ModelMCMC: ModelFit object, output from sampling

In psadil/nmmr: NeuroModulation Modeling in R

Description

ModelFit object, output from sampling

ModelFit object, output from sampling

Public fields

standata

The original data that was used to prepare the model

cmdstanmcmc

CmdStanMCMC object

Methods

Public methods

• ModelMCMC$new()

• ModelMCMC$loo()

• ModelMCMC$make_vtf0()

• ModelMCMC$.lfun()

• ModelMCMC$.llfun()

• ModelMCMC$clone()

---

Method new()

Create a new ModelFit You probably don't want to call this

Usage

ModelMCMC$new(standata, cmdstanmcmc)

Arguments

standata

Data that were used to generate the samples

cmdstanmcmc

cmdstanr::CmdStanMCMC output by model

Returns

A new ModelFit object. Estimate the LOO score

---

Method loo()

Usage

ModelMCMC$loo(cores = 1)

Arguments

cores

int

Returns

A loo::psis_loo object create voxel tuning function posterior

---

Method make_vtf0()

Usage

ModelMCMC$make_vtf0(cores = 1)

Arguments

cores

integer number of cores over which to parallelize

Returns

posterior::draws_array, Likelihood function

---

Method .lfun()

Usage

ModelMCMC$.lfun(data_i, draws)

Arguments

data_i

single row of data

draws

posterior

Returns

The likelihood of a single observation Log-Likelihood function

---

Method .llfun()

Usage

ModelMCMC$.llfun(data_i, draws)

Arguments

data_i

single row of data

draws

posterior

Returns

The log-likelihood of a single observation

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ModelMCMC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

psadil/nmmr documentation built on June 13, 2021, 11:42 a.m.