```
# NOT RUN {
## generate random LBA data:
rt1 <- rLBA(500, A=0.5, b=1, t0 = 0.5, mean_v=c(2.4, 1.6), sd_v=c(1,1.2))
head(rt1)
prop.table(table(rt1$response))
# original parameters have 'high' log-likelihood:
sum(log(dLBA(rt1$rt, rt1$response, A=0.5, b=1, t0 = 0.5, mean_v=c(2.4, 1.6), sd_v=c(1,1.2))))
# data can also be passed as data.frame (same is true for pLBA):
sum(log(dLBA(rt1, A=0.5, b=1, t0 = 0.5, mean_v=c(2.4, 1.6), sd_v=c(1,1.2))))
objective_fun <- function(par, rt, response, distribution = "norm") {
# simple parameters
spar <- par[!grepl("[12]$", names(par))]
# distribution parameters:
dist_par_names <- unique(sub("[12]$", "", grep("[12]$" ,names(par), value = TRUE)))
dist_par <- vector("list", length = length(dist_par_names))
names(dist_par) <- dist_par_names
for (i in dist_par_names) dist_par[[i]] <- as.list(unname(par[grep(i, names(par))]))
dist_par$sd_v <- c(1, dist_par$sd_v) # fix first sd to 1
# get summed log-likelihood:
d <- do.call(dLBA, args = c(rt=list(rt), response=list(response), spar, dist_par,
distribution=distribution, silent=TRUE))
if (any(d < 0e-10)) return(1e6)
else return(-sum(log(d)))
}
# gives same value as manual calculation above:
objective_fun(c(A=0.5, b=1, t0=0.5, mean_v1=2.4, mean_v2=1.6, sd_v2=1.2),
rt=rt1$rt, response=rt1$response)
# }
# NOT RUN {
# can we recover the parameters?
# should be run several times with different random values of init_par
init_par <- runif(6)
init_par[2] <- sum(init_par[1:2]) # ensures b is larger than A
init_par[3] <- runif(1, 0, min(rt1$rt)) #ensures t0 is mot too large
names(init_par) <- c("A", "b", "t0", "mean_v1", "mean_v2", "sd_v2")
nlminb(objective_fun, start = init_par, rt=rt1$rt, response=rt1$response, lower = 0)
# }
# NOT RUN {
# plot cdf (2 accumulators):
curve(pLBA(x, response = 1, A=0.5, b=1, t0 = 0.5, mean_v=c(2.4, 1.6), sd_v=c(1,1.2)),
xlim = c(0, 2), ylim = c(0,1),
ylab = "cumulative probability", xlab = "response time",
main = "Defective CDFs of LBA")
curve(pLBA(x, response = 2, A=0.5, b=1, t0 = 0.5, mean_v=c(2.4, 1.6), sd_v=c(1,1.2)),
add=TRUE, lty = 2)
legend("topleft", legend=c("1", "2"), title="Response", lty=1:2)
# plot cdf (3 accumulators):
curve(pLBA(x, response = 1, A=0.5, b=1, t0 = 0.5, mean_v=c(2.4, 1.6, 1.0), sd_v=c(1,1.2, 2.0)),
xlim = c(0, 2), ylim = c(0,1),
ylab = "cumulative probability", xlab = "response time",
main = "Defective CDFs of LBA")
curve(pLBA(x, response = 2, A=0.5, b=1, t0 = 0.5, mean_v=c(2.4, 1.6, 1.0), sd_v=c(1,1.2, 2.0)),
add=TRUE, lty = 2)
curve(pLBA(x, response = 3, A=0.5, b=1, t0 = 0.5, mean_v=c(2.4, 1.6, 1.0), sd_v=c(1,1.2, 2.0)),
add=TRUE, lty = 3)
legend("topleft", legend=c("1", "2", "3"), title="Response", lty=1:2)
## qLBA can only return values up to maximal predicted probability:
(max_p <- pLBA(Inf, response = 1, A=0.5, b=1, t0 = 0.5, mean_v=c(2.4, 1.6), sd_v=c(1,1.2)))
# [1] 0.6604696
qLBA(0.66, response = 1, A=0.5, b=1, t0 = 0.5, mean_v=c(2.4, 1.6), sd_v=c(1,1.2))
# 2.559532
qLBA(0.67, response = 1, A=0.5, b=1, t0 = 0.5, mean_v=c(2.4, 1.6), sd_v=c(1,1.2))
# NA
# to get predicted quantiles, scale required quantiles by maximally predicted response rate:
qs <- c(.1, .3, .5, .7, .9)
qLBA(qs*max_p, response = 1, A=0.5, b=1, t0 = 0.5, mean_v=c(2.4, 1.6), sd_v=c(1,1.2))
# 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)
qLBA(qs, response = 1, A=0.5, b=1, t0 = 0.5, mean_v=c(2.4, 1.6), sd_v=c(1,1.2), scale_p=TRUE)
# qLBA also accepts a data.frame as first argument:
t <- data.frame(p = rep(c(0.05, 0.1, 0.66), 2), response = rep(1:2, each = 3))
# p response
# 1 0.05 1
# 2 0.10 1
# 3 0.66 1
# 4 0.05 2
# 5 0.10 2
# 6 0.66 2
qLBA(t, A=0.5, b=1, t0 = 0.5, mean_v=c(2.4, 1.6), sd_v=c(1,1.2))
## 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)))
### trial wise parameters work as expected (only since package version 0.9):
x1 <- dLBA(rt=c(1,1), response=c(1,2), A=1,b=list(c(1,3),c(2,4)),
t0=0.1, mean_v=c(3,3), sd_v=c(1,1),distribution="norm")
x2a <- dLBA(rt=c(1), response=c(1), A=1,b=list(c(1),c(2)),
t0=0.1,mean_v=c(3,3),sd_v=c(1,1),distribution="norm")
x2b <- dLBA(rt=c(1), response=c(2), A=1,b=list(c(3),c(4)),
t0=0.1,mean_v=c(3,3),sd_v=c(1,1),distribution="norm")
all(x1 == c(x2a, x2b)) ## should be TRUE
# }
```

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