R/validpar.R
In AFialkowski/SimCorrMix: Simulation of Correlated Data with Multiple Variable Types Including Continuous and Count Mixture Distributions
#' @title Parameter Check for Simulation or Correlation Validation Functions
#'
#' @description This function checks the parameter inputs to the simulation functions \code{\link[SimCorrMix]{contmixvar1}},
#' \code{\link[SimCorrMix]{corrvar}}, and \code{\link[SimCorrMix]{corrvar2}} and to the correlation validation functions
#' \code{\link[SimCorrMix]{validcorr}} and \code{\link[SimCorrMix]{validcorr2}}. It should be used prior to execution of these
#' functions to ensure all inputs are of the correct format. Those functions do not contain parameter checks in order to decrease
#' simulation time. This would be important if the user is running several simulation repetitions so that the inputs only have to
#' be checked once. Note that the inputs do not include all of the inputs to the simulation functions. See the appropriate function
#' documentation for more details about parameter inputs.
#'
#' @param k_cat the number of ordinal (r >= 2 categories) variables (default = 0)
#' @param k_cont the number of continuous non-mixture variables (default = 0)
#' @param k_mix the number of continuous mixture variables (default = 0)
#' @param k_pois the number of regular Poisson and zero-inflated Poisson variables (default = 0)
#' @param k_nb the number of regular Negative Binomial and zero-inflated Negative Binomial variables (default = 0)
#' @param method the method used to generate the \code{k_cont} non-mixture and \code{k_mix} mixture 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 \code{k_cont} non-mixture and \code{k_mix} mixture continuous variables
#' (i.e. \code{rep(0, (k_cont + k_mix))})
#' @param vars a vector of variances for the \code{k_cont} non-mixture and \code{k_mix} mixture continuous variables
#' (i.e. \code{rep(1, (k_cont + k_mix))})
#' @param skews a vector of skewness values for the \code{k_cont} non-mixture continuous variables
#' @param skurts a vector of standardized kurtoses (kurtosis - 3, so that normal variables have a value of 0)
#' for the \code{k_cont} non-mixture continuous variables
#' @param fifths a vector of standardized fifth cumulants for the \code{k_cont} non-mixture continuous variables
#' (not necessary for \code{method} = "Fleishman")
#' @param sixths a vector of standardized sixth cumulants for the \code{k_cont} non-mixture continuous variables
#' (not necessary for \code{method} = "Fleishman")
#' @param Six a list of vectors of sixth cumulant correction values for the \code{k_cont} non-mixture continuous variables
#' if no valid PDF constants are found, \cr ex: \code{Six = list(seq(0.01, 2, 0.01), seq(1, 10, 0.5))};
#' if no correction is desired for variable \eqn{Y_{cont_i}}, set set the i-th list component equal to \code{NULL};
#' if no correction is desired for any of the \eqn{Y_{cont}} keep as \code{Six = list()}
#' (not necessary for \code{method} = "Fleishman")
#' @param mix_pis a vector if using \code{\link[SimCorrMix]{contmixvar1}} or a list of length \code{k_mix} with i-th component a vector of mixing probabilities that sum to 1 for component distributions of \eqn{Y_{mix_i}}
#' @param mix_mus a vector if using \code{\link[SimCorrMix]{contmixvar1}} or a list of length \code{k_mix} with i-th component a vector of means for component distributions of \eqn{Y_{mix_i}}
#' @param mix_sigmas a vector if using \code{\link[SimCorrMix]{contmixvar1}} or a list of length \code{k_mix} with i-th component a vector of standard deviations for component distributions of \eqn{Y_{mix_i}}
#' @param mix_skews a vector if using \code{\link[SimCorrMix]{contmixvar1}} or a list of length \code{k_mix} with i-th component a vector of skew values for component distributions of \eqn{Y_{mix_i}}
#' @param mix_skurts a vector if using \code{\link[SimCorrMix]{contmixvar1}} or a list of length \code{k_mix} with i-th component a vector of standardized kurtoses for component distributions of \eqn{Y_{mix_i}}
#' @param mix_fifths a vector if using \code{\link[SimCorrMix]{contmixvar1}} or a list of length \code{k_mix} with i-th component a vector of standardized fifth cumulants for component distributions of \eqn{Y_{mix_i}}
#' (not necessary for \code{method} = "Fleishman")
#' @param mix_sixths a vector if using \code{\link[SimCorrMix]{contmixvar1}} or a list of length \code{k_mix} with i-th component a vector of standardized sixth cumulants for component distributions of \eqn{Y_{mix_i}}
#' (not necessary for \code{method} = "Fleishman")
#' @param mix_Six if using \code{\link[SimCorrMix]{contmixvar1}}, a list of vectors of sixth cumulant corrections for the components of the
#' continuous mixture variable; else a list of length \code{k_mix} with i-th component a list of vectors of sixth cumulant correction values
#' for component distributions of \eqn{Y_{mix_i}}; use \code{NULL} if no correction is desired for a given component or
#' mixture variable; if no correction is desired for any of the \eqn{Y_{mix}} keep as \code{mix_Six = list()}
#' (not necessary for \code{method} = "Fleishman")
#' @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 lam a vector of lambda (mean > 0) constants for the Poisson variables (see \code{stats::dpois}); the order should be
#' 1st regular Poisson variables, 2nd zero-inflated Poisson variables
#' @param p_zip a vector of probabilities of structural zeros (not including zeros from the Poisson distribution) for the
#' zero-inflated Poisson variables (see \code{VGAM::dzipois}); if \code{p_zip} = 0, \eqn{Y_{pois}} has a regular Poisson
#' distribution; if \code{p_zip} is in (0, 1), \eqn{Y_{pois}} has a zero-inflated Poisson distribution;
#' if \code{p_zip} is in \code{(-(exp(lam) - 1)^(-1), 0)}, \eqn{Y_{pois}} has a zero-deflated Poisson distribution and \code{p_zip}
#' is not a probability; if \code{p_zip = -(exp(lam) - 1)^(-1)}, \eqn{Y_{pois}} has a positive-Poisson distribution
#' (see \code{VGAM::dpospois}); if \code{length(p_zip) < length(lam)}, the missing values are set to 0 (and ordered 1st)
#' @param size a vector of size parameters for the Negative Binomial variables (see \code{stats::dnbinom}); the order should be
#' 1st regular NB variables, 2nd zero-inflated NB variables
#' @param prob a vector of success probability parameters for the NB variables; order the same as in \code{size}
#' @param mu a vector of mean parameters for the NB variables (*Note: either \code{prob} or \code{mu} should be supplied for all Negative Binomial variables,
#' not a mixture; default = NULL); order the same as in \code{size}; for zero-inflated NB this refers to
#' the mean of the NB distribution (see \code{VGAM::dzinegbin})
#' @param p_zinb a vector of probabilities of structural zeros (not including zeros from the NB distribution) for the zero-inflated NB variables
#' (see \code{VGAM::dzinegbin}); if \code{p_zinb} = 0, \eqn{Y_{nb}} has a regular NB distribution;
#' if \code{p_zinb} is in \code{(-prob^size/(1 - prob^size),} \code{0)}, \eqn{Y_{nb}} has a zero-deflated NB distribution and \code{p_zinb}
#' is not a probability; if \code{p_zinb = -prob^size/(1 - prob^size)}, \eqn{Y_{nb}} has a positive-NB distribution (see
#' \code{VGAM::dposnegbin}); if \code{length(p_zinb) < length(size)}, the missing values are set to 0 (and ordered 1st)
#' @param pois_eps a vector of length \code{k_pois} containing total cumulative probability truncation values; if none are provided,
#' the default is 0.0001 for each variable
#' @param nb_eps a vector of length \code{k_nb} containing total cumulative probability truncation values; if none are provided,
#' the default is 0.0001 for each variable
#' @param rho the target correlation matrix which must be ordered
#' \emph{1st ordinal, 2nd continuous non-mixture, 3rd components of continuous mixtures, 4th regular Poisson, 5th zero-inflated Poisson,
#' 6th regular NB, 7th zero-inflated NB}; note that \code{rho} is specified in terms of the components of \eqn{Y_{mix}}
#' @param Sigma an intermediate correlation matrix to use if the user wants to provide one, else it is calculated within by
#' \code{\link[SimCorrMix]{intercorr}}
#' @param cstart a list of length equal to \code{k_cont} + the total number of mixture components containing initial values for root-solving
#' algorithm used in \code{\link[SimMultiCorrData]{find_constants}}. If user specified, each list element must be input as a matrix.
#' For \code{method} = "Fleishman", each should have 3 columns for \eqn{c1, c2, c3};
#' for \code{method} = "Polynomial", each should have 5 columns for \eqn{c1, c2, c3, c4, c5}. If no starting values are specified for
#' a given component, that list element should be \code{NULL}.
#' @param quiet if FALSE prints messages, if TRUE suppresses message printing
#' @importFrom stats cor dbeta dbinom dchisq density dexp df dgamma dlnorm dlogis dmultinom dnbinom dnorm dpois dt dunif dweibull ecdf
#' median pbeta pbinom pchisq pexp pf pgamma plnorm plogis pnbinom pnorm ppois pt punif pweibull qbeta qbinom qchisq qexp qf qgamma
#' qlnorm qlogis qnbinom qnorm qpois qt quantile qunif qweibull rbeta rbinom rchisq rexp rf rgamma rlnorm rlogis rmultinom rnbinom
#' rnorm rpois rt runif rweibull sd uniroot var
#' @import utils
#' @export
#' @return TRUE if all inputs are correct, else it will stop with a correction message
#' @keywords ParameterCheck
#' @seealso \code{\link[SimCorrMix]{contmixvar1}}, \code{\link[SimCorrMix]{corrvar}}, \code{\link[SimCorrMix]{corrvar2}},
#' \code{\link[SimCorrMix]{validcorr}}, \code{\link[SimCorrMix]{validcorr2}}
#' @examples
#' validpar(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)), rho = matrix(c(1, 0.4, 0.4, 1), 2, 2),
#' quiet = TRUE)
#' \dontrun{
#' # 2 continuous mixture, 1 binary, 1 zero-inflated Poisson, and
#' # 1 zero-inflated NB variable
#'
#' # Mixture variables: Normal mixture with 2 components;
#' # mixture of Logistic(0, 1), Chisq(4), Beta(4, 1.5)
#' # Find cumulants of components of 2nd mixture variable
#' L <- calc_theory("Logistic", c(0, 1))
#' C <- calc_theory("Chisq", 4)
#' B <- calc_theory("Beta", c(4, 1.5))
#'
#' skews <- skurts <- fifths <- sixths <- NULL
#' Six <- list()
#' mix_pis <- list(c(0.4, 0.6), c(0.3, 0.2, 0.5))
#' mix_mus <- list(c(-2, 2), c(L[1], C[1], B[1]))
#' mix_sigmas <- list(c(1, 1), c(L[2], C[2], B[2]))
#' mix_skews <- list(rep(0, 2), c(L[3], C[3], B[3]))
#' mix_skurts <- list(rep(0, 2), c(L[4], C[4], B[4]))
#' mix_fifths <- list(rep(0, 2), c(L[5], C[5], B[5]))
#' mix_sixths <- list(rep(0, 2), c(L[6], C[6], B[6]))
#' mix_Six <- list(list(NULL, NULL), list(1.75, NULL, 0.03))
#' Nstcum <- calc_mixmoments(mix_pis[[1]], mix_mus[[1]], mix_sigmas[[1]],
#' mix_skews[[1]], mix_skurts[[1]], mix_fifths[[1]], mix_sixths[[1]])
#' Mstcum <- calc_mixmoments(mix_pis[[2]], mix_mus[[2]], mix_sigmas[[2]],
#' mix_skews[[2]], mix_skurts[[2]], mix_fifths[[2]], mix_sixths[[2]])
#' means <- c(Nstcum[1], Mstcum[1])
#' vars <- c(Nstcum[2]^2, Mstcum[2]^2)
#'
#' marginal <- list(0.3)
#' support <- list(c(0, 1))
#' lam <- 0.5
#' p_zip <- 0.1
#' size <- 2
#' prob <- 0.75
#' p_zinb <- 0.2
#'
#' k_cat <- k_pois <- k_nb <- 1
#' k_cont <- 0
#' k_mix <- 2
#' Rey <- matrix(0.39, 8, 8)
#' diag(Rey) <- 1
#' rownames(Rey) <- colnames(Rey) <- c("O1", "M1_1", "M1_2", "M2_1", "M2_2",
#' "M2_3", "P1", "NB1")
#'
#' # set correlation between components of the same mixture variable to 0
#' Rey["M1_1", "M1_2"] <- Rey["M1_2", "M1_1"] <- 0
#' Rey["M2_1", "M2_2"] <- Rey["M2_2", "M2_1"] <- Rey["M2_1", "M2_3"] <- 0
#' Rey["M2_3", "M2_1"] <- Rey["M2_2", "M2_3"] <- Rey["M2_3", "M2_2"] <- 0
#'
#' # use before contmixvar1 with 1st mixture variable:
#' # change mix_pis to not sum to 1
#'
#' check1 <- validpar(k_mix = 1, method = "Polynomial", means = Nstcum[1],
#' vars = Nstcum[2]^2, mix_pis = C(0.4, 0.5), mix_mus = mix_mus[[1]],
#' mix_sigmas = mix_sigmas[[1]], mix_skews = mix_skews[[1]],
#' mix_skurts = mix_skurts[[1]], mix_fifths = mix_fifths[[1]],
#' mix_sixths = mix_sixths[[1]])
#'
#' # use before validcorr: should return TRUE
#'
#' check2 <- validpar(k_cat, k_cont, k_mix, k_pois, k_nb, "Polynomial", means,
#' vars, skews, skurts, fifths, sixths, Six, mix_pis, mix_mus, mix_sigmas,
#' mix_skews, mix_skurts, mix_fifths, mix_sixths, mix_Six, marginal, support,
#' lam, p_zip, size, prob, mu = NULL, p_zinb, rho = Rey)
#'
#' }
#'
validpar <- function(k_cat = 0, k_cont = 0, k_mix = 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(), mix_pis = list(), mix_mus = list(),
mix_sigmas = list(), mix_skews = list(),
mix_skurts = list(), mix_fifths = list(),
mix_sixths = list(), mix_Six = list(),
marginal = list(), support = list(), lam = NULL,
p_zip = 0, size = NULL, prob = NULL, mu = NULL,
p_zinb = 0, pois_eps = 0.0001, nb_eps = 0.0001,
rho = NULL, Sigma = NULL, cstart = list(),
quiet = FALSE) {
if (k_cat > 0) {
if (k_cat != length(marginal))
stop("Length of marginal does not match the number of ordinal
variables.")
if (!all(unlist(lapply(marginal,
function(x) (sort(x) == x & min(x) > 0 & max(x) < 1)))))
stop("Error in given marginal distributions.")
if (length(support) > 0 & (k_cat != length(support)))
stop("Length of support does not match the number of ordinal
variables.")
}
k_comp <- 0
if ((k_cont + k_mix) > 0) {
if (length(method) != 1)
stop("Choose a PMT method for the continuous variables.")
if (length(means) != (k_cont + k_mix) | length(vars) != (k_cont + k_mix))
stop("Length of means and vars should be k_cont + k_mix.")
if (k_cont > 0) {
if (length(skews) != k_cont | length(skurts) != k_cont)
stop("Parameters for continuous non-mixture distributions should be
vectors of length k_cont.")
if (method == "Polynomial") {
if (length(fifths) != k_cont | length(sixths) != k_cont |
(length(Six) != 0 & length(Six) != k_cont))
stop("Parameters for continuous non-mixture distributions should be
vectors of length k_cont. Six should be either list() or
a list of length k_cont.")
}
}
if (k_mix > 0) {
if (class(mix_pis) == "list") {
k_comp <- lengths(mix_pis)
if (length(mix_pis) != k_mix | length(mix_mus) != k_mix |
length(mix_sigmas) != k_mix | length(mix_skews) != k_mix |
length(mix_skurts) != k_mix)
stop("Parameters for continuous mixture distributions should be
lists of length equal to k_mix.")
if (!all(lengths(mix_mus) %in% k_comp) |
!all(lengths(mix_sigmas) %in% k_comp) |
!all(lengths(mix_skews) %in% k_comp) |
!all(lengths(mix_skurts) %in% k_comp))
stop("Components of mixture parameter lists should be the same
length as components of mix_pis.")
if (method == "Polynomial") {
if (length(mix_fifths) != k_mix | length(mix_sixths) != k_mix |
(length(mix_Six) != 0 & length(mix_Six) != k_mix))
stop("Parameters for continuous mixture distributions should be
lists of length equal to k_mix. mix_Six should be either
list() or a list of length k_mix.")
if (!all(lengths(mix_fifths) %in% k_comp) |
!all(lengths(mix_sixths) %in% k_comp))
stop("Components of mixture parameter lists should be the same
length as components of mix_pis.")
}
if (all.equal(unlist(lapply(mix_pis, sum)), rep(1, k_mix)) == FALSE)
stop("Mixing parameters should sum to 1 for each variable.")
}
if (class(mix_pis) == "numeric") {
if (k_mix > 1 | k_cat > 0 | k_pois > 0 | k_nb > 0)
stop("Mixture parameters should be lists of length equal to k_mix.")
if (sum(mix_pis) != 1)
stop("Mixing parameters should sum to 1.")
k_comp <- length(mix_pis)
if (length(mix_mus) != k_comp | length(mix_sigmas) != k_comp |
length(mix_skews) != k_comp | length(mix_skurts) != k_comp)
stop("Mixture parameters should have same length as mix_pis.")
if (method == "Polynomial") {
if (length(mix_fifths) != k_comp | length(mix_sixths) != k_comp |
!(length(mix_Six) %in% c(0, k_comp)))
stop("Mixture parameters should have same length as mix_pis.")
}
}
}
if (length(cstart) != 0) {
if (length(cstart) != (k_cont + k_comp))
stop("Length of cstart should be k_cont + number of mixture
components.")
k.c <- unlist(lapply(cstart, ncol))
if (method == "Fleishman") {
if (!all.equal(k.c, rep(3, length(cstart))))
stop("Dimension of cstart matrices should be number of starting
values by 3")
} else {
if (!all.equal(k.c, rep(5, length(cstart))))
stop("Dimension of cstart matrices should be number of starting
values by 5")
}
}
}
if (k_pois > 0) {
if (k_pois != length(lam))
stop("Length of lam does not match the number of Poisson variables.")
if (sum(lam < 0) > 0)
stop("Lambda values cannot be negative.")
if (length(p_zip) < k_pois & quiet == FALSE)
message("Default of p_zip = 0 will be used for Poisson variables.")
if (length(pois_eps) < k_pois & quiet == FALSE)
message("Default of pois_eps = 0.0001 will be used for Poisson variables
if using correlation method 2.")
}
if (k_nb > 0) {
if (k_nb != length(size))
stop("Length of size does not match the number of NB variables.")
if (length(prob) > 0 & length(mu) > 0)
stop("Either give success probabilities or means for NB variables.")
if (length(prob) > 0 & k_nb != length(prob))
stop("Length of prob does not match the number of NB variables.")
if (length(mu) > 0 & k_nb != length(mu))
stop("Length of mu does not match the number of NB variables.")
if (length(p_zinb) < k_nb & quiet == FALSE)
message("Default of p_zinb = 0 will be used for NB variables.")
if (length(nb_eps) < k_nb & quiet == FALSE)
message("Default of nb_eps = 0.0001 will be used for NB variables
if using correlation method 2.")
}
k <- k_cat + k_cont + sum(k_comp) + k_pois + k_nb
if (!is.null(Sigma)) {
if (ncol(Sigma) != k | nrow(Sigma) != k)
stop("Sigma matrix is not of the right dimension.")
if (!isSymmetric(Sigma) | !all(diag(Sigma) == 1))
stop("Sigma matrix not valid! Check symmetry and diagonal values.")
if (min(eigen(Sigma, symmetric = TRUE)$values) < 0 & quiet == FALSE)
message("Sigma matrix is not positive-definite. Nearest
positive-definite matrix will be used if use.nearPD = TRUE.")
}
if (!is.null(rho)) {
if (ncol(rho) != k | nrow(rho) != k)
stop("Correlation matrix is not of the right dimension.")
if (!isSymmetric(rho) | !all(diag(rho) == 1))
stop("Correlation matrix not valid! Check symmetry and diagonal
values.")
if (min(eigen(rho, symmetric = TRUE)$values) < 0 & quiet == FALSE)
message("Target correlation matrix is not positive definite.")
}
return(TRUE)
}
AFialkowski/SimCorrMix documentation built on May 30, 2019, 3:47 p.m.
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#' @title Parameter Check for Simulation or Correlation Validation Functions
#'
#' @description This function checks the parameter inputs to the simulation functions \code{\link[SimCorrMix]{contmixvar1}},
#' \code{\link[SimCorrMix]{corrvar}}, and \code{\link[SimCorrMix]{corrvar2}} and to the correlation validation functions
#' \code{\link[SimCorrMix]{validcorr}} and \code{\link[SimCorrMix]{validcorr2}}. It should be used prior to execution of these
#' functions to ensure all inputs are of the correct format. Those functions do not contain parameter checks in order to decrease
#' simulation time. This would be important if the user is running several simulation repetitions so that the inputs only have to
#' be checked once. Note that the inputs do not include all of the inputs to the simulation functions. See the appropriate function
#' documentation for more details about parameter inputs.
#'
#' @param k_cat the number of ordinal (r >= 2 categories) variables (default = 0)
#' @param k_cont the number of continuous non-mixture variables (default = 0)
#' @param k_mix the number of continuous mixture variables (default = 0)
#' @param k_pois the number of regular Poisson and zero-inflated Poisson variables (default = 0)
#' @param k_nb the number of regular Negative Binomial and zero-inflated Negative Binomial variables (default = 0)
#' @param method the method used to generate the \code{k_cont} non-mixture and \code{k_mix} mixture 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 \code{k_cont} non-mixture and \code{k_mix} mixture continuous variables
#' (i.e. \code{rep(0, (k_cont + k_mix))})
#' @param vars a vector of variances for the \code{k_cont} non-mixture and \code{k_mix} mixture continuous variables
#' (i.e. \code{rep(1, (k_cont + k_mix))})
#' @param skews a vector of skewness values for the \code{k_cont} non-mixture continuous variables
#' @param skurts a vector of standardized kurtoses (kurtosis - 3, so that normal variables have a value of 0)
#' for the \code{k_cont} non-mixture continuous variables
#' @param fifths a vector of standardized fifth cumulants for the \code{k_cont} non-mixture continuous variables
#' (not necessary for \code{method} = "Fleishman")
#' @param sixths a vector of standardized sixth cumulants for the \code{k_cont} non-mixture continuous variables
#' (not necessary for \code{method} = "Fleishman")
#' @param Six a list of vectors of sixth cumulant correction values for the \code{k_cont} non-mixture continuous variables
#' if no valid PDF constants are found, \cr ex: \code{Six = list(seq(0.01, 2, 0.01), seq(1, 10, 0.5))};
#' if no correction is desired for variable \eqn{Y_{cont_i}}, set set the i-th list component equal to \code{NULL};
#' if no correction is desired for any of the \eqn{Y_{cont}} keep as \code{Six = list()}
#' (not necessary for \code{method} = "Fleishman")
#' @param mix_pis a vector if using \code{\link[SimCorrMix]{contmixvar1}} or a list of length \code{k_mix} with i-th component a vector of mixing probabilities that sum to 1 for component distributions of \eqn{Y_{mix_i}}
#' @param mix_mus a vector if using \code{\link[SimCorrMix]{contmixvar1}} or a list of length \code{k_mix} with i-th component a vector of means for component distributions of \eqn{Y_{mix_i}}
#' @param mix_sigmas a vector if using \code{\link[SimCorrMix]{contmixvar1}} or a list of length \code{k_mix} with i-th component a vector of standard deviations for component distributions of \eqn{Y_{mix_i}}
#' @param mix_skews a vector if using \code{\link[SimCorrMix]{contmixvar1}} or a list of length \code{k_mix} with i-th component a vector of skew values for component distributions of \eqn{Y_{mix_i}}
#' @param mix_skurts a vector if using \code{\link[SimCorrMix]{contmixvar1}} or a list of length \code{k_mix} with i-th component a vector of standardized kurtoses for component distributions of \eqn{Y_{mix_i}}
#' @param mix_fifths a vector if using \code{\link[SimCorrMix]{contmixvar1}} or a list of length \code{k_mix} with i-th component a vector of standardized fifth cumulants for component distributions of \eqn{Y_{mix_i}}
#' (not necessary for \code{method} = "Fleishman")
#' @param mix_sixths a vector if using \code{\link[SimCorrMix]{contmixvar1}} or a list of length \code{k_mix} with i-th component a vector of standardized sixth cumulants for component distributions of \eqn{Y_{mix_i}}
#' (not necessary for \code{method} = "Fleishman")
#' @param mix_Six if using \code{\link[SimCorrMix]{contmixvar1}}, a list of vectors of sixth cumulant corrections for the components of the
#' continuous mixture variable; else a list of length \code{k_mix} with i-th component a list of vectors of sixth cumulant correction values
#' for component distributions of \eqn{Y_{mix_i}}; use \code{NULL} if no correction is desired for a given component or
#' mixture variable; if no correction is desired for any of the \eqn{Y_{mix}} keep as \code{mix_Six = list()}
#' (not necessary for \code{method} = "Fleishman")
#' @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 lam a vector of lambda (mean > 0) constants for the Poisson variables (see \code{stats::dpois}); the order should be
#' 1st regular Poisson variables, 2nd zero-inflated Poisson variables
#' @param p_zip a vector of probabilities of structural zeros (not including zeros from the Poisson distribution) for the
#' zero-inflated Poisson variables (see \code{VGAM::dzipois}); if \code{p_zip} = 0, \eqn{Y_{pois}} has a regular Poisson
#' distribution; if \code{p_zip} is in (0, 1), \eqn{Y_{pois}} has a zero-inflated Poisson distribution;
#' if \code{p_zip} is in \code{(-(exp(lam) - 1)^(-1), 0)}, \eqn{Y_{pois}} has a zero-deflated Poisson distribution and \code{p_zip}
#' is not a probability; if \code{p_zip = -(exp(lam) - 1)^(-1)}, \eqn{Y_{pois}} has a positive-Poisson distribution
#' (see \code{VGAM::dpospois}); if \code{length(p_zip) < length(lam)}, the missing values are set to 0 (and ordered 1st)
#' @param size a vector of size parameters for the Negative Binomial variables (see \code{stats::dnbinom}); the order should be
#' 1st regular NB variables, 2nd zero-inflated NB variables
#' @param prob a vector of success probability parameters for the NB variables; order the same as in \code{size}
#' @param mu a vector of mean parameters for the NB variables (*Note: either \code{prob} or \code{mu} should be supplied for all Negative Binomial variables,
#' not a mixture; default = NULL); order the same as in \code{size}; for zero-inflated NB this refers to
#' the mean of the NB distribution (see \code{VGAM::dzinegbin})
#' @param p_zinb a vector of probabilities of structural zeros (not including zeros from the NB distribution) for the zero-inflated NB variables
#' (see \code{VGAM::dzinegbin}); if \code{p_zinb} = 0, \eqn{Y_{nb}} has a regular NB distribution;
#' if \code{p_zinb} is in \code{(-prob^size/(1 - prob^size),} \code{0)}, \eqn{Y_{nb}} has a zero-deflated NB distribution and \code{p_zinb}
#' is not a probability; if \code{p_zinb = -prob^size/(1 - prob^size)}, \eqn{Y_{nb}} has a positive-NB distribution (see
#' \code{VGAM::dposnegbin}); if \code{length(p_zinb) < length(size)}, the missing values are set to 0 (and ordered 1st)
#' @param pois_eps a vector of length \code{k_pois} containing total cumulative probability truncation values; if none are provided,
#' the default is 0.0001 for each variable
#' @param nb_eps a vector of length \code{k_nb} containing total cumulative probability truncation values; if none are provided,
#' the default is 0.0001 for each variable
#' @param rho the target correlation matrix which must be ordered
#' \emph{1st ordinal, 2nd continuous non-mixture, 3rd components of continuous mixtures, 4th regular Poisson, 5th zero-inflated Poisson,
#' 6th regular NB, 7th zero-inflated NB}; note that \code{rho} is specified in terms of the components of \eqn{Y_{mix}}
#' @param Sigma an intermediate correlation matrix to use if the user wants to provide one, else it is calculated within by
#' \code{\link[SimCorrMix]{intercorr}}
#' @param cstart a list of length equal to \code{k_cont} + the total number of mixture components containing initial values for root-solving
#' algorithm used in \code{\link[SimMultiCorrData]{find_constants}}. If user specified, each list element must be input as a matrix.
#' For \code{method} = "Fleishman", each should have 3 columns for \eqn{c1, c2, c3};
#' for \code{method} = "Polynomial", each should have 5 columns for \eqn{c1, c2, c3, c4, c5}. If no starting values are specified for
#' a given component, that list element should be \code{NULL}.
#' @param quiet if FALSE prints messages, if TRUE suppresses message printing
#' @importFrom stats cor dbeta dbinom dchisq density dexp df dgamma dlnorm dlogis dmultinom dnbinom dnorm dpois dt dunif dweibull ecdf
#' median pbeta pbinom pchisq pexp pf pgamma plnorm plogis pnbinom pnorm ppois pt punif pweibull qbeta qbinom qchisq qexp qf qgamma
#' qlnorm qlogis qnbinom qnorm qpois qt quantile qunif qweibull rbeta rbinom rchisq rexp rf rgamma rlnorm rlogis rmultinom rnbinom
#' rnorm rpois rt runif rweibull sd uniroot var
#' @import utils
#' @export
#' @return TRUE if all inputs are correct, else it will stop with a correction message
#' @keywords ParameterCheck
#' @seealso \code{\link[SimCorrMix]{contmixvar1}}, \code{\link[SimCorrMix]{corrvar}}, \code{\link[SimCorrMix]{corrvar2}},
#' \code{\link[SimCorrMix]{validcorr}}, \code{\link[SimCorrMix]{validcorr2}}
#' @examples
#' validpar(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)), rho = matrix(c(1, 0.4, 0.4, 1), 2, 2),
#' quiet = TRUE)
#' \dontrun{
#' # 2 continuous mixture, 1 binary, 1 zero-inflated Poisson, and
#' # 1 zero-inflated NB variable
#'
#' # Mixture variables: Normal mixture with 2 components;
#' # mixture of Logistic(0, 1), Chisq(4), Beta(4, 1.5)
#' # Find cumulants of components of 2nd mixture variable
#' L <- calc_theory("Logistic", c(0, 1))
#' C <- calc_theory("Chisq", 4)
#' B <- calc_theory("Beta", c(4, 1.5))
#'
#' skews <- skurts <- fifths <- sixths <- NULL
#' Six <- list()
#' mix_pis <- list(c(0.4, 0.6), c(0.3, 0.2, 0.5))
#' mix_mus <- list(c(-2, 2), c(L[1], C[1], B[1]))
#' mix_sigmas <- list(c(1, 1), c(L[2], C[2], B[2]))
#' mix_skews <- list(rep(0, 2), c(L[3], C[3], B[3]))
#' mix_skurts <- list(rep(0, 2), c(L[4], C[4], B[4]))
#' mix_fifths <- list(rep(0, 2), c(L[5], C[5], B[5]))
#' mix_sixths <- list(rep(0, 2), c(L[6], C[6], B[6]))
#' mix_Six <- list(list(NULL, NULL), list(1.75, NULL, 0.03))
#' Nstcum <- calc_mixmoments(mix_pis[[1]], mix_mus[[1]], mix_sigmas[[1]],
#' mix_skews[[1]], mix_skurts[[1]], mix_fifths[[1]], mix_sixths[[1]])
#' Mstcum <- calc_mixmoments(mix_pis[[2]], mix_mus[[2]], mix_sigmas[[2]],
#' mix_skews[[2]], mix_skurts[[2]], mix_fifths[[2]], mix_sixths[[2]])
#' means <- c(Nstcum[1], Mstcum[1])
#' vars <- c(Nstcum[2]^2, Mstcum[2]^2)
#'
#' marginal <- list(0.3)
#' support <- list(c(0, 1))
#' lam <- 0.5
#' p_zip <- 0.1
#' size <- 2
#' prob <- 0.75
#' p_zinb <- 0.2
#'
#' k_cat <- k_pois <- k_nb <- 1
#' k_cont <- 0
#' k_mix <- 2
#' Rey <- matrix(0.39, 8, 8)
#' diag(Rey) <- 1
#' rownames(Rey) <- colnames(Rey) <- c("O1", "M1_1", "M1_2", "M2_1", "M2_2",
#' "M2_3", "P1", "NB1")
#'
#' # set correlation between components of the same mixture variable to 0
#' Rey["M1_1", "M1_2"] <- Rey["M1_2", "M1_1"] <- 0
#' Rey["M2_1", "M2_2"] <- Rey["M2_2", "M2_1"] <- Rey["M2_1", "M2_3"] <- 0
#' Rey["M2_3", "M2_1"] <- Rey["M2_2", "M2_3"] <- Rey["M2_3", "M2_2"] <- 0
#'
#' # use before contmixvar1 with 1st mixture variable:
#' # change mix_pis to not sum to 1
#'
#' check1 <- validpar(k_mix = 1, method = "Polynomial", means = Nstcum[1],
#' vars = Nstcum[2]^2, mix_pis = C(0.4, 0.5), mix_mus = mix_mus[[1]],
#' mix_sigmas = mix_sigmas[[1]], mix_skews = mix_skews[[1]],
#' mix_skurts = mix_skurts[[1]], mix_fifths = mix_fifths[[1]],
#' mix_sixths = mix_sixths[[1]])
#'
#' # use before validcorr: should return TRUE
#'
#' check2 <- validpar(k_cat, k_cont, k_mix, k_pois, k_nb, "Polynomial", means,
#' vars, skews, skurts, fifths, sixths, Six, mix_pis, mix_mus, mix_sigmas,
#' mix_skews, mix_skurts, mix_fifths, mix_sixths, mix_Six, marginal, support,
#' lam, p_zip, size, prob, mu = NULL, p_zinb, rho = Rey)
#'
#' }
#'
validpar <- function(k_cat = 0, k_cont = 0, k_mix = 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(), mix_pis = list(), mix_mus = list(),
mix_sigmas = list(), mix_skews = list(),
mix_skurts = list(), mix_fifths = list(),
mix_sixths = list(), mix_Six = list(),
marginal = list(), support = list(), lam = NULL,
p_zip = 0, size = NULL, prob = NULL, mu = NULL,
p_zinb = 0, pois_eps = 0.0001, nb_eps = 0.0001,
rho = NULL, Sigma = NULL, cstart = list(),
quiet = FALSE) {
if (k_cat > 0) {
if (k_cat != length(marginal))
stop("Length of marginal does not match the number of ordinal
variables.")
if (!all(unlist(lapply(marginal,
function(x) (sort(x) == x & min(x) > 0 & max(x) < 1)))))
stop("Error in given marginal distributions.")
if (length(support) > 0 & (k_cat != length(support)))
stop("Length of support does not match the number of ordinal
variables.")
}
k_comp <- 0
if ((k_cont + k_mix) > 0) {
if (length(method) != 1)
stop("Choose a PMT method for the continuous variables.")
if (length(means) != (k_cont + k_mix) | length(vars) != (k_cont + k_mix))
stop("Length of means and vars should be k_cont + k_mix.")
if (k_cont > 0) {
if (length(skews) != k_cont | length(skurts) != k_cont)
stop("Parameters for continuous non-mixture distributions should be
vectors of length k_cont.")
if (method == "Polynomial") {
if (length(fifths) != k_cont | length(sixths) != k_cont |
(length(Six) != 0 & length(Six) != k_cont))
stop("Parameters for continuous non-mixture distributions should be
vectors of length k_cont. Six should be either list() or
a list of length k_cont.")
}
}
if (k_mix > 0) {
if (class(mix_pis) == "list") {
k_comp <- lengths(mix_pis)
if (length(mix_pis) != k_mix | length(mix_mus) != k_mix |
length(mix_sigmas) != k_mix | length(mix_skews) != k_mix |
length(mix_skurts) != k_mix)
stop("Parameters for continuous mixture distributions should be
lists of length equal to k_mix.")
if (!all(lengths(mix_mus) %in% k_comp) |
!all(lengths(mix_sigmas) %in% k_comp) |
!all(lengths(mix_skews) %in% k_comp) |
!all(lengths(mix_skurts) %in% k_comp))
stop("Components of mixture parameter lists should be the same
length as components of mix_pis.")
if (method == "Polynomial") {
if (length(mix_fifths) != k_mix | length(mix_sixths) != k_mix |
(length(mix_Six) != 0 & length(mix_Six) != k_mix))
stop("Parameters for continuous mixture distributions should be
lists of length equal to k_mix. mix_Six should be either
list() or a list of length k_mix.")
if (!all(lengths(mix_fifths) %in% k_comp) |
!all(lengths(mix_sixths) %in% k_comp))
stop("Components of mixture parameter lists should be the same
length as components of mix_pis.")
}
if (all.equal(unlist(lapply(mix_pis, sum)), rep(1, k_mix)) == FALSE)
stop("Mixing parameters should sum to 1 for each variable.")
}
if (class(mix_pis) == "numeric") {
if (k_mix > 1 | k_cat > 0 | k_pois > 0 | k_nb > 0)
stop("Mixture parameters should be lists of length equal to k_mix.")
if (sum(mix_pis) != 1)
stop("Mixing parameters should sum to 1.")
k_comp <- length(mix_pis)
if (length(mix_mus) != k_comp | length(mix_sigmas) != k_comp |
length(mix_skews) != k_comp | length(mix_skurts) != k_comp)
stop("Mixture parameters should have same length as mix_pis.")
if (method == "Polynomial") {
if (length(mix_fifths) != k_comp | length(mix_sixths) != k_comp |
!(length(mix_Six) %in% c(0, k_comp)))
stop("Mixture parameters should have same length as mix_pis.")
}
}
}
if (length(cstart) != 0) {
if (length(cstart) != (k_cont + k_comp))
stop("Length of cstart should be k_cont + number of mixture
components.")
k.c <- unlist(lapply(cstart, ncol))
if (method == "Fleishman") {
if (!all.equal(k.c, rep(3, length(cstart))))
stop("Dimension of cstart matrices should be number of starting
values by 3")
} else {
if (!all.equal(k.c, rep(5, length(cstart))))
stop("Dimension of cstart matrices should be number of starting
values by 5")
}
}
}
if (k_pois > 0) {
if (k_pois != length(lam))
stop("Length of lam does not match the number of Poisson variables.")
if (sum(lam < 0) > 0)
stop("Lambda values cannot be negative.")
if (length(p_zip) < k_pois & quiet == FALSE)
message("Default of p_zip = 0 will be used for Poisson variables.")
if (length(pois_eps) < k_pois & quiet == FALSE)
message("Default of pois_eps = 0.0001 will be used for Poisson variables
if using correlation method 2.")
}
if (k_nb > 0) {
if (k_nb != length(size))
stop("Length of size does not match the number of NB variables.")
if (length(prob) > 0 & length(mu) > 0)
stop("Either give success probabilities or means for NB variables.")
if (length(prob) > 0 & k_nb != length(prob))
stop("Length of prob does not match the number of NB variables.")
if (length(mu) > 0 & k_nb != length(mu))
stop("Length of mu does not match the number of NB variables.")
if (length(p_zinb) < k_nb & quiet == FALSE)
message("Default of p_zinb = 0 will be used for NB variables.")
if (length(nb_eps) < k_nb & quiet == FALSE)
message("Default of nb_eps = 0.0001 will be used for NB variables
if using correlation method 2.")
}
k <- k_cat + k_cont + sum(k_comp) + k_pois + k_nb
if (!is.null(Sigma)) {
if (ncol(Sigma) != k | nrow(Sigma) != k)
stop("Sigma matrix is not of the right dimension.")
if (!isSymmetric(Sigma) | !all(diag(Sigma) == 1))
stop("Sigma matrix not valid! Check symmetry and diagonal values.")
if (min(eigen(Sigma, symmetric = TRUE)$values) < 0 & quiet == FALSE)
message("Sigma matrix is not positive-definite. Nearest
positive-definite matrix will be used if use.nearPD = TRUE.")
}
if (!is.null(rho)) {
if (ncol(rho) != k | nrow(rho) != k)
stop("Correlation matrix is not of the right dimension.")
if (!isSymmetric(rho) | !all(diag(rho) == 1))
stop("Correlation matrix not valid! Check symmetry and diagonal
values.")
if (min(eigen(rho, symmetric = TRUE)$values) < 0 & quiet == FALSE)
message("Target correlation matrix is not positive definite.")
}
return(TRUE)
}
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