```
# NOT RUN {
## identical calls (but different random values)
rt1 <- rdiffusion(500, a=1, v=2, t0=0.5)
head(rt1)
rt2 <- rdiffusion(500, a=1, v=2, t0=0.5, d=0, sz=0, sv=0, st0=0)
head(rt2)
# get density for random RTs (possible to specify arguments for pdiffusion in same way):
sum(log(ddiffusion(rt1$rt, rt1$response, a=1, v=2, t0=0.5))) # response is factor
sum(log(ddiffusion(rt1$rt, as.numeric(rt1$response), a=1, v=2, t0=0.5))) # response is numeric
sum(log(ddiffusion(rt1$rt, as.character(rt1$response), a=1, v=2, t0=0.5))) # response is character
sum(log(ddiffusion(rt1, a=1, v=2, t0=0.5))) # response is data.frame
sum(log(ddiffusion(rt2$rt, rt2$response, a=1, v=2, t0=0.5)))
# can we recover the parameters?
ll_diffusion <- function(pars, rt, response)
{
densities <- ddiffusion(rt, response=response,
a=pars[1], v=pars[2], t0=pars[3],
sz=pars[4],
st0=pars[5], sv=pars[6])
if (any(densities == 0)) return(1e6)
return(-sum(log(densities)))
}
# }
# NOT RUN {
)
start <- c(runif(2, 0.5, 3), 0.1, runif(3, 0, 0.5))
names(start) <- c("a", "v", "t0", "sz", "st0", "sv")
recov <- nlminb(start, ll_diffusion, lower = 0, rt=rt1$rt, response=rt1$response)
round(recov$par, 3)
# a v t0 sz st0 sv
# 1.019 1.879 0.496 0.000 0.000 0.389
## results of course depend on random seed for rdiffusion and runif
# }
# NOT RUN {
# }
# NOT RUN {
## replicate Table 1 from Wagenmakers et al. (2007) using rdiffusion:
n <- 1e5 # number of samples
# take parameter valeus from Table 2 and set s to 0.1
george <- rdiffusion(n, a = 0.12, v = 0.25, t0 = 0.3, s = 0.1)
rich <- rdiffusion(n, a = 0.12, v = 0.25, t0 = 0.25, s = 0.1)
amy <- rdiffusion(n, a = 0.08, v = 0.25, t0 = 0.3, s = 0.1)
mark <- rdiffusion(n, a = 0.08, v = 0.25, t0 = 0.25, s = 0.1)
george$id <- "george"
rich$id <- "rich"
amy$id <- "amy"
mark$id <- "mark"
wag <- rbind(george, rich, amy, mark)
wag$id <- factor(wag$id, levels = c("george", "rich", "amy", "mark"))
opt <- options()
options(digits = 3)
aggregate(cbind(rt, as.numeric(response)-1) ~ id, wag, mean)
# id rt V2
# 1 george 0.517 0.952
# 2 rich 0.467 0.953
# 3 amy 0.422 0.881
# 4 mark 0.372 0.882
options(digits = 1)
aggregate(rt ~ id, wag, var)
# id rt
# 1 george 0.024
# 2 rich 0.024
# 3 amy 0.009
# 4 mark 0.009
options(opt)
# }
# NOT RUN {
## plot density:
curve(ddiffusion(x, a=1, v=2, t0=0.5, response = "upper"),
xlim=c(0,3), main="Density of upper responses", ylab="density", xlab="response time")
curve(ddiffusion(x, a=1, v=2, t0=0.5, st0=0.2, response = "upper"),
add=TRUE, lty = 2)
legend("topright", legend=c("no", "yes"), title = "Starting Point Variability?", lty = 1:2)
# plot cdf:
curve(pdiffusion(x, a=1, v=2, t0=0.5, st0=0.2, response="u"),
xlim = c(0, 3),ylim = c(0,1),
ylab = "cumulative probability", xlab = "response time",
main = "CDF of diffusion model with start point variability")
curve(pdiffusion(x, a=1, v=2, t0=0.5, st0=0.2, response="l"),
add=TRUE, lty = 2)
legend("topleft", legend=c("upper", "lower"), title="response boundary", lty=1:2)
# }
# NOT RUN {
### qdiffusion can only return values up to maximal predicted probability:
(max_p <- pdiffusion(Inf, a=1, v=2, t0=0.5, st0=0.2, sz = 0.1, sv = 0.5, response="u"))
# [1] 0.87
# (Note that with the current integration routine for pdiffusion use Inf and not smaller values.)
qdiffusion(0.87, a=1, v=2, t0=0.5, st0=0.2, sz = 0.1, sv = 0.5, response="u")
# [1] 1.945802
qdiffusion(0.88, a=1, v=2, t0=0.5, st0=0.2, sz = 0.1, sv = 0.5, response="u")
# NA with warning.
# to get predicted quantiles, scale required quantiles by maximally predicted response rate:
qs <- c(.1, .3, .5, .7, .9)
qdiffusion(qs*max_p, a=1, v=2, t0=0.5, st0=0.2, sz = 0.1, sv = 0.5, response="u")
# or set scale_p to TRUE which scales automatically by maximum p
# (but can be slow as it calculates max_p for each probability separately)
qdiffusion(qs, a=1, v=2, t0=0.5, st0=0.2, sz = 0.1, sv = 0.5, response="u", scale_p = TRUE)
# qdiffusion also accepts a data.frame as first argument:
t3 <- data.frame(p = rep(c(0.05, 0.1, 0.87), 2), response = rep(c("upper", "lower"), each = 3))
# p response
# 1 0.05 upper
# 2 0.10 upper
# 3 0.87 upper
# 4 0.05 lower
# 5 0.10 lower
# 6 0.87 lower
qdiffusion(t3, a=1, v=2, t0=0.5, st0=0.2, sz = 0.1, sv = 0.5, scale_p = TRUE)
# }
# NOT RUN {
## LBA and diffusion can be used interchangeably:
rt1 <- rLBA(500, A=0.5, b=1, t0 = 0.5, mean_v=c(2.4, 1.6), sd_v=c(1,1.2))
rt2 <- rdiffusion(500, a=1, v=2, t0=0.5)
# data can also be passed as data.frame (same is true for pLBA):
sum(log(dLBA(rt1, A=0.5, b=1, t0 = 0.5, mean_v=c(2.4, 1.6), sd_v=c(1,1.2))))
sum(log(dLBA(rt2, A=0.5, b=1, t0 = 0.5, mean_v=c(2.4, 1.6), sd_v=c(1,1.2))))
sum(log(ddiffusion(rt1, a=1, v=2, t0=0.5)))
sum(log(ddiffusion(rt2, a=1, v=2, t0=0.5)))
# }
```

Run the code above in your browser using DataLab