fit: Function that computes the fits (marginal and...
In bachirtadde/CInLPN: Causal Inference for Latent Processes Network

View source: R/RcppExports.R

fit	R Documentation

Function that computes the fits (marginal and subject-specific) for individuals. That is observations are available and from them, fits will be compute from the model. The difference between fits and predictions is that, for predictions there is no observation where as for fit observations are available.

Description

Function that computes the fits (marginal and subject-specific) for individuals. That is observations are available and from them, fits will be compute from the model. The difference between fits and predictions is that, for predictions there is no observation where as for fit observations are available.

Usage

fit(K, nD, mapping, paras, m_is, Mod_MatrixY, df, x, z, q, nb_paraD, x0,
  z0, q0, if_link, tau, tau_is, modA_mat, DeltaT, MCnr, minY, maxY, knots,
  degree, epsPred)

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
`paras`	values of model parameters
`m_is`	vector of numbers of visit occasions for individuals
`Mod_MatrixY`	model.matrix from 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
`MCnr`	an integer that indicates the number of sample for MC method
`minY`	a vector of minima of outcomes
`maxY`	a vector of maxima of outcomes
`knots`	indicates position of knots used to transform outcomes
`degree`	indicates degree of the basis of splines
`epsPred`	convergence criteria for prediction using MC method