Nothing
cat("\n-------------------- Testing LBA 1 Subject --------------------")
rm(list = ls())
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 = .75, B = 1.25, t0 = .15, mean_v.true = 2.5, mean_v.false = 1.5)
ntrial <- 50
dat <- simulate(model, nsim = ntrial, ps = p.vector)
dmi <- BuildDMI(dat, model)
p.prior <- BuildPrior(
dists = c("tnorm", "tnorm", "beta", "tnorm", "tnorm"),
p1 = c(A = 1, B = 1, t0 = 1, mean_v.true = 1, mean_v.false = 1),
p2 = c(1, 1, 1, 1, 1),
lower = c(rep(0, 3), rep(NA, 2)),
upper = c(rep(NA, 2), 1, rep(NA, 2)))
## Sampling ---------
fit0 <- StartNewsamples(dmi, p.prior, pm0=0, pm1=0, block = FALSE)
fit <- run(fit0, thin = 4, block = FALSE)
hat <- gelman(fit);
pdf(file = "LBA1S.pdf")
p0 <- ggdmc:::plot.model(fit)
p1 <- ggdmc:::plot.model(fit0, start = 51)
p2 <- ggdmc:::plot.model(fit)
p2 <- ggdmc:::plot.model(fit, pll = F, den = T)
dev.off()
## Analysis -----------
est <- summary(fit, recovery = TRUE, ps = p.vector, verbose = TRUE)
# A B mean_v.false mean_v.true t0
# True 0.75 1.25 1.50 2.50 0.15
# 2.5% Estimate 0.62 1.14 1.45 2.46 0.13
# 50% Estimate 0.74 1.25 1.51 2.50 0.15
# 97.5% Estimate 0.83 1.38 1.57 2.55 0.17
# Median-True -0.01 0.00 0.01 0.00 0.00
## A B t0 mean_v.true mean_v.false
## True 0.75 0.25 0.20 2.50 1.50
## 2.5% Estimate 0.60 0.17 0.17 2.24 1.12
## 50% Estimate 0.75 0.25 0.20 2.59 1.48
## 97.5% Estimate 0.90 0.35 0.22 2.95 1.84
## Median-True 0.00 0.00 0.00 0.09 -0.02
## A B mean_v.false mean_v.true t0
## True 0.75 1.25 1.50 2.50 0.15
## 2.5% Estimate 0.56 1.08 1.37 2.40 0.12
## 50% Estimate 0.72 1.23 1.46 2.47 0.15
## 97.5% Estimate 0.84 1.41 1.55 2.53 0.18
## Median-True -0.03 -0.02 -0.04 -0.03 0.00
# A B mean_v.false mean_v.true t0
# True 0.75 1.25 1.50 2.50 0.15
# 2.5% Estimate 0.51 1.21 1.50 2.50 0.08
# 50% Estimate 0.71 1.39 1.58 2.57 0.12
# 97.5% Estimate 0.86 1.63 1.68 2.64 0.15
# Median-True -0.04 0.14 0.08 0.07 -0.03
# A B mean_v.false mean_v.true t0
# True 0.75 1.25 1.50 2.50 0.15
# 2.5% Estimate 0.37 1.21 1.44 2.44 0.09
# 50% Estimate 0.61 1.40 1.54 2.51 0.12
# 97.5% Estimate 0.77 1.65 1.63 2.58 0.15
# Median-True -0.14 0.15 0.04 0.01 -0.03
# A B mean_v.false mean_v.true t0
# True 0.75 1.25 1.50 2.50 0.15
# 2.5% Estimate 0.41 1.23 1.51 2.50 0.09
# 50% Estimate 0.62 1.43 1.60 2.57 0.12
# 97.5% Estimate 0.79 1.65 1.69 2.64 0.15
# Median-True -0.13 0.18 0.10 0.07 -0.03
# A B mean_v.false mean_v.true t0
# True 0.75 1.25 1.50 2.50 0.15
# 2.5% Estimate 0.70 1.01 1.32 2.38 0.15
# 50% Estimate 0.82 1.11 1.40 2.44 0.17
# 97.5% Estimate 0.92 1.27 1.48 2.50 0.19
# Median-True 0.07 -0.14 -0.10 -0.06 0.02
# A B mean_v.false mean_v.true t0
# True 0.75 1.25 1.50 2.50 0.15
# 2.5% Estimate 0.67 1.04 1.40 2.42 0.14
# 50% Estimate 0.80 1.17 1.47 2.48 0.17
# 97.5% Estimate 0.92 1.33 1.54 2.54 0.19
# Median-True 0.05 -0.08 -0.03 -0.02 0.02
# A B mean_v.false mean_v.true t0
# True 0.75 1.25 1.50 2.50 0.15
# 2.5% Estimate 0.40 1.19 1.42 2.43 0.10
# 50% Estimate 0.61 1.37 1.51 2.50 0.13
# 97.5% Estimate 0.76 1.57 1.59 2.56 0.16
# Median-True -0.14 0.12 0.01 0.00 -0.02
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