Nothing
cat("\n-------------------- Testing likelihood --------------------")
## Wiener ----------
cat("\nWiener diffusion model: \n")
rm(list = ls())
model <- BuildModel(
p.map = list(a = "1", v="1", z="1", d="1", sz="1", sv="1", t0="1", st0="1"),
match.map = list(M = list(s1 = "r1", s2 = "r2")),
factors = list(S = c("s1", "s2")),
responses = c("r1","r2"),
constants = c(st0 = 0, d = 0, sv = 0, sz = 0),
type = "rd")
p.vector <- c(a=1, v=1.5, z=0.5, t0=.15)
dat <- simulate(model, nsim = 10, ps = p.vector)
dmi <- BuildDMI(dat, model)
res0 <- likelihood(p.vector, dmi)
cat( round(res0, 2), "\n")
## DDM ----------
cat("\nDecision diffusion model: \n")
model <- BuildModel(
p.map = list(a = "1", v = "1", z = "1", d = "1", sz = "1", sv = "1",
t0 = "1", st0 = "1"),
match.map = list(M = list(s1 = "r1", s2 = "r2")),
factors = list(S = c("s1", "s2")),
responses = c("r1", "r2"),
constants = c(st0 = 0, d = 0),
type = "rd")
p.vector <- c(a = 1, v = 1.2, z = .38, sz = .25, sv = .2, t0 = .15)
dat <- simulate(model, nsim = 10, ps = p.vector)
dmi <- BuildDMI(dat, model)
res0 <- likelihood(p.vector, dmi)
cat( round(res0, 2), "\n")
## LBA --------
cat("\nLBA model: \n")
model <- BuildModel(
p.map = list(A = "1", B = "R", t0 = "1", mean_v = c("D", "M"),
sd_v = "M", st0 = "1"),
match.map = list(M = list(s1 = 1, s2 = 2)),
factors = list(S = c("s1", "s2"), D = c("d1", "d2")),
constants = c(sd_v.false = 1, st0 = 0),
responses = c("r1", "r2"),
type = "norm")
p.vector <- c(A=.51, B.r1=.69, B.r2=.88, t0=.24, mean_v.d1.true=1.1,
mean_v.d2.true=1.0, mean_v.d1.false=.34, mean_v.d2.false=.02,
sd_v.true=.11)
dat <- simulate(model, nsim = 10, ps = p.vector)
dmi <- BuildDMI(dat, model)
res0 <- likelihood(p.vector, dmi)
cat( round(res0, 2) , "\n")
## LBA --------
cat("\nLikelihood example 1: \n")
model <- BuildModel(
p.map = list(A = "1", B = "1", t0 = "1", mean_v = "M", sd_v = "1",
st0 = "1"),
match.map = list(M = list(s1 = 1, s2 = 2)),
factors = list(S = c("s1", "s2")),
constants = c(st0 = 0, sd_v = 1),
responses = c("r1", "r2"),
type = "norm")
p.vector <- c(A = .25, B = .35, t0 = .2, mean_v.true = 1, mean_v.false = .25)
dat <- simulate(model, 1e3, ps = p.vector)
dmi <- BuildDMI(dat, model)
den <- likelihood(p.vector, dmi)
cat("\nLikelihood example 2: \n")
model <- BuildModel(
p.map = list(a = "1", v = "1", z = "1", d = "1", t0 = "1", sv = "1",
sz = "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")
p.vector <- c(a = 1, v = 1, z = 0.5, sz = 0.25, sv = 0.2, t0 = .15)
dat <- simulate(model, 1e2, ps = p.vector)
dmi <- BuildDMI(dat, model)
den <- likelihood (p.vector, dmi)
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