ddm_sims

ddm_sims.R

library(tidyverse)

# DDM simulation function
sim_ddm <- function(drift_rate, boundary_separation, bias, ndt, n_sims, seed = NULL) {
  set.seed(seed)
  sim_df = NULL
  for (sim in 1:n_sims){
    step = 1
    value = bias
    n = 1
    while (abs(value[n]) < boundary_separation) {
      n = n + 1
      value[n] = (value[n - 1] + rnorm(1, 0, abs(drift_rate)))
      step[n] = n
    }
    dd_df <- tibble(sim_n = sim, step, value) %>% 
    mutate( 
      sim_n = as.factor(sim_n), 
      value = case_when(
        value > boundary_separation ~ boundary_separation, 
        value < -boundary_separation ~ -boundary_separation, 
        TRUE ~ .$value)
      )
    sim_df = rbind(sim_df, dd_df)
  }
  return(sim_df)
}

# Get α, β, δ, and τ from DDM 
# Plot 20 random simulations after fitting model
sim_ddm(
  drift_rate = 0.8, 
  boundary_separation =  1, 
  bias = 0, 
  ndt = 0.2, 
  n_sims = 20, 
  seed = 12345) %>% 
  ggplot(., aes(x = step, y = value, color = sim_n)) + 
    geom_line(show.legend = F)

# You can increase the number of sims and plot the average trajectories too

Thank you! Have you tired to use brms to run your ddm?

Yep. I looked into a few other options (there is an interesting one in python but I couldnt get it to work: http://ski.clps.brown.edu/hddm_docs/index.html), but brms is pretty much home for me now. How do you usually fit yours?

I haven't done much DDM. I am planning to collect some data online next semester that I want to look at with DDM so I am exploring options.

@jvcasillas Can you share the code you used to make the figure as well? I think it would be a good way to depict predictions one has about the different parameters.

Ah the plot you are referring to is just a schematic to explain the model (it's in the method section of the paper Im working on). I use it when I explain what each parameter represents. Here is the code:

# DDM explanation -------------------------------------------------------------

# Generate skewed RT-like data
my_skewed_x <- fGarch::rsnorm(100, mean = 2.5, sd = 1.5, xi = 3.5)

# Correct response dist
inset_top <- tibble(x = my_skewed_x) %>%
  ggplot(., aes(x = x)) + 
    geom_density(fill = viridis::viridis_pal(option = "B", begin = 0.3)(1)) + 
    theme_void()

# Incorrect response dist
inset_bottom <- tibble(x = my_skewed_x) %>%
  ggplot(., aes(x = x)) + 
    geom_density(fill = viridis::viridis_pal(option = "B", begin = 0.75)(1)) + 
    scale_y_reverse() + 
    theme_void()

threshold_labs <- tibble(
  step = 80, value = c(1.4, -1.4), 
  labs = c("Correct response", "Incorrect response")
)

# Main plot
main_plot <- sim_ddm(q_type = "yn", eq = "low", lt = "low", seed = 20180119, 
  drift_rate = 0.11, boundary_separation = 1.25, bias = 0, ndt = 0, n_sims = 2) %>% 
  ggplot(., aes(x = step, y = value, color = sim_n)) + 
    geom_vline(xintercept = 1) + 
    geom_hline(yintercept = c(-1.25, 1.25), lty = 3) + 
    geom_line(show.legend = F, size = 1.25) + 
    geom_segment(inherit.aes = F, aes(x = 1, xend = 98, y = 0, yend = 0), 
      lty = 2, size = 0.25) + 
    coord_cartesian(ylim = c(-2, 2), xlim = c(-5, 100)) + 
    scale_x_continuous(breaks = c(1, seq(10, 100, 10)), 
      labels = c("0", seq(10, 100, 10))) + 
    scale_y_continuous(breaks = NULL) + 
    labs(y = expression(alpha), x = "Time step") + 
    annotate("text", x = -4.5, y = 0.15, size = 5, label = expression(tau)) + 
    geom_segment(inherit.aes = F, aes(x = -5, y = 0, xend = 0, yend = 0), 
      arrow = arrow(length = unit(0.2, "cm")), size = 0.5) + 
    geom_segment(inherit.aes = F, aes(x = -5, y = 0, xend = -9, yend = 0), 
      arrow = arrow(length = unit(0.2, "cm"))) + 
    annotate("text", x = 101, y = 0, size = 5, label = expression(beta)) + 
    geom_segment(inherit.aes = F, aes(x = 101, y = 0.2, xend = 101, yend = 0.5), 
      arrow = arrow(length = unit(0.2, "cm")), size = 0.5) + 
    geom_segment(inherit.aes = F, aes(x = 101, y = -0.2, xend = 101, yend = -0.5), 
      arrow = arrow(length = unit(0.2, "cm"))) + 
    annotate("text", x = 32, y = 0.25, size = 5, label = expression(delta)) + 
    geom_segment(inherit.aes = F, aes(x = 30.5, y = 0.35, xend = 25, yend = 0.7), 
      arrow = arrow(length = unit(0.2, "cm"))) + 
    geom_text(inherit.aes = F, data = threshold_labs, hjust = 0, 
      aes(x = step, y = value, label = labs), family = "Times") + 
    scale_color_viridis_d(option = "B", begin = 0.3, end = 0.75) + 
    theme_minimal(base_family = 'Times', base_size = 13) + 
    theme(panel.grid.major = element_line(size = 0.2),
          panel.grid.minor = element_blank(), 
      axis.title.y = element_text(size = rel(.9), hjust = 0.5))

# Combine plots
ddm_explanation <- main_plot + annotation_custom(
  grob = ggplotGrob(inset_top), 
  ymin = 1.25, ymax = 2, xmin = -2.5, xmax = 75) + 
  annotation_custom(
  grob = ggplotGrob(inset_bottom), 
  ymin = -1.25, ymax = -2, xmin = -2.5, xmax = 75)

# -----------------------------------------------------------------------------

This is how Im using it: