CInLPN.default: Function that start estimation and prediction tasks
In bachirtadde/CInLPN: Causal Inference for Latent Processes Network

CInLPN.default

R Documentation

Function that start estimation and prediction tasks

Description

Function that start estimation and prediction tasks

Usage

CInLPN.default(fixed_X0.models, fixed_DeltaX.models, randoms_X0.models,
  randoms_DeltaX.models, mod_trans.model, DeltaT, outcomes, predictors, nD,
  mapping.to.LP, link, knots = NULL, subject, rdata, Time, makepred,
  MCnr, paras.ini = NULL, indexparaFixeUser, paraFixeUser, maxiter,
  univarmaxiter, nproc = 1, epsa = 1e-04, epsb = 1e-04,
  epsd = 0.001, print.info = FALSE,
  TimeDiscretization = TimeDiscretization, ...)

Arguments

`fixed_X0.models`	fixed effects in the submodel for the baseline level of processes
`fixed_DeltaX.models`	a two-sided linear formula object for specifying the response outcomes (one the left part of ~ symbol) and the covariates with fixed-effects (on the right part of ~ symbol) in the submodel for change over time of latent processes
`randoms_X0.models`	random effects in the submodel for the baseline level of processes
`randoms_DeltaX.models`	random effects in the submodel for change over time of latent processes
`mod_trans.model`	model for elements of the temporal transition matrix, which captures the temporal influences between latent processes
`DeltaT`	indicates the discretization step
`outcomes`	indicates names of the outcomes
`predictors`	all explicative variables of the model
`nD`	number of the latent processes
`mapping.to.LP`	indicates which outcome measured which latent process, it is a mapping table between outcomes and latents processes
`link`	indicates link used to transform outcome
`knots`	indicates position of knots used to transform outcomes
`subject`	indicates the name of the covariate representing the grouping structure
`rdata`	indicates the row data frame containing all the variables to estimate the model
`Time`	indicates the name of the covariate representing the time
`makepred`	indicates if predictions in the real scales of outcomes have to be done
`MCnr`	number of replicates to compute the predictions in the real scales of the outcomes
`paras.ini`	initial values for parameters, default values is NULL
`indexparaFixeUser`	position of parameters to be constrained
`paraFixeUser`	values associated to the index of parameters to be constrained
`maxiter`	maximum iteration
`univarmaxiter`	maximum iteration for estimating univariate model
`nproc`	number of processor to be used for running this package, default value is 1
`epsa`	threshold for the convergence criterion on the parameters, default value is 1.e-4
`epsb`	threshold for the convergence criterion on the likelihood, default value is 1.e-4
`epsd`	threshold for the convergence criterion on the derivatives, default value is 1.e-3
`print.info`	to print information during the liklihood optimization, default value is FALSE
`TimeDiscretization`	a boolean indicating if the inital time have to be discretized. When setting to FALSE, It allows to avoid discretization when running univarite model during parameter initialization.
`...`	optional parameters