Nothing

```
# This function is used with optim to maximize the Q function.
# The paraters in 'pars' are: beta and sigma.
# df: kL dimensional vector of degrees of freedom.
# u: Matrix of MCMC ouput for the random effects.
# sigmaType: Type of each covariance matrix:
# 0 - Diagonal
# 1 - Exchangeable
# 2 - AR(1)
# sigmaDim: Dimensions of the sigma matrices.
# kKi: Dimension of each variance component vector. Length equal to kR.
# kLh: Number of subvariance components within each variance components. The
# subvariance components share a covariance matrix. Length equal to kR.
# KLhi: Number of random effects in each subvariance component.
# kY, kX, kZ: Data and design matrices.
toMaxDiagPoisson_t <- function(pars, df, u, sigmaType, kKi, kLh, kLhi, kY, kX, kZ) {
kP <- ncol(kX) # Number of fixed coefficients
kR <- length(kKi) # Number of variance components, this is the number of sigma matrices
kK <- ncol(kZ) # Number of random effects
kL <- sum(kLh) # Number of subvariance components
beta <- pars[1:kP]
s0 <- length(pars[-(1:kP)]) # Number of variance parameters
# We call ovSigma the overall covariance matrix.
ovSigma <- constructSigma(pars = pars[-(1:kP)], sigmaType = sigmaType, kK = kK, kR = kR, kLh = kLh, kLhi = kLhi)
if (min(eigen(ovSigma)$values) <= 0) {
return(list(value = -Inf, gradient = rep(0, length(pars)), hessian = matrix(0, length(pars), length(pars))))
}
return(qFunctionDiagPoissonCpp_t(beta = beta, sigma = ovSigma, sigmaType = sigmaType, u = u, df = df, kKi = kKi, kLh = kLh, kLhi = kLhi, kY, kX, kZ))
}
```

**Any scripts or data that you put into this service are public.**

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.