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plot_parameter_effects.R
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plot_parameter_effects.R
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# Clear workspace
rm(list=ls())
# Set working directory
getwd()
# Load packages
library(tidyverse)
library(gridExtra)
# Source model functions
source("dmc/dmc.R")
load_model("LBA", "lbaN_B.R")
# Summary functions -------------------------------------------------------
fixedeffects.meanthetas <- function(samples){
## bring longer thetas down to min nmc by sampling
nmcs <- sapply(samples, function(x) x$nmc)
nmc <- min(nmcs)
for (i in 1:length(samples)) if (nmcs[i] > nmc) samples[[i]]$theta <-
samples[[i]]$theta[,,sample(1:dim(samples[[i]]$theta)[3], nmc)]
samps <- lapply(samples, function(x) x["theta"])
## thetas into big array for apply
samps2 <- unlist(samps)
dim3 <- c(dim(samps[[1]]$theta), length(samps2)/prod(dim(samps[[1]]$theta)))
dim(samps2) <- dim3
samps3 <- apply(samps2, c(1,2,3), mean)
## back to a theta list after applied
colnames(samps3) <- colnames(samps[[1]]$theta)
samps5 <- list(samps3)
attributes(samps5) <- attributes(samps[[1]])
samps5
}
# -------------------------------------------------------------------------
# Load samples
print(load("samples/sTPPM_full_sdvS.RData"))
samples <- samples1
samples[[1]]$p.names
# Get parameter summary ---------------------------------------------------
# summary.dmc()
# summary.dmc(samples[[1]])$statistics
# parms <- summary.dmc(samples)
# parms <- do.call(rbind, lapply(parms, function(x) x$statistics[,1]))
# parms
# Load parameter summary
print(load("deriv/map_parms_full_sdvS.RData"))
str(parms)
head(parms)
nrow(parms)
# Mean over participants
colMeans(parms)
# Summarize thetas
mean_thetas <- fixedeffects.meanthetas(samples)[[1]]
# Save
save(mean_thetas, file = "deriv/mean_thetas_full_sdvS.RData")
# Load
print(load("deriv/mean_thetas_full_sdvS.RData"))
# Explore mean thetas
str(mean_thetas)
dim(mean_thetas)
mean_thetas
# Create parameter data frame ---------------------------------------------
msds <- cbind(apply(mean_thetas, 2, median), apply(mean_thetas, 2, sd))
colnames(msds) <- c("M", "SD")
msds <- data.frame(msds)
msds
colMeans(parms)
# Add factors for plotting ------------------------------------------------
ps <- data.frame(msds)
ps$TP <- NA; ps$PM <- NA; ps$PM_trial <- NA; ps$S <- NA; ps$R <- NA
ps$TP[grep("3s", rownames(ps))] <- "3s"
ps$TP[grep("6s", rownames(ps))] <- "6s"
ps$PM[grep("10", rownames(ps))] <- "10% PM"
ps$PM[grep("30", rownames(ps))] <- "30% PM"
ps$PM_trial[grep("cc", rownames(ps))] <- "Absent"
ps$PM_trial[grep("nn", rownames(ps))] <- "Absent"
ps$PM_trial[grep("pc", rownames(ps))] <- "Present"
ps$PM_trial[grep("pn", rownames(ps))] <- "Present"
ps$PM_trial[grep("pp", rownames(ps))] <- "Present"
ps$S[grep("cc", rownames(ps))] <- "Conflict"
ps$S[grep("nn", rownames(ps))] <- "Non-conflict"
ps$S[grep("pc", rownames(ps))] <- "PM conflict"
ps$S[grep("pn", rownames(ps))] <- "PM non-conflict"
ps$S[grep("pp", rownames(ps))] <- "PM"
ps$R[grep("C", rownames(ps))] <- "Conflict"
ps$R[grep("N", rownames(ps))] <- "Non-conflict"
ps$R[grep("P", rownames(ps))] <- "PM"
ps$TP <- factor(ps$TP)
ps$PM <- factor(ps$PM)
ps$PM_trial <- factor(ps$PM_trial)
ps$S <- factor(ps$S)
ps$R <- factor(ps$R)
str(ps)
ps
# Get A
A <- ps[ grep("^A", rownames(ps)), c("M", "SD") ]
A
# Get B
B <- ps[ grep("B.", rownames(ps)), c("M", "SD", "TP", "PM", "R") ]
B
# Get v
v <- ps[ grep("mean_v.", rownames(ps)), ]
v <- v[ -grep("PMFA", rownames(v)), ]
v
# Exclude PM miss rates
v <- v[!(v$S == "PM conflict" & v$R != "PM") & !(v$S == "PM non-conflict" & v$R != "PM"),]
v
# Get reactive control v
reactive <- ps[ grep("mean_v.", rownames(ps)), ]
reactive <- reactive[ -grep("PMFA", rownames(reactive)), ]
reactive <- reactive[ -grep("P", rownames(reactive)), ]
reactive <- reactive[ (reactive$S == "Conflict" & reactive$R == "Conflict")|
(reactive$S == "PM conflict" & reactive$R == "Conflict")|
(reactive$S == "Non-conflict" & reactive$R == "Non-conflict")|
(reactive$S == "PM non-conflict" & reactive$R == "Non-conflict"), ]
reactive
# Get sdv
sdv <- ps[ grep("sd_v", rownames(ps)), c("M", "SD") ]
sdv
# Get t0
t0 <- ps[ grep("t0", rownames(ps)), c("M", "SD") ]
t0
# Make plots --------------------------------------------------------------
# Plot thresholds
B_plot <- B %>%
ggplot(aes(x = factor(TP), y = M)) +
geom_point(stat = "identity", aes(color = R, shape = R), size = 3) +
geom_line(aes(y = M, group = R, color = R),
linetype = "dashed",
size = 0.8) +
geom_errorbar(aes(ymin = M - SD,
ymax = M + SD,
width = 0.3, color = R)) +
ylim(1.1, 2.3) +
facet_grid(. ~ PM) +
labs(title = "Threshold",
x = "Time pressure",
y = "B",
color = "Response",
shape = "Response") +
theme_minimal()
B_plot
ggsave("plots/B_plot.png", plot = B_plot,
width = 2200, height = 1200, units = "px")
# Plot rates
v_plot <- v %>%
ggplot(aes(x = factor(TP), y = M, shape = R, color = R)) +
geom_point(stat = "identity", aes(), size = 3) +
geom_line(aes(y = M, group = R),
linetype = "dashed",
size = 0.8) +
geom_errorbar(aes(ymin = M - SD,
ymax = M + SD,
width = 0.3)) +
ylim(0.5, 2.1) +
facet_grid(S ~ PM) +
labs(title = "Accumulation rate",
x = "Time pressure",
y = "v",
color = "Response",
shape = "Response") +
theme_minimal()
v_plot
ggsave("plots/v_plot.png", plot = v_plot,
width = 2200, height = 1400, units = "px")
# Plot reactive control
reactive_plot <- reactive %>%
ggplot(aes(x = factor(PM_trial), y = M, shape = R, color = R)) +
geom_point(stat = "identity", aes(), size = 3) +
geom_line(aes(y = M, group = R),
linetype = "dashed",
size = 0.8) +
geom_errorbar(aes(ymin = M - SD,
ymax = M + SD,
width = 0.3)) +
ylim(0.4, 1.7) +
facet_grid(PM ~ TP) +
labs(title = "Reactive control of ongoing task accumulation rate",
x = "PM stimulus",
y = "v",
color = "Response",
shape = "Response") +
theme_minimal()
reactive_plot
ggsave("plots/reactive_plot.png", plot = reactive_plot,
width = 2200, height = 1200, units = "px")