}}). When using Headrick's fifth-order approximation,
#' if simulation results indicate that a
#' continuous variable does not generate a valid pdf, the user can try \code{\link[SimMultiCorrData]{find_constants}} with various sixth
#' cumulant correction vectors to determine if a valid pdf can be found.
#'
#' 2) \bold{Binary and Ordinal Variables:} Ordinal variables (\eqn{r \ge 2} categories) are generated by discretizing the standard normal variables
#' at quantiles. These quantiles are determined by evaluating the inverse standard normal cdf at the cumulative probabilities defined by each
#' variable's marginal distribution. The required inputs for ordinal variables are the cumulative marginal probabilities and support values
#' (if desired). The probabilities should be combined into a list of length equal to the number of ordinal variables. The \eqn{i^{th}} element
#' is a vector of the cumulative probabilities defining the marginal distribution of the \eqn{i^{th}} variable. If the variable can take
#' \eqn{r} values, the vector will contain \eqn{r - 1} probabilities (the \eqn{r^{th}} is assumed to be \eqn{1}).
#'
#' \emph{Note for binary variables:} the user-suppled probability should be the probability of the \eqn{1^{st}} (lower) support value. This would
#' ordinarily be considered the probability of \emph{failure} (\eqn{q}), while the probability of the \eqn{2^{nd}} (upper) support value would be
#' considered the probability of \emph{success} (\eqn{p = 1 - q}). The support values should be combined into a separate list. The \eqn{i^{th}}
#' element is a vector containing the \eqn{r} ordered support values.
#'
#' 3) \bold{Count Variables:} Count variables are generated using the inverse cdf method. The cumulative distribution function of a standard
#' normal variable has a uniform distribution. The appropriate quantile function \eqn{F_{Y}^{-1}} is applied to this uniform variable with the
#' designated parameters to generate the count variable: \eqn{Y = F_{y}^{-1}(\Phi(Z))}. For Poisson variables, the lambda (mean) value should be
#' given. For Negative Binomial variables, the size (target number of successes) and either the success probability or the mean should be given.
#' The Negative Binomial variable represents the number of failures which occur in a sequence of Bernoulli trials before the target number of
#' successes is achieved.
#'
#' More details regarding the variable types can be found in the \bold{Variable Types} vignette.
#'
#' @section Overview of Correlation Method 1:
#' The intermediate correlations used in correlation method 1 are more simulation based than those in method 2, which means that accuracy
#' increases with sample size and the number of repetitions. In addition, specifying the seed allows for reproducibility. In
#' addition, method 1 differs from method 2 in the following ways:
#'
#' 1) The intermediate correlation for \bold{count variables} is based on the method of Yahav & Shmueli (2012, \doi{10.1002/asmb.901}), which
#' uses a simulation based, logarithmic transformation of the target correlation. This method becomes less accurate as the variable mean
#' gets closer to zero.
#'
#' 2) The \bold{ordinal - count variable} correlations are based on an extension of the method of Amatya & Demirtas (2015,
#' \doi{10.1080/00949655.2014.953534}), in which
#' the correlation correction factor is the product of the upper Frechet-Hoeffding bound on the correlation between the count
#' variable and the normal variable used to generate it and a simulated upper bound on the correlation between an ordinal variable
#' and the normal variable used to generate it (see Demirtas & Hedeker, 2011, \doi{10.1198/tast.2011.10090}).
#'
#' 3) The \bold{continuous - count variable} correlations are based on an extension of the methods of Amatya & Demirtas (2015) and
#' Demirtas et al. (2012, \doi{10.1002/sim.5362}), in which the correlation correction factor is the product of the upper Frechet-Hoeffding bound
#' on the correlation between the count variable and the normal variable used to generate it and the power method correlation
#' between the continuous variable and the normal variable used to generate it (see Headrick & Kowalchuk, 2007, \doi{10.1080/10629360600605065}).
#' The intermediate correlations are the ratio of the target correlations to the correction factor.
#'
#' Please see the \bold{Comparison of Method 1 and Method 2} vignette for more information and an step-by-step overview of the
#' simulation process.
#'
#' @section Choice of Fleishman's third-order or Headrick's fifth-order method:
#' Using the fifth-order approximation allows additional control over the fifth and sixth moments of the generated distribution, improving
#' accuracy. In addition, the range of feasible standardized kurtosis values, given skew and standardized fifth (\eqn{\gamma_{3}}) and sixth
#' (\eqn{\gamma_{4}}) cumulants, is larger than with Fleishman's method (see \code{\link[SimMultiCorrData]{calc_lower_skurt}}).
#' For example, the Fleishman method can not be used to generate a
#' non-normal distribution with a ratio of \eqn{\gamma_{3}^2/\gamma_{4} > 9/14} (see Headrick & Kowalchuk, 2007).
#' This eliminates the
#' Chi-squared family of distributions, which has a constant ratio of \eqn{\gamma_{3}^2/\gamma_{4} = 2/3}. However, if the fifth and
#' sixth cumulants do not exist, the Fleishman approximation should be used.
#'
#' @section Reasons for Function Errors:
#' 1) The most likely cause for function errors is that no solutions to \code{\link[SimMultiCorrData]{fleish}} or
#' \code{\link[SimMultiCorrData]{poly}} converged when using \code{\link[SimMultiCorrData]{find_constants}}. If this happens,
#' the simulation will stop. It may help to first use \code{\link[SimMultiCorrData]{find_constants}} for each continuous variable to
#' determine if a vector of sixth cumulant correction values is needed. The solutions can be used as starting values (see \code{cstart} below).
#' If the standardized cumulants are obtained from \code{calc_theory}, the user may need to use rounded values as inputs (i.e.
#' \code{skews = round(skews, 8)}).
#'
#' 2) In addition, the kurtosis may be outside the region of possible values. There is an associated lower boundary for kurtosis associated
#' with a given skew (for Fleishman's method) or skew and fifth and sixth cumulants (for Headrick's method). Use
#' \code{\link[SimMultiCorrData]{calc_lower_skurt}} to determine the boundary for a given set of cumulants.
#'
#' 3) As mentioned above, the feasibility of the final correlation matrix rho, given the
#' distribution parameters, should be checked first using \code{\link[SimMultiCorrData]{valid_corr}}. This function either checks
#' if a given \code{rho} is plausible or returns the lower and upper final correlation limits. It should be noted that even if a target
#' correlation matrix is within the "plausible range," it still may not be possible to achieve the desired matrix. This happens most
#' frequently when generating ordinal variables (r >= 2 categories). The error loop frequently fixes these problems.
#'
#' @param n the sample size (i.e. the length of each simulated variable; default = 10000)
#' @param k_cont the number of continuous variables (default = 0)
#' @param k_cat the number of ordinal (r >= 2 categories) variables (default = 0)
#' @param k_pois the number of Poisson variables (default = 0)
#' @param k_nb the number of Negative Binomial variables (default = 0)
#' @param method the method used to generate the k_cont continuous variables. "Fleishman" uses Fleishman's third-order polynomial transformation
#' and "Polynomial" uses Headrick's fifth-order transformation.
#' @param means a vector of means for the k_cont continuous variables (i.e. = rep(0, k_cont))
#' @param vars a vector of variances (i.e. = rep(1, k_cont))
#' @param skews a vector of skewness values (i.e. = rep(0, k_cont))
#' @param skurts a vector of standardized kurtoses (kurtosis - 3, so that normal variables have a value of 0; i.e. = rep(0, k_cont))
#' @param fifths a vector of standardized fifth cumulants (not necessary for \code{method} = "Fleishman"; i.e. = rep(0, k_cont))
#' @param sixths a vector of standardized sixth cumulants (not necessary for \code{method} = "Fleishman"; i.e. = rep(0, k_cont))
#' @param Six a list of vectors of correction values to add to the sixth cumulants if no valid pdf constants are found,
#' ex: \code{Six = list(seq(0.01, 2,by = 0.01), seq(1, 10,by = 0.5))}; if no correction is desired for variable Y_i, set set the i-th list
#' component equal to NULL
#' @param marginal a list of length equal to \code{k_cat}; the i-th element is a vector of the cumulative
#' probabilities defining the marginal distribution of the i-th variable;
#' if the variable can take r values, the vector will contain r - 1 probabilities (the r-th is assumed to be 1; default = list());
#' for binary variables, these should be input the same as for ordinal variables with more than 2 categories (i.e. the user-specified
#' probability is the probability of the 1st category, which has the smaller support value)
#' @param support a list of length equal to \code{k_cat}; the i-th element is a vector containing the r ordered support values;
#' if not provided (i.e. \code{support = list()}), the default is for the i-th element to be the
#' vector 1, ..., r
#' @param nrand the number of random numbers to generate in calculating intermediate correlations (default = 10000)
#' @param lam a vector of lambda (> 0) constants for the Poisson variables (see \code{\link[stats]{Poisson}})
#' @param size a vector of size parameters for the Negative Binomial variables (see \code{\link[stats]{NegBinomial}})
#' @param prob a vector of success probability parameters
#' @param mu a vector of mean parameters (*Note: either \code{prob} or \code{mu} should be supplied for all Negative Binomial variables,
#' not a mixture; default = NULL)
#' @param Sigma an intermediate correlation matrix to use if the user wants to provide one (default = NULL)
#' @param rho the target correlation matrix (\emph{must be ordered ordinal, continuous, Poisson, Negative Binomial}; default = NULL)
#' @param cstart a list containing initial values for root-solving algorithm used in \code{\link[SimMultiCorrData]{find_constants}}
#' (see \code{\link[BB]{multiStart}} for \code{method} = "Fleishman" or \code{\link[nleqslv]{nleqslv}} for \code{method} = "Polynomial").
#' If user specified, each list element must be input as a matrix. If no starting values are specified for a given continuous
#' variable, that list element should be NULL. If NULL and all 4 standardized cumulants (rounded to 3 digits) are within
#' 0.01 of those in Headrick's common distribution table (see \code{\link[SimMultiCorrData]{Headrick.dist}}
#' data), uses his constants as starting values; else, generates n sets of random starting values from
#' uniform distributions.
#' @param seed the seed value for random number generation (default = 1234)
#' @param errorloop if TRUE, uses \code{\link[SimMultiCorrData]{error_loop}} to attempt to correct the final correlation
#' (default = FALSE)
#' @param epsilon the maximum acceptable error between the final and target correlation matrices (default = 0.001)
#' in the calculation of ordinal intermediate correlations with \code{\link[SimMultiCorrData]{ordnorm}} or in the error loop
#' @param maxit the maximum number of iterations to use (default = 1000) in the calculation of ordinal
#' intermediate correlations with \code{\link[SimMultiCorrData]{ordnorm}} or in the error loop
#' @param extra_correct if TRUE, within each variable pair, if the maximum correlation error is still greater than 0.1, the intermediate
#' correlation is set equal to the target correlation (with the assumption that the calculated final correlation will be
#' less than 0.1 away from the target)
#' @importFrom psych describe
#' @import stats
#' @import utils
#' @importFrom Matrix nearPD
#' @import GenOrd
#' @import BB
#' @import nleqslv
#' @export
#' @keywords simulation, continuous, ordinal, Poisson, Negative Binomial, Fleishman, Headrick, method1
#' @seealso \code{\link[SimMultiCorrData]{find_constants}}, \code{\link[SimMultiCorrData]{findintercorr}},
#' \code{\link[BB]{multiStart}}, \code{\link[nleqslv]{nleqslv}}
#' @return A list whose components vary based on the type of simulated variables. Simulated variables are returned as data.frames:
#' @return If \bold{ordinal variables} are produced:
#'
#' \code{ordinal_variables} the generated ordinal variables,
#'
#' \code{summary_ordinal} a list, where the i-th element contains a data.frame with column 1 = target cumulative probabilities and
#' column 2 = simulated cumulative probabilities for ordinal variable Y_i
#' @return If \bold{continuous variables} are produced:
#'
#' \code{constants} a data.frame of the constants,
#'
#' \code{continuous_variables} the generated continuous variables,
#'
#' \code{summary_continuous} a data.frame containing a summary of each variable,
#'
#' \code{summary_targetcont} a data.frame containing a summary of the target variables,
#'
#' \code{sixth_correction} a vector of sixth cumulant correction values,
#'
#' \code{valid.pdf} a vector where the i-th element is "TRUE" if the constants for the i-th continuous variable generate a valid pdf,
#' else "FALSE"
#' @return If \bold{Poisson variables} are produced:
#'
#' \code{Poisson_variables} the generated Poisson variables,
#'
#' \code{summary_Poisson} a data.frame containing a summary of each variable
#' @return If \bold{Negative Binomial variables} are produced:
#'
#' \code{Neg_Bin_variables} the generated Negative Binomial variables,
#'
#' \code{summary_Neg_Bin} a data.frame containing a summary of each variable
#' @return Additionally, the following elements:
#'
#' \code{correlations} the final correlation matrix,
#'
#' \code{Sigma1} the intermediate correlation before the error loop,
#'
#' \code{Sigma2} the intermediate correlation matrix after the error loop,
#'
#' \code{Constants_Time} the time in minutes required to calculate the constants,
#'
#' \code{Intercorrelation_Time} the time in minutes required to calculate the intermediate correlation matrix,
#'
#' \code{Error_Loop_Time} the time in minutes required to use the error loop,
#'
#' \code{Simulation_Time} the total simulation time in minutes,
#'
#' \code{niter} a matrix of the number of iterations used for each variable in the error loop,
#'
#' \code{maxerr} the maximum final correlation error (from the target rho).
#'
#' If a particular element is not required, the result is NULL for that element.
#' @references
#' Amatya A & Demirtas H (2015). Simultaneous generation of multivariate mixed data with Poisson and normal marginals.
#' Journal of Statistical Computation and Simulation, 85(15): 3129-39. \doi{10.1080/00949655.2014.953534}.
#'
#' Barbiero A, Ferrari PA (2015). GenOrd: Simulation of Discrete Random Variables with Given
#' Correlation Matrix and Marginal Distributions. R package version 1.4.0. \url{https://CRAN.R-project.org/package=GenOrd}
#'
#' Demirtas H & Hedeker D (2011). A practical way for computing approximate lower and upper correlation bounds.
#' American Statistician, 65(2): 104-109. \doi{10.1198/tast.2011.10090}.
#'
#' Demirtas H, Hedeker D, & Mermelstein RJ (2012). Simulation of massive public health data by power polynomials.
#' Statistics in Medicine, 31(27): 3337-3346. \doi{10.1002/sim.5362}.
#'
#' Ferrari PA, Barbiero A (2012). Simulating ordinal data. Multivariate Behavioral Research, 47(4): 566-589.
#' \doi{10.1080/00273171.2012.692630}.
#'
#' Fleishman AI (1978). A Method for Simulating Non-normal Distributions. Psychometrika, 43, 521-532. \doi{10.1007/BF02293811}.
#'
#' Frechet M. Sur les tableaux de correlation dont les marges sont donnees. Ann. l'Univ. Lyon SectA. 1951;14:53-77.
#'
#' Hasselman B (2018). nleqslv: Solve Systems of Nonlinear Equations. R package version 3.3.2.
#' \url{https://CRAN.R-project.org/package=nleqslv}
#'
#' Headrick TC (2002). Fast Fifth-order Polynomial Transforms for Generating Univariate and Multivariate
#' Non-normal Distributions. Computational Statistics & Data Analysis, 40(4):685-711. \doi{10.1016/S0167-9473(02)00072-5}.
#' (\href{http://www.sciencedirect.com/science/article/pii/S0167947302000725}{ScienceDirect})
#'
#' Headrick TC (2004). On Polynomial Transformations for Simulating Multivariate Nonnormal Distributions.
#' Journal of Modern Applied Statistical Methods, 3(1), 65-71. \doi{10.22237/jmasm/1083370080}.
#'
#' Headrick TC, Kowalchuk RK (2007). The Power Method Transformation: Its Probability Density Function, Distribution
#' Function, and Its Further Use for Fitting Data. Journal of Statistical Computation and Simulation, 77, 229-249. \doi{10.1080/10629360600605065}.
#'
#' Headrick TC, Sawilowsky SS (1999). Simulating Correlated Non-normal Distributions: Extending the Fleishman Power
#' Method. Psychometrika, 64, 25-35. \doi{10.1007/BF02294317}.
#'
#' Headrick TC, Sheng Y, & Hodis FA (2007). Numerical Computing and Graphics for the Power Method Transformation Using
#' Mathematica. Journal of Statistical Software, 19(3), 1 - 17. \doi{10.18637/jss.v019.i03}.
#'
#' Higham N (2002). Computing the nearest correlation matrix - a problem from finance; IMA Journal of Numerical Analysis 22: 329-343.
#'
#' Hoeffding W. Scale-invariant correlation theory. In: Fisher NI, Sen PK, editors. The collected works of Wassily Hoeffding.
#' New York: Springer-Verlag; 1994. p. 57-107.
#'
#' Olsson U, Drasgow F, & Dorans NJ (1982). The Polyserial Correlation Coefficient. Psychometrika, 47(3): 337-47.
#' \doi{10.1007/BF02294164}.
#'
#' Vale CD & Maurelli VA (1983). Simulating Multivariate Nonnormal Distributions. Psychometrika, 48, 465-471. \doi{10.1007/BF02293687}.
#'
#' Varadhan R, Gilbert P (2009). BB: An R Package for Solving a Large System of Nonlinear Equations and for
#' Optimizing a High-Dimensional Nonlinear Objective Function, J. Statistical Software, 32(4). \doi{10.18637/jss.v032.i04}.
#' \url{http://www.jstatsoft.org/v32/i04/}
#'
#' Yahav I & Shmueli G (2012). On Generating Multivariate Poisson Data in Management Science Applications. Applied Stochastic
#' Models in Business and Industry, 28(1): 91-102. \doi{10.1002/asmb.901}.
#'
#' @examples
#' Sim1 <- rcorrvar(n = 1000, k_cat = 1, k_cont = 1, method = "Polynomial",
#' means = 0, vars = 1, skews = 0, skurts = 0, fifths = 0, sixths = 0,
#' marginal = list(c(1/3, 2/3)), support = list(0:2),
#' rho = matrix(c(1, 0.4, 0.4, 1), 2, 2))
#'
#' \dontrun{
#'
#' # Binary, Ordinal, Continuous, Poisson, and Negative Binomial Variables
#'
#' options(scipen = 999)
#' seed <- 1234
#' n <- 10000
#'
#' Dist <- c("Logistic", "Weibull")
#' Params <- list(c(0, 1), c(3, 5))
#' Stcum1 <- calc_theory(Dist[1], Params[[1]])
#' Stcum2 <- calc_theory(Dist[2], Params[[2]])
#' Stcum <- rbind(Stcum1, Stcum2)
#' rownames(Stcum) <- Dist
#' colnames(Stcum) <- c("mean", "sd", "skew", "skurtosis", "fifth", "sixth")
#' Stcum
#' Six <- list(seq(1.7, 1.8, 0.01), seq(0.10, 0.25, 0.01))
#' marginal <- list(0.3)
#' lam <- 0.5
#' size <- 2
#' prob <- 0.75
#'
#' Rey <- matrix(0.4, 5, 5)
#' diag(Rey) <- 1
#'
#' # Make sure Rey is within upper and lower correlation limits
#' valid <- valid_corr(k_cat = 1, k_cont = 2, k_pois = 1, k_nb = 1,
#' method = "Polynomial", means = Stcum[, 1],
#' vars = Stcum[, 2]^2, skews = Stcum[, 3],
#' skurts = Stcum[, 4], fifths = Stcum[, 5],
#' sixths = Stcum[, 6], Six = Six, marginal = marginal,
#' lam = lam, size = size, prob = prob, rho = Rey,
#' seed = seed)
#'
#' # Simulate variables without error loop
#' Sim1 <- rcorrvar(n = n, k_cat = 1, k_cont = 2, k_pois = 1, k_nb = 1,
#' method = "Polynomial", means = Stcum[, 1],
#' vars = Stcum[, 2]^2, skews = Stcum[, 3],
#' skurts = Stcum[, 4], fifths = Stcum[, 5],
#' sixths = Stcum[, 6], Six = Six, marginal = marginal,
#' lam = lam, size = size, prob = prob, rho = Rey,
#' seed = seed)
#' names(Sim1)
#'
#' # Look at the maximum correlation error
#' Sim1$maxerr
#'
#' Sim1_error = round(Sim1$correlations - Rey, 6)
#'
#' # interquartile-range of correlation errors
#' quantile(as.numeric(Sim1_error), 0.25)
#' quantile(as.numeric(Sim1_error), 0.75)
#'
#' # Simulate variables with error loop
#' Sim1_EL <- rcorrvar(n = n, k_cat = 1, k_cont = 2,
#' k_pois = 1, k_nb = 1, method = "Polynomial",
#' means = Stcum[, 1], vars = Stcum[, 2]^2,
#' skews = Stcum[, 3], skurts = Stcum[, 4],
#' fifths = Stcum[, 5], sixths = Stcum[, 6],
#' Six = Six, marginal = marginal, lam = lam,
#' size = size, prob = prob, rho = Rey,
#' seed = seed, errorloop = TRUE)
#' # Look at the maximum correlation error
#' Sim1_EL$maxerr
#'
#' EL_error = round(Sim1_EL$correlations - Rey, 6)
#'
#' # interquartile-range of correlation errors
#' quantile(as.numeric(EL_error), 0.25)
#' quantile(as.numeric(EL_error), 0.75)
#'
#' # Look at results
#' # Ordinal variables
#' Sim1_EL$summary_ordinal
#'
#' # Continuous variables
#' round(Sim1_EL$constants, 6)
#' round(Sim1_EL$summary_continuous, 6)
#' round(Sim1_EL$summary_targetcont, 6)
#' Sim1_EL$valid.pdf
#'
#' # Count variables
#' Sim1_EL$summary_Poisson
#' Sim1_EL$summary_Neg_Bin
#'
#' # Generate Plots
#'
#' # Logistic (1st continuous variable)
#' # 1) Simulated Data CDF (find cumulative probability up to y = 0.5)
#' plot_sim_cdf(Sim1_EL$continuous_variables[, 1], calc_cprob = TRUE,
#' delta = 0.5)
#'
#' # 2) Simulated Data and Target Distribution PDFs
#' plot_sim_pdf_theory(Sim1_EL$continuous_variables[, 1], Dist = "Logistic",
#' params = c(0, 1))
#'
#' # 3) Simulated Data and Target Distribution
#' plot_sim_theory(Sim1_EL$continuous_variables[, 1], Dist = "Logistic",
#' params = c(0, 1))
#'
#' }
rcorrvar <- function(n = 10000, k_cont = 0, k_cat = 0, k_pois = 0, k_nb = 0,
method = c("Fleishman", "Polynomial"),
means = NULL, vars = NULL, skews = NULL,
skurts = NULL, fifths = NULL, sixths = NULL,
Six = list(), marginal = list(), support = list(),
nrand = 100000, lam = NULL,
size = NULL, prob = NULL, mu = NULL, Sigma = NULL,
rho = NULL, cstart = NULL, seed = 1234,
errorloop = FALSE, epsilon = 0.001, maxit = 1000,
extra_correct = TRUE) {
start.time <- Sys.time()
k <- k_cat + k_cont + k_pois + k_nb
if (k_cat > 0 & k_cat != length(marginal))
stop("Length of marginal does not match the number of Ordinal variables!")
if (k_cont > 0 & k_cont != length(means))
stop("Length of means does not match the number of Continuous variables!")
if (k_cont > 0 & k_cont != length(vars))
stop("Length of variances does not match the number of Continuous
variables!")
if (k_pois > 0 & k_pois != length(lam))
stop("Length of lam does not match the number of Poisson variables!")
if (k_pois > 0 & sum(lam < 0) > 0)
stop("Lambda values cannnot be negative!")
if (k_nb > 0 & k_nb != length(size))
stop("Length of size does not match the number of Negative Binomial
variables!")
if (k_nb > 0 & length(prob) > 1 & length(mu) > 1)
stop("Either give success probabilities or means for Negative Binomial
variables!")
SixCorr <- numeric(k_cont)
Valid.PDF <- numeric(k_cont)
if (ncol(rho) != k)
stop("Correlation matrix dimension does not match the number of
variables!")
if (!isSymmetric(rho) | min(eigen(rho, symmetric = TRUE)$values) < 0 |
!all(diag(rho) == 1)) stop("Correlation matrix not valid!")
start.time.constants <- Sys.time()
csame.dist <- NULL
if (k_cont > 1) {
for (i in 2:length(skews)) {
if (skews[i] %in% skews[1:(i - 1)]) {
csame <- which(skews[1:(i - 1)] == skews[i])
for (j in 1:length(csame)) {
if (method == "Polynomial") {
if ((skurts[i] == skurts[csame[j]]) &
(fifths[i] == fifths[csame[j]]) &
(sixths[i] == sixths[csame[j]])) {
csame.dist <- rbind(csame.dist, c(csame[j], i))
break
}
}
if (method == "Fleishman") {
if (skurts[i] == skurts[csame[j]]) {
csame.dist <- rbind(csame.dist, c(csame[j], i))
break
}
}
}
}
}
}
if (k_cont >= 1) {
if (method == "Fleishman") {
constants <- matrix(NA, nrow = k_cont, ncol = 4)
colnames(constants) <- c("c0", "c1", "c2", "c3")
}
if (method == "Polynomial") {
constants <- matrix(NA, nrow = k_cont, ncol = 6)
colnames(constants) <- c("c0", "c1", "c2", "c3", "c4", "c5")
}
for (i in 1:k_cont) {
if (!is.null(csame.dist)) {
rind <- which(csame.dist[, 2] == i)
if (length(rind) > 0) {
constants[i, ] <- constants[csame.dist[rind, 1], ]
SixCorr[i] <- SixCorr[csame.dist[rind, 1]]
Valid.PDF[i] <- Valid.PDF[csame.dist[rind, 1]]
}
}
if (sum(is.na(constants[i, ])) > 0) {
if (length(Six) == 0) Six2 <- NULL else
Six2 <- Six[[i]]
if (length(cstart) == 0) cstart2 <- NULL else
cstart2 <- cstart[[i]]
cons <-
suppressWarnings(find_constants(method = method, skews = skews[i],
skurts = skurts[i],
fifths = fifths[i],
sixths = sixths[i], Six = Six2,
cstart = cstart2, n = 25,
seed = seed))
if (length(cons) == 1 | is.null(cons)) {
stop(paste("Constants can not be found for continuous variable ", i,
".", sep = ""))
}
con_solution <- cons$constants
SixCorr[i] <- ifelse(is.null(cons$SixCorr1), NA, cons$SixCorr1)
Valid.PDF[i] <- cons$valid
constants[i, ] <- con_solution
}
cat("\n", "Constants: Distribution ", i, " \n")
}
}
stop.time.constants <- Sys.time()
if (k_cat > 0) {
if (!all(unlist(lapply(marginal, function(x) (sort(x) == x & min(x) > 0 &
max(x) < 1)))))
stop("Error in given marginal distributions!")
len <- length(support)
kj <- numeric(k_cat)
for (i in 1:k_cat) {
kj[i] <- length(marginal[[i]]) + 1
if (len == 0) {
support[[i]] <- 1:kj[i]
}
}
}
start.time.intercorr <- Sys.time()
if (is.null(Sigma)) {
Sigma <- findintercorr(n = n, k_cont = k_cont, k_cat = k_cat,
k_pois = k_pois, k_nb = k_nb, method = method,
constants = constants, marginal = marginal,
support = support, nrand = nrand, lam = lam,
size = size, prob = prob, mu = mu, rho = rho,
seed = seed, epsilon = epsilon, maxit = maxit)
}
if (min(eigen(Sigma, symmetric = TRUE)$values) < 0) {
warning("Intermediate correlation matrix is not positive definite.
Nearest positive definite matrix is used!")
Sigma <- as.matrix(nearPD(Sigma, corr = T, keepDiag = T)$mat)
}
if (!isSymmetric(Sigma) | min(eigen(Sigma, symmetric = TRUE)$values) < 0 |
!all(diag(Sigma) == 1))
stop("Calculated intermediate correlation matrix not valid!")
stop.time.intercorr <- Sys.time()
eig <- eigen(Sigma, symmetric = TRUE)
sqrteigval <- diag(sqrt(eig$values), nrow = nrow(Sigma), ncol = ncol(Sigma))
eigvec <- eig$vectors
fry <- eigvec %*% sqrteigval
set.seed(seed)
X <- matrix(rnorm(k * n), n)
X <- scale(X, TRUE, FALSE)
X <- X %*% svd(X, nu = 0)$v
X <- scale(X, FALSE, TRUE)
X <- fry %*% t(X)
X <- t(X)
if (k_cat > 0) {
X_cat <- matrix(X[, 1:k_cat], nrow = n, ncol = k_cat, byrow = F)
}
if (k_cont > 0) {
X_cont <- matrix(X[, (k_cat + 1):(k_cat + k_cont)], nrow = n,
ncol = k_cont, byrow = F)
}
if (k_pois > 0) {
X_pois <- matrix(X[, (k_cat + k_cont + 1):(k_cat + k_cont + k_pois)],
nrow = n, ncol = k_pois, byrow = F)
}
if (k_nb > 0) {
X_nb <- matrix(X[, (k_cat + k_cont + k_pois + 1):(k_cat + k_cont +
k_pois + k_nb)],
nrow = n, ncol = k_nb, byrow = F)
}
Y_cat <- NULL
Y <- NULL
Yb <- NULL
Y_pois <- NULL
Y_nb <- NULL
if (k_cat > 0) {
Y_cat <- matrix(1, nrow = n, ncol = k_cat)
for (i in 1:length(marginal)) {
Y_cat[, i] <- as.integer(cut(X_cat[, i], breaks = c(min(X_cat[, i]) - 1,
qnorm(marginal[[i]]), max(X_cat[, i]) + 1)))
Y_cat[, i] <- support[[i]][Y_cat[, i]]
}
}
if (k_cont > 0) {
Y <- matrix(1, nrow = n, ncol = k_cont)
Yb <- matrix(1, nrow = n, ncol = k_cont)
for (i in 1:k_cont) {
if (method == "Fleishman") {
Y[, i] <- constants[i, 1] + constants[i, 2] * X_cont[, i] +
constants[i, 3] * X_cont[, i]^2 + constants[i, 4] * X_cont[, i]^3
}
if (method == "Polynomial") {
Y[, i] <- constants[i, 1] + constants[i, 2] * X_cont[, i] +
constants[i, 3] * X_cont[, i]^2 + constants[i, 4] * X_cont[, i]^3 +
constants[i, 5] * X_cont[, i]^4 + constants[i, 6] * X_cont[, i]^5
}
Yb[, i] <- means[i] + sqrt(vars[i]) * Y[, i]
}
}
if (k_pois > 0) {
Y_pois <- matrix(1, nrow = n, ncol = k_pois)
for (i in 1:k_pois) {
Y_pois[, i] <- qpois(pnorm(X_pois[, i]), lam[i])
}
}
if (k_nb > 0) {
Y_nb <- matrix(1, nrow = n, ncol = k_nb)
if (length(prob) > 0) {
for (i in 1:k_nb) {
Y_nb[, i] <- qnbinom(pnorm(X_nb[, i]), size[i], prob[i])
}
}
if (length(mu) > 0) {
for (i in 1:k_nb) {
Y_nb[, i] <- qnbinom(pnorm(X_nb[, i]), size[i], mu = mu[i])
}
}
}
rho_calc <- calc_final_corr(k_cat = k_cat, k_cont = k_cont, k_pois = k_pois,
k_nb = k_nb, Y_cat = Y_cat, Yb = Yb,
Y_pois = Y_pois, Y_nb = Y_nb)
Sigma1 <- Sigma
start.time.error <- Sys.time()
rho0 <- rho
niter <- diag(0, k, k)
emax <- max(abs(rho_calc - rho0))
if (emax > epsilon & errorloop == TRUE) {
EL <- error_loop(k_cat = k_cat, k_cont = k_cont, k_pois = k_pois,
k_nb = k_nb, Y_cat = Y_cat, Y = Y, Yb = Yb,
Y_pois = Y_pois, Y_nb = Y_nb, marginal = marginal,
support = support, method = method, means = means,
vars = vars, constants = constants,
lam = lam, size = size, prob = prob, mu = mu,
n = n, seed = seed, epsilon = epsilon, maxit = maxit,
rho0 = rho0, Sigma = Sigma, rho_calc = rho_calc,
extra_correct = extra_correct)
Sigma <- EL$Sigma
rho_calc <- EL$rho_calc
Y_cat <- EL$Y_cat
Y <- EL$Y
Yb <- EL$Yb
Y_pois <- EL$Y_pois
Y_nb <- EL$Y_nb
niter <- EL$niter
}
stop.time.error <- Sys.time()
Sigma2 <- Sigma
emax <- max(abs(rho_calc - rho0))
niter <- as.data.frame(niter)
rownames(niter) <- c(1:k)
colnames(niter) <- c(1:k)
stop.time <- Sys.time()
Time.constants <- round(difftime(stop.time.constants, start.time.constants,
units = "min"), 3)
cat("\nConstants calculation time:", Time.constants, "minutes \n")
Time.intercorr <- round(difftime(stop.time.intercorr, start.time.intercorr,
units = "min"), 3)
cat("Intercorrelation calculation time:", Time.intercorr, "minutes \n")
Time.error <- round(difftime(stop.time.error, start.time.error,
units = "min"), 3)
cat("Error loop calculation time:", Time.error, "minutes \n")
Time <- round(difftime(stop.time, start.time, units = "min"), 3)
cat("Total Simulation time:", Time, "minutes \n")
result <- list()
if (k_cat > 0) {
summary_cat <- list()
for (i in 1:k_cat) {
summary_cat[[i]] <- as.data.frame(cbind(append(marginal[[i]], 1),
cumsum(table(Y_cat[, i]))/n))
colnames(summary_cat[[i]]) <- c("Target", "Simulated")
}
result <- append(result, list(ordinal_variables = as.data.frame(Y_cat),
summary_ordinal = summary_cat))
}
if (k_cont > 0) {
cont_sum <- describe(Yb, type = 1)
sim_fifths <- rep(NA, k_cont)
sim_sixths <- rep(NA, k_cont)
for (i in 1:k_cont) {
sim_fifths[i] <- calc_moments(Yb[, i])[5]
sim_sixths[i] <- calc_moments(Yb[, i])[6]
}
cont_sum <- as.data.frame(cbind(c(1:k_cont),
cont_sum[, -c(1, 6, 7, 10, 13)],
sim_fifths, sim_sixths))
colnames(cont_sum) <- c("Distribution", "n", "mean", "sd", "median",
"min", "max", "skew", "skurtosis", "fifth",
"sixth")
if (method == "Fleishman") {
target_sum <- as.data.frame(cbind(c(1:k_cont), means, sqrt(vars), skews,
skurts))
colnames(target_sum) <- c("Distribution", "mean", "sd", "skew",
"skurtosis")
} else {
target_sum <- as.data.frame(cbind(c(1:k_cont), means, sqrt(vars), skews,
skurts, fifths, sixths))
colnames(target_sum) <- c("Distribution", "mean", "sd", "skew",
"skurtosis", "fifth", "sixth")
}
result <- append(result, list(constants = as.data.frame(constants),
continuous_variables = as.data.frame(Yb),
summary_continuous = cont_sum,
summary_targetcont = target_sum,
sixth_correction = SixCorr,
valid.pdf = Valid.PDF))
}
if (k_pois > 0) {
summary_pois <- describe(Y_pois, type = 1)
summary_pois <- as.data.frame(cbind(summary_pois$vars, summary_pois$n,
summary_pois$mean, lam,
(summary_pois[, 4])^2, lam,
summary_pois$median, summary_pois$min,
summary_pois$max, summary_pois$skew,
summary_pois$kurtosis))
colnames(summary_pois) <- c("Distribution", "n", "mean", "Exp_mean",
"var", "Exp_var", "median", "min", "max",
"skew", "skurtosis")
result <- append(result, list(Poisson_variables = as.data.frame(Y_pois),
summary_Poisson = summary_pois))
}
if (k_nb > 0) {
summary_nb <- describe(Y_nb, type = 1)
if (length(prob) > 0) {
mu <- size * (1 - prob)/prob
}
if (length(mu) > 0) {
prob <- size/(mu + size)
}
summary_nb <- as.data.frame(cbind(summary_nb$vars, summary_nb$n, prob,
summary_nb$mean, mu,
(summary_nb[, 4])^2,
size * (1 - prob)/prob^2,
summary_nb$median, summary_nb$min,
summary_nb$max,
summary_nb$skew, summary_nb$kurtosis))
colnames(summary_nb) <- c("Distribution", "n", "success_prob", "mean",
"Exp_mean", "var", "Exp_var", "median",
"min", "max", "skew", "skurtosis")
result <- append(result, list(Neg_Bin_variables = as.data.frame(Y_nb),
summary_Neg_Bin = summary_nb))
}
result <- append(result, list(correlations = rho_calc, Sigma1 = Sigma1,
Sigma2 = Sigma2,
Constants_Time = Time.constants,
Intercorrelation_Time = Time.intercorr,
Error_Loop_Time = Time.error,
Simulation_Time = Time,
niter = niter, maxerr = emax))
result
}
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