condAIC: Conditional AIC (cAIC)

In RestoreNet: Random-Effects Stochastic Reaction Networks

Conditional AIC (cAIC)

Description

Conditional AIC (cAIC) of the conditional log-likelihood l(y \vert u) of y given the random effects u

Usage

condAIC(X, Z, y, theta, Delta, V, VCNs, nObs, verbose = TRUE)

Arguments

X	A n \times K dimensional (design) matrix.
Z	An \times Jp dimensional block-diagonal design matrix. Each j-th block (j = 1,\dots,J) is a n_j \times p dimensional design matrix for the j-th clone.
y	n-dimensional vector of the time-adjacent cellular increments
theta	p-dimensional vector parameter.
Delta	covariance matrix of the random effects u
V	A p \times K dimensional net-effect matrix.
VCNs	A n-dimensional vector including values of the vector copy number corresponding to the cell counts of y.
nObs	A K-dimensional vector including the frequencies of each clone k (k = 1,\dots,K).
verbose	(defaults to TRUE) Logical value. If TRUE, then information messages on the progress of the algorithm are printed to the console.

Value

Conditional AIC (cAIC) of the conditional log-likelihood l(y \vert u) of y given the random effects u.

RestoreNet documentation built on May 29, 2024, 4 a.m.