jlike: Function to calculate joint likelihood used in the jointmeta1...
In joineRmeta: Joint Modelling for Meta-Analytic (Multi-Study) Data

Function to calculate joint likelihood used in the jointmeta1 function

jlike(
  data,
  longdat,
  survdat,
  q,
  likeests,
  lgpt,
  studies,
  p1,
  p2,
  long.rand.ind,
  randstart.ind,
  randstart.ind.cov,
  r = NULL,
  long.rand.stud = NULL,
  randstart.stud = NULL,
  randstart.stud.cov = NULL,
  strat,
  study.name,
  id.name
)

`data`	an object of class jointdata containing the variables named in the model formulae
`longdat`	the longitudinal data with factors and interaction terms expanded, ordered by increasing survival time
`survdat`	the survival data with factors and interaction terms expanded, ordered by increasing survival time
`q`	the number of individual level random effects
`likeests`	a list of values required to calculated the log-likelihood for the fitted joint model. This list has the following elements: `beta1` a data frame containing the estimates of the coefficients of the fixed effect parameters of the longitudinal sub-model. `beta2` a data frame containing the estimates of the coefficients of the fixed effect parameters of the survival sub-model. `sigma.e` the estimate of the variance for the measurement errors in the joint model. `D` the estimated covariance matrix for the individual level random effects `A` the estimated covariance matrix for the study level random effects. This is only present in the output if study level random effects were specified in the function call to `jointmeta1`. `random2` a list of matrices containing the conditional modes of the individual level random effects given the supplied data and the estimated parameters of the joint model. The list is of length equal to the number of studies in the dataset, and each element of the list has number of rows equal to the number of individuals in the study, and number of columns equal to the number of specified individual level random effects. `random3` a matrix containing the conditional modes of the study level random effects given the supplied data and the estimated parameters of the joint model. The matrix has number of rows equal to the number of studies, and number of columns equal to the number of specified study level random effects. `long.rand.ind.form` a character string giving the formulation of the individual level random effects. `long.rand.stud.form` a character string giving the formulation of the study level random effects if included in the model. `conv` a logical value indicating whether convergence of the EM algorithm was achieved or not. `iters` the number of iterations completed by the EM algorithm `n.bystudy` the number of individuals present in each study in the data supplied to the function. `haz` the estimated baseline hazard. If `strat = TRUE` in the function call to `jointmeta1` then this is a list of length equal to the number of studies in the supplied dataset, each element of the list being the baseline hazard for the corresponding study. Otherwise there is a common baseline across all studies in the dataset and this is one vector. `rs` a counter to indicate the last how many unique event times had occured by the individual's survival time - this is for use during further calculation in the joint model EM algorithm. If a stratified baseline this is a list of numerical vectors, whereas if the baseline is not stratified this is a single numeric vector. `sf` the unique event times observed in the dataset. If a stratified baseline this is a list of numerical vectors, whereas if the baseline is not stratified this is a single numeric vector.
`lgpt`	the number of quadrature points which the log-likelihood is evaluated over following a model fit. This defaults to `lgpt = 7`.
`studies`	the names of the studies present in the supplied data
`p1`	the number of fixed effects included in the longitudinal sub-model
`p2`	the number of fixed effects included in the survival sub-model
`long.rand.ind`	a vector of character strings to indicate what variables to assign individual level random effects to. A maximum of three individual level random effects can be assigned. To assign a random intercept include 'int' in the vector. To not include an individual level random intercept include 'noint' in the vector. For example to fit a model with individual level random intercept and random slope set `long.rand.ind = c('int', 'time')`, where `'time'` is the longitudinal time variable in the `data`.
`randstart.ind`	a list of the conditional modes of the individual level random effects in each study given the data and the estimates of the separate longitudinal model parameters
`randstart.ind.cov`	a list of the conditional covariance matrices for each individual for the individual level random effects given the data and the estimates of the separate longitudinal model parameters
`r`	the number of study level random effects (if included in the model)
`long.rand.stud`	a vector of character strings to indicate what variables to assign study level random effects to. If no study level random effects then this either not specified in function call or set to `NULL`. If a study level random intercept is required, include the name of the study membership variable for example `long.rand.stud = 'study'`.
`randstart.stud`	a data frame containing the conditional modes of the study level random effects given the data and the estimates of the separate longitudinal model parameters. This is only present if study level random effects were specified in the `jointmeta1` function call.
`randstart.stud.cov`	a list of conditional covariance matrices for each study for the study level random effects given the data and the estimates of the separate longitudinal model parameters. This is only present if study level random effects were specified in the `jointmeta1` function call.
`strat`	logical value: if `TRUE` then the survival sub-model is calculated with a baseline stratified by study. Otherwise baseline is unstratified
`study.name`	a character string denoting the name of the variable in the baseline dataset in `data` holding study membership, for example `study.name = 'study'`.
`id.name`	character string specifying the id variable in the dataset

A list containing three elements:

log.like: the overall log-likelihood for the fitted joint model.
longlog.like: the portion of the log-likelihood attributable to the longitudinal sub-model.
survlog.like: the portion of the log-likelihood attributable to the survival sub-model.