validpar {SimCorrMix}

Parameter Check for Simulation or Correlation Validation Functions

Description

This function checks the parameter inputs to the simulation functions contmixvar1, corrvar, and corrvar2 and to the correlation validation functions validcorr and 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.

Usage

validpar(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)

Arguments

k_cat

the number of ordinal (r >= 2 categories) variables (default = 0)

k_cont

the number of continuous non-mixture variables (default = 0)

k_mix

the number of continuous mixture variables (default = 0)

k_pois

the number of regular Poisson and zero-inflated Poisson variables (default = 0)

k_nb

the number of regular Negative Binomial and zero-inflated Negative Binomial variables (default = 0)

method

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

means

a vector of means for the k_cont non-mixture and k_mix mixture continuous variables (i.e. rep(0, (k_cont + k_mix)))

vars

a vector of variances for the k_cont non-mixture and k_mix mixture continuous variables (i.e. rep(1, (k_cont + k_mix)))

skews

a vector of skewness values for the k_cont non-mixture continuous variables

skurts

a vector of standardized kurtoses (kurtosis - 3, so that normal variables have a value of 0) for the k_cont non-mixture continuous variables

fifths

a vector of standardized fifth cumulants for the k_cont non-mixture continuous variables (not necessary for method = "Fleishman")

sixths

a vector of standardized sixth cumulants for the k_cont non-mixture continuous variables (not necessary for method = "Fleishman")

Six

a list of vectors of sixth cumulant correction values for the k_cont non-mixture continuous variables if no valid PDF constants are found,
ex: Six = list(seq(0.01, 2, 0.01), seq(1, 10, 0.5)); if no correction is desired for variable Y_{cont_i}, set set the i-th list component equal to NULL; if no correction is desired for any of the Y_{cont} keep as Six = list() (not necessary for method = "Fleishman")

mix_pis

a vector if using contmixvar1 or a list of length k_mix with i-th component a vector of mixing probabilities that sum to 1 for component distributions of Y_{mix_i}

mix_mus

a vector if using contmixvar1 or a list of length k_mix with i-th component a vector of means for component distributions of Y_{mix_i}

mix_sigmas

a vector if using contmixvar1 or a list of length k_mix with i-th component a vector of standard deviations for component distributions of Y_{mix_i}

mix_skews

a vector if using contmixvar1 or a list of length k_mix with i-th component a vector of skew values for component distributions of Y_{mix_i}

mix_skurts

a vector if using contmixvar1 or a list of length k_mix with i-th component a vector of standardized kurtoses for component distributions of Y_{mix_i}

mix_fifths

a vector if using contmixvar1 or a list of length k_mix with i-th component a vector of standardized fifth cumulants for component distributions of Y_{mix_i} (not necessary for method = "Fleishman")

mix_sixths

a vector if using contmixvar1 or a list of length k_mix with i-th component a vector of standardized sixth cumulants for component distributions of Y_{mix_i} (not necessary for method = "Fleishman")

mix_Six

if using contmixvar1, a list of vectors of sixth cumulant corrections for the components of the continuous mixture variable; else a list of length k_mix with i-th component a list of vectors of sixth cumulant correction values for component distributions of Y_{mix_i}; use NULL if no correction is desired for a given component or mixture variable; if no correction is desired for any of the Y_{mix} keep as mix_Six = list() (not necessary for method = "Fleishman")

marginal

a list of length equal to 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)

support

a list of length equal to k_cat; the i-th element is a vector containing the r ordered support values; if not provided (i.e. support = list()), the default is for the i-th element to be the vector 1, ..., r

lam

a vector of lambda (mean > 0) constants for the Poisson variables (see stats::dpois); the order should be 1st regular Poisson variables, 2nd zero-inflated Poisson variables

p_zip

a vector of probabilities of structural zeros (not including zeros from the Poisson distribution) for the zero-inflated Poisson variables (see VGAM::dzipois); if p_zip = 0, Y_{pois} has a regular Poisson distribution; if p_zip is in (0, 1), Y_{pois} has a zero-inflated Poisson distribution; if p_zip is in (-(exp(lam) - 1)^(-1), 0), Y_{pois} has a zero-deflated Poisson distribution and p_zip is not a probability; if p_zip = -(exp(lam) - 1)^(-1), Y_{pois} has a positive-Poisson distribution (see VGAM::dpospois); if length(p_zip) < length(lam), the missing values are set to 0 (and ordered 1st)

size

a vector of size parameters for the Negative Binomial variables (see stats::dnbinom); the order should be 1st regular NB variables, 2nd zero-inflated NB variables

prob

a vector of success probability parameters for the NB variables; order the same as in size

mu

a vector of mean parameters for the NB variables (*Note: either prob or mu should be supplied for all Negative Binomial variables, not a mixture; default = NULL); order the same as in size; for zero-inflated NB this refers to the mean of the NB distribution (see VGAM::dzinegbin)

p_zinb

a vector of probabilities of structural zeros (not including zeros from the NB distribution) for the zero-inflated NB variables (see VGAM::dzinegbin); if p_zinb = 0, Y_{nb} has a regular NB distribution; if p_zinb is in (-prob^size/(1 - prob^size), 0), Y_{nb} has a zero-deflated NB distribution and p_zinb is not a probability; if p_zinb = -prob^size/(1 - prob^size), Y_{nb} has a positive-NB distribution (see VGAM::dposnegbin); if length(p_zinb) < length(size), the missing values are set to 0 (and ordered 1st)

pois_eps

a vector of length k_pois containing total cumulative probability truncation values; if none are provided, the default is 0.0001 for each variable

nb_eps

a vector of length k_nb containing total cumulative probability truncation values; if none are provided, the default is 0.0001 for each variable

rho

the target correlation matrix which must be ordered 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 rho is specified in terms of the components of Y_{mix}

Sigma

an intermediate correlation matrix to use if the user wants to provide one, else it is calculated within by intercorr

cstart

a list of length equal to k_cont + the total number of mixture components containing initial values for root-solving algorithm used in find_constants. If user specified, each list element must be input as a matrix. For method = "Fleishman", each should have 3 columns for c1, c2, c3; for method = "Polynomial", each should have 5 columns for c1, c2, c3, c4, c5. If no starting values are specified for a given component, that list element should be NULL.

quiet

if FALSE prints messages, if TRUE suppresses message printing

Value

TRUE if all inputs are correct, else it will stop with a correction message

See Also

contmixvar1, corrvar, corrvar2, validcorr, 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)
## Not run: 
# 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)


## End(Not run)