# Calculate Intermediate MVN Correlation for Ordinal, Continuous, Poisson, or Negative Binomial Variables: Correlation Method 2 This function calculates a `k x k` intermediate matrix of correlations, where `k = k_cat + k_cont +` `k_pois + k_nb`, to be used in simulating variables with `corrvar2`. The `k_cont` includes regular continuous variables and components of continuous mixture variables. The ordering of the variables must be ordinal, continuous non-mixture, components of continuous mixture variables, regular Poisson, zero-inflated Poisson, regular Negative Binomial (NB), and zero-inflated NB (note that it is possible for `k_cat`, `k_cont`, `k_pois`, and/or `k_nb` to be 0). There are no parameter input checks in order to decrease simulation time. All inputs should be checked prior to simulation with `validpar`. There is a message given if the calculated intermediate correlation matrix `Sigma` is not positive-definite because it may not be possible to find a MVN correlation matrix that will produce the desired marginal distributions. This function is called by the simulation function `corrvar2`, and would only be used separately if the user wants to first find the intermediate correlation matrix. This matrix `Sigma` can be used as an input to `corrvar2`. Please see the Comparison of Correlation Methods 1 and 2 vignette for information about calculations by variable pair type and the differences between this function and `intercorr`. ```r intercorr2(k_cat = 0, k_cont = 0, k_pois = 0, k_nb = 0, method = c("Fleishman", "Polynomial"), constants = NULL, 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, epsilon = 0.001, maxit = 1000, quiet = FALSE) ``` ## Arguments - `k_cat`: the number of ordinal (r >= 2 categories) variables (default = 0) - `k_cont`: the number of continuous non-mixture variables and components of continuous mixture variables (default = 0) - `k_pois`: the number of regular and zero-inflated Poisson variables (default = 0) - `k_nb`: the number of regular and zero-inflated Negative Binomial variables (default = 0) - `method`: the method used to generate the `k_cont` continuous variables. "Fleishman" uses a third-order polynomial transformation and "Polynomial" uses Headrick's fifth-order transformation. - `constants`: a matrix with `k_cont` rows, each a vector of constants c0, c1, c2, c3 (if `method` = "Fleishman") or c0, c1, c2, c3, c4, c5 (if `method` = "Polynomial") like that returned by `find_constants` - `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()) - `support`: a list of length equal to `k_cat`; the i-th element is a vector of 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 regular and zero-inflated 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 $Y_{mix}$ - `epsilon`: the maximum acceptable error between the pairwise correlations (default = 0.001) in the calculation of ordinal intermediate correlations with `ord_norm` - `maxit`: the maximum number of iterations to use (default = 1000) in the calculation of ordinal intermediate correlations with `ord_norm` - `quiet`: if FALSE prints simulation messages, if TRUE suppresses message printing ## Returns the intermediate MVN correlation matrix ## Examples ```r Sigma1 <- intercorr2(k_cat = 1, k_cont = 1, method = "Polynomial", constants = matrix(c(0, 1, 0, 0, 0, 0), 1, 6), marginal = list(0.3), support = list(c(0, 1)), rho = matrix(c(1, 0.4, 0.4, 1), 2, 2), quiet = TRUE) ## Not run: # 1 continuous mixture, 1 binary, 1 zero-inflated Poisson, and # 1 zero-inflated NB variable # The defaults of pois_eps <- nb_eps <- 0.0001 are used. # Mixture of N(-2, 1) and N(2, 1) constants <- rbind(c(0, 1, 0, 0, 0, 0), c(0, 1, 0, 0, 0, 0)) 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 <- 2 Rey <- matrix(0.35, 5, 5) diag(Rey) <- 1 rownames(Rey) <- colnames(Rey) <- c("O1", "M1_1", "M1_2", "P1", "NB1") # set correlation between components of the same mixture variable to 0 Rey["M1_1", "M1_2"] <- Rey["M1_2", "M1_1"] <- 0 Sigma2 <- intercorr2(k_cat, k_cont, k_pois, k_nb, "Polynomial", constants, marginal, support, lam, p_zip, size, prob, mu = NULL, p_zinb, rho = Rey) ## End(Not run) ``` ## References Please see references for `SimCorrMix`. ## See Also `corrvar2`

intercorr2 function