bizicount (version 1.3.3)

make_DHARMa: DHARMa-class objects from bizicount models

Description

A wrapper around the DHARMa package's createDHARMa function. Creates a list of DHARMa objects, one for each margin of a bizicount-class object, using simulated responses from simulate.bizicount.

Usage

make_DHARMa(object, nsim = 250, seed = 123, method = "PIT")

Value

A list of DHARMa objects.

Arguments

object

A bizicount-class object, as returned by bizicount.

nsim

Number of simulated responses from the fitted model to use for diagnostics.

seed

Random seed for simulating from fitted model.

method

See createDHARMa.

Author

John Niehaus

References

Florian Hartig (2022). DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.4.5. https://CRAN.R-project.org/package=DHARMa

See Also

DHARMa, createDHARMa, simulate.bizicount

Examples

## SETUP
set.seed(123)
n = 100

# define a function to simulate from a gaussian copula
# first margin is zero-inflated negative binomial (zinb)
# second margin is zero-inflated poisson (zip)
# Note: marginal distributions are hard-coded in function, including
# inverse dispersion parameter for zinb.
gen = function(n, b1, b2, g1, g2, dep) {

     k1 = length(b1)
     k2 = length(b2)

     X1 = cbind(1, matrix(rbinom(n * (k1 - 1), 1, .5), ncol = k1 - 1))
     X2 = cbind(1, matrix(rexp(n * (k2 - 1), 3), ncol = k2 - 1))

     lam1 = exp(X1 %*% b1)
     lam2 = exp(X2 %*% b2)

     Z1 = cbind(1, matrix(runif(n * (k1 - 1), -1, 1), ncol = k1 - 1))
     Z2 = cbind(1, matrix(rnorm(n * (k2 - 1)), ncol = k2 - 1))

     psi1 = plogis(Z1 %*% g1)
     psi2 = plogis(Z2 %*% g2)

     norm_vars = MASS::mvrnorm(
          n,
          mu = c(0, 0),
          Sigma = matrix(c(1, dep, dep, 1), ncol =2)
     )

     U = pnorm(norm_vars)

     y1 =  qzinb(U[, 1],
                 mu = lam1,
                 psi = psi1,
                 size = .3)
     y2 =  qzip(U[, 2],
                lambda = lam2,
                psi = psi2)

     dat = data.frame(
          X1 = X1[, -1],
          X2 = X2[, -1],
          Z1 = Z1[, -1],
          Z2 = Z2[, -1],
          y1,
          y2,
          lam1,
          lam2,
          psi1,
          psi2
     )
     return(dat)
}


# define parameters
b1 = c(1, -2, 3)
b2 = c(-1, 3, 1)
g1 = c(2, -1.5, 2)
g2 = c(-1, -3.75, 1.25)
rho = .5


# generate data
dat = gen(n, b1, b2, g1, g2, rho)
f1 = y1 ~ X1.1 + X1.2 | Z1.1 + Z1.2
f2 = y2 ~ X2.1 + X2.2 | Z2.1 + Z2.2

## END SETUP




# estimate model

mod = bizicount(f1, f2, dat, cop = "g", margins = c("zinb", "zip"), keep=TRUE)


# diagnose model with DHARMa
# see end for simulate.bizicount example.

dharm = make_DHARMa(mod, nsim = 100)

lapply(dharm, DHARMa::testResiduals)