Loglik: Function that computes the log-likelihood of the observed...
In bachirtadde/CInLPN: Causal Inference for Latent Processes Network

Loglik

R Documentation

Function that computes the log-likelihood of the observed data

Description

Function that computes the log-likelihood of the observed data

Usage

Loglik(K, nD, mapping, paraOpt, paraFixe, posfix, m_is, Mod_MatrixY,
  Mod_MatrixYprim, df, x, z, q, nb_paraD, x0, z0, q0, if_link, tau, tau_is,
  modA_mat, DeltaT)

Arguments

`K`	an integer indicating the number of markers
`nD`	an integer indicating the number of latent processes
`mapping`	indicates which outcome measured which latent process, it is a mapping table between outcomes and latents processes
`paraOpt`	initial values for model parameters
`paraFixe`	values associated to the index of parameters to be constrained
`posfix`	position of parameters to be constrained
`m_is`	vector of numbers of visit occasions for individuals
`Mod_MatrixY`	model.matrix from markers transformation submodels
`Mod_MatrixYprim`	model.matrix from the derivates of markers transformation submodels
`df`	vector of numbers of parameters for each transformation model
`x`	model.matrix for change's fixed submodel
`z`	model.matrix for change's random effects submodel
`q`	a vector of number of random effects on each change latent process over time
`nb_paraD`	number of paramerters of the variance-covariance matrix of random effects
`x0`	model.matrix for baseline's fixed submodel
`z0`	model.matrix for baseline's random effects submodel
`q0`	a vector of number of random effects on each initial latent process level
`if_link`	indicates if non linear link is used to transform an outcome
`tau`	a vector of integers indicating times (including maximum time)
`tau_is`	a vector of integers indicating times for individuals
`modA_mat`	model.matrix for elements of the transistion matrix
`DeltaT`	double that indicates the discretization step