Nothing
cat("\n-------------------- Testing HDDM --------------------")
rm(list = ls())
model <- BuildModel(
p.map = list(a = "1", v = "F", 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"), F = c("f1", "f2")),
constants = c(st0 = 0, d = 0),
responses = c("r1", "r2"),
type = "rd")
npar <- length(GetPNames(model))
## Population distribution
pop.mean <- c(a=2, v.f1=4, v.f2=3, z=0.5, sz=0.3, sv=1, t0=0.3)
pop.scale <- c(a=0.5, v.f1=.5, v.f2=.5, z=0.1, sz=0.1, sv=.3, t0=0.05)
pop.prior <- BuildPrior(
dists = rep("tnorm", npar),
p1 = pop.mean,
p2 = pop.scale,
lower = c(0,-5, -5, 0, 0, 0, 0),
upper = c(5, 7, 7, 1, 2, 1, 1))
## Simulate some data
dat <- simulate(model, nsub = 4, nsim = 10, prior = pop.prior)
dmi <- BuildDMI(dat, model)
ps <- attr(dat, "parameters")
p.prior <- BuildPrior(
dists = rep("tnorm", npar),
p1 = pop.mean,
p2 = pop.scale*5,
lower = c(0,-5, -5, 0, 0, 0, 0),
upper = c(5, 7, 7, 1, 2, 1, 1))
mu.prior <- ggdmc::BuildPrior(
dists = rep("tnorm", npar),
p1 = pop.mean,
p2 = pop.scale*5,
lower = c(0,-5, -5, 0, 0, 0, 0),
upper = c(5, 7, 7, 1, 2, 1, 1)
)
sigma.prior <- BuildPrior(
dists = rep("beta", npar),
p1 = c(a=1, v.f1=1,v.f2 = 1, z=1, sz=1, sv=1, t0=1),
p2 = rep(1, npar),
upper = rep(2, npar))
priors <- list(pprior=p.prior, location=mu.prior, scale=sigma.prior)
## Sampling ------------
fit0 <- StartNewsamples(dmi, priors)
fit <- run(fit0)
fit <- run(fit, 1e2, add=TRUE)
res <- hgelman(fit, verbose = TRUE)
est0 <- summary(fit, recovery = TRUE, ps = ps, verbose = TRUE)
est1 <- summary(fit, hyper = TRUE, recovery = TRUE, ps = pop.mean, type = 1, verbose = TRUE)
est2 <- summary(fit, hyper = TRUE, recovery = TRUE, ps = pop.scale, type = 2, verbose = TRUE)
pdf(file = "HDDM.pdf")
p1 <- plot(fit0, hyper = TRUE, start = 51)
p2 <- plot(fit, pll = FALSE)
p3 <- plot(fit)
dev.off()
## a sv sz t0 v.f1 v.f2 z
## True 2.00 1.00 0.30 0.30 4.00 3.00 0.50
## 2.5% Estimate 1.35 0.13 0.01 0.20 3.06 2.02 0.12
## 50% Estimate 1.81 0.86 0.22 0.28 3.68 2.58 0.51
## 97.5% Estimate 2.13 0.99 0.43 0.33 4.26 3.23 0.90
## Median-True -0.19 -0.14 -0.08 -0.02 -0.32 -0.42 0.01
##
## a sv sz t0 v.f1 v.f2 z
## True 0.50 0.30 0.10 0.05 0.50 0.50 0.10
## 2.5% Estimate 0.21 0.05 0.09 0.04 0.39 0.42 0.07
## 50% Estimate 0.38 0.23 0.24 0.06 0.71 0.75 0.13
## 97.5% Estimate 1.00 1.88 0.59 0.89 1.46 1.53 1.83
## Median-True -0.12 -0.07 0.14 0.01 0.21 0.25 0.03
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