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model_full_sdvS.R
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model_full_sdvS.R
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# Clear workspace
rm(list=ls())
# Set working directory to top-level folder containing DMC
# setwd("~/DMC")
# setwd("D:/ATC experiments/Exp5 TP-PM/Analysis/DMC")
# Load libraries and DMC functions
source("dmc/dmc.R")
load_model("LBA", "lbaN_B.R")
# Load data
print(load("Data/ATC_TP-PM_clean.RData"))
dat <- cleandats[,c("s","S","TP","PM","R","RT")]
# names(dat)[c(1)] <- c("subjects")
levels(dat$R)
levels(dat$S)
head(dat)
str(dat)
# NB: RT has been truncated below 0.20s
# Rename PM factor levels
levels(dat$PM)
dat$PM <- factor(dat$PM, levels = c("10%", "30%"), labels = c("10", "30"))
# Check factor levels
lapply(dat, levels)
# 3200 trials per subject
table(dat$s)
# Create match maps -------------------------------------------------------
# expand.grid(list(S = c("cc","nn","pc","pn"),
# TP = c("3s","6s"),
# PM = c("10","30"),
# R = c("C","N","P")))y
# Mean v map
map_v <- empty.map(
list(S = c("cc","nn","pc","pn"),
TP = c("3s","6s"),
PM = c("10","30"),
R = c("C","N","P")),
levels = c("cc3s10C","nn3s10C","pc3s10C","pn3s10C",
"cc6s10C","nn6s10C","pc6s10C","pn6s10C",
"cc3s30C","nn3s30C","pc3s30C","pn3s30C",
"cc6s30C","nn6s30C","pc6s30C","pn6s30C",
"cc3s10N","nn3s10N","pc3s10N","pn3s10N",
"cc6s10N","nn6s10N","pc6s10N","pn6s10N",
"cc3s30N","nn3s30N","pc3s30N","pn3s30N",
"cc6s30N","nn6s30N","pc6s30N","pn6s30N",
"pp3s10P",
"pp6s10P",
"pp3s30P",
"pp6s30P",
"PMFA"))
map_v
length(map_v)
length(levels(map_v))
map_v[1:48] <- c("cc3s10C","nn3s10C","pc3s10C","pn3s10C",
"cc6s10C","nn6s10C","pc6s10C","pn6s10C",
"cc3s30C","nn3s30C","pc3s30C","pn3s30C",
"cc6s30C","nn6s30C","pc6s30C","pn6s30C",
"cc3s10N","nn3s10N","pc3s10N","pn3s10N",
"cc6s10N","nn6s10N","pc6s10N","pn6s10N",
"cc3s30N","nn3s30N","pc3s30N","pn3s30N",
"cc6s30N","nn6s30N","pc6s30N","pn6s30N",
"PMFA","PMFA","pp3s10P","pp3s10P",
"PMFA","PMFA","pp6s10P","pp6s10P",
"PMFA","PMFA","pp3s30P","pp3s30P",
"PMFA","PMFA","pp6s30P","pp6s30P")
map_v
# sdv map
map_sdv <- empty.map(
list(S = c("cc","nn","pc","pn"),
TP = c("3s","6s"),
PM = c("10","30"),
R = c("C","N","P")),
levels = c("ccSDV","nnSDV","ppSDV"))
map_sdv
length(map_sdv)
length(levels(map_sdv))
map_sdv[1:48] <- c("ccSDV","nnSDV","ppSDV","ppSDV",
"ccSDV","nnSDV","ppSDV","ppSDV",
"ccSDV","nnSDV","ppSDV","ppSDV",
"ccSDV","nnSDV","ppSDV","ppSDV",
"ccSDV","nnSDV","ppSDV","ppSDV",
"ccSDV","nnSDV","ppSDV","ppSDV",
"ccSDV","nnSDV","ppSDV","ppSDV",
"ccSDV","nnSDV","ppSDV","ppSDV",
"ccSDV","nnSDV","ppSDV","ppSDV",
"ccSDV","nnSDV","ppSDV","ppSDV",
"ccSDV","nnSDV","ppSDV","ppSDV",
"ccSDV","nnSDV","ppSDV","ppSDV")
map_sdv
# Threshold map
map_B <- empty.map(
list(S = c("cc","nn","pc","pn"),
TP = c("3s","6s"),
PM = c("10","30"),
R = c("C","N","P")),
levels = c("3s10C",
"3s30C",
"6s10C",
"6s30C",
"3s10N",
"3s30N",
"6s10N",
"6s30N",
"3s10P",
"3s30P",
"6s10P",
"6s30P"))
map_B
length(map_B)
length(levels(map_B))
map_B[1:48] <- c("3s10C","3s10C","3s10C","3s10C",
"6s10C","6s10C","6s10C","6s10C",
"3s30C","3s30C","3s30C","3s30C",
"6s30C","6s30C","6s30C","6s30C",
"3s10N","3s10N","3s10N","3s10N",
"6s10N","6s10N","6s10N","6s10N",
"3s30N","3s30N","3s30N","3s30N",
"6s30N","6s30N","6s30N","6s30N",
"3s10P","3s10P","3s10P","3s10P",
"6s10P","6s10P","6s10P","6s10P",
"3s30P","3s30P","3s30P","3s30P",
"6s30P","6s30P","6s30P","6s30P")
map_B
# Build model -------------------------------------------------------------
model <- model.dmc(
p.map = list(
A = "1",
B = "MAPB",
t0 = "1",
mean_v = "MAPV",
sd_v = "MAPSDV",
st0 = "1",
N = "PM"),
match.map = list(
M = list(
cc = "C",
nn = "N",
pc = "P",
pn = "P"),
MAPB = map_B,
MAPSDV = map_sdv,
MAPV = map_v),
factors = list(
S = c("cc", "nn", "pc", "pn"),
TP = c("3s", "6s"),
PM = c("10", "30")),
constants = c(N.10 = 3, N.30 = 3,
st0 = 0,
sd_v.ppSDV = 0.5),
responses = c("C","N","P"),
type = "normN")
length(attr(model, "p.vector"))
# Create parameter vector
p.vector <- c(t0 = 0.3, A = 1.5,
B.3s10C = 2, B.3s30C = 2, B.6s10C = 2, B.6s30C = 2,
B.3s10N = 2, B.3s30N = 2, B.6s10N = 2, B.6s30N = 2,
B.3s10P = 2, B.3s30P = 2, B.6s10P = 2, B.6s30P = 2,
mean_v.cc3s10C = 1, mean_v.nn3s10C = 0, mean_v.pc3s10C = 0, mean_v.pn3s10C = 0,
mean_v.cc6s10C = 1, mean_v.nn6s10C = 0, mean_v.pc6s10C = 0, mean_v.pn6s10C = 0,
mean_v.cc3s30C = 1, mean_v.nn3s30C = 0, mean_v.pc3s30C = 0, mean_v.pn3s30C = 0,
mean_v.cc6s30C = 1, mean_v.nn6s30C = 0, mean_v.pc6s30C = 0, mean_v.pn6s30C = 0,
mean_v.cc3s10N = 0, mean_v.nn3s10N = 1, mean_v.pc3s10N = 0, mean_v.pn3s10N = 0,
mean_v.cc6s10N = 0, mean_v.nn6s10N = 1, mean_v.pc6s10N = 0, mean_v.pn6s10N = 0,
mean_v.cc3s30N = 0, mean_v.nn3s30N = 1, mean_v.pc3s30N = 0, mean_v.pn3s30N = 0,
mean_v.cc6s30N = 0, mean_v.nn6s30N = 1, mean_v.pc6s30N = 0, mean_v.pn6s30N = 0,
mean_v.pp3s10P = 1, mean_v.pp6s10P = 1, mean_v.pp3s30P = 1, mean_v.pp6s30P = 1,
mean_v.PMFA = 0,
sd_v.ccSDV = 0.5, sd_v.nnSDV = 0.5)
length(p.vector)
# Check parameter vector matches model
check.p.vector(p.vector, model)
# Check model simulates
# simulate.dmc(p.vector, model)
# Set priors
p.prior <- prior.p.dmc(
dists = c("beta", rep("tnorm", length(p.vector)-1)),
p1 = c(p.vector),
p2 = c(0.2, 0.1, rep(1, 12), rep(1, 37), rep(0.5, 2)),
lower = c(0.1, 0, rep(0, 12), rep(NA, 37), rep(0, 2)),
upper = c(1, 10, rep(Inf, 12), rep(Inf, 37), rep(Inf, 2))
)
length(p.prior)
length(p.vector)
# Plot priors
# par(mfcol = c(2, 4)); for (i in names(p.prior)) plot.prior(i, p.prior)
# Make data model
dm <- data.model.dmc(dat, model)
save(dm, file = "samples/dmTPPM_full_sdvS.RData")
# Initialize samples object
# 159 chains
n.chains <- length(p.prior) * 3
# Generate start points for fixed effects model
samples <- h.samples.dmc(nmc = 100,
p.prior = p.prior,
data = dm,
thin = 10,
n.chains = n.chains)
# Save
save(samples, file = "samples/sTPPM_full_sdvS.RData")
# Load
print(load("samples/sTPPM_full_sdvS.RData"))
# -------------------------------------------------------------------------
#
# # Generate start points for hierarchical model
# # Hyper-level
# hstart <- make.hstart(samples)
#
# # Subject-level
# theta <- make.theta1(samples)
#
#
# # Hyper-level priors
# # Mu
# mu.prior <- prior.p.dmc(
# dists = rep("tnorm", 51),
# p1 = c(t0 = 1, p.vector[-1]),
# p2 = c(1, 1, rep(1, 12), rep(1.5, 37)),
# lower = c(0.1, 0, rep(0, 12), rep(NA, 37)),
# upper = c(1, 10, rep(Inf, length(p.vector)-2))
# )
#
# # Sigma
# sigma.prior <- prior.p.dmc(
# dists = c(rep("gamma", 51)),
# p1 = c(1, 1, rep(1, 12), rep(1.5, 37)),
# p2 = c(rep(1, 51))
# )
#
# # Create hyper prior object
# pp.prior <- list(mu.prior, sigma.prior)
#
# # Initialize samples object
# n.chains <- length(p.prior) * 3
#
# # Generate start points for hierarchical model
# hsamples <- h.samples.dmc(nmc = 100,
# p.prior = p.prior,
# data = dm,
# pp.prior = pp.prior,
# thin = 10,
# hstart.prior = hstart,
# theta1 = theta,
# n.chains = n.chains
# )
#
# # Save
# save(hsamples, file = "dmc/samples/hsTPPM_full_sdvS.RData")