summed_log_likelihood: Sum and Log Probability Density of a EAM model

In TasCL/ggdmc: Dynamic Model of Choice with Parallel Computation, and C++ Capabilities

Description

Get log likelihood summed over a model data instance. The input data has to be a data frame carrying a model specification, which is usually created by model.data.dmc function. summed_log_likelihood_parallel does calculation in parallel. Use it when only the data set is big.

Usage

summed_log_likelihood(pVec, data)

summed_log_likelihood_parallel(pVec, data)

Arguments

pVec	a parameter vector
data	a model data instance argument.

Value

a double scalar

Examples

m1 <- model.dmc(
  p.map     = list(a="1", v="1", z="1", d="1", sz="1", sv="1", t0="1", st0="1"),
  constants = c(st0=0, d=0),
  match.map = list(M=list(s1="r1", s2="r2")),
  factors   = list(S=c("s1", "s2")),
  responses = c("r1", "r2"),
  type      = "rd")

pVec <- c(a=1, v=1, z=0.5, sz=0.25, sv=0.2,t0=.15)

## Set up a model-data instance
dat <- simulate(m1, nsim=1e2, p.vector=pVec)
mdi <- data.model.dmc(dat, m1)
summed_log_likelihood(pVec, mdi)
## [1] 0.3796048

TasCL/ggdmc documentation built on May 9, 2019, 4:19 p.m.