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inst/doc/validity.R
In fddm: Fast Implementation of the Diffusion Decision Model
## ----echo=FALSE---------------------------------------------------------------
req_suggested_packages <- c("rtdists", "RWiener","testthat")
pcheck <- lapply(req_suggested_packages, requireNamespace,
quietly = TRUE)
if (any(!unlist(pcheck))) {
message("Required package(s) for this vignette are not available/installed and code will not be executed.")
knitr::opts_chunk$set(eval = FALSE)
}
## ----setup, include=FALSE-------------------------------------------------------------------------
op <- options(width = 100)
knitr::opts_chunk$set(
collapse = TRUE,
error = TRUE, # ensures compilation even if testthat checks fail
comment = "#>"
)
## ----validity-pkg, eval=TRUE----------------------------------------------------------------------
library("fddm")
require("rtdists")
require("RWiener")
source(system.file("extdata", "Gondan_et_al_density.R", package = "fddm", mustWork = TRUE))
## ----validity-run, eval=TRUE----------------------------------------------------------------------
# Define parameter space
RT <- c(0.001, 0.1, 1, 10)
A <- c(0.5, 1, 5)
V <- c(-5, 0, 5)
t0 <- 1e-4 # must be nonzero for RWiener
W <- c(0.2, 0.5, 0.8)
SV <- c(0, 0.5, 1.5)
SV_THRESH <- 1e-6
eps <- 1e-6 # this is the setting from rtdists
nRT <- length(RT)
nA <- length(A)
nV <- length(V)
nW <- length(W)
nSV <- length(SV)
resp <- rep("lower", nRT) # for RWiener
fnames <- c("fs_SWSE_17", "fs_SWSE_14", "ft_SWSE_17", "ft_SWSE_14",
"fb_SWSE_17", "fb_SWSE_17",
"fs_Gon_17", "fs_Gon_14", "fb_Gon_17", "fb_Gon_14",
"fs_Nav_17", "fs_Nav_14", "fb_Nav_17", "fb_Nav_14",
"fl_Nav_09", "RWiener", "Gondan", "rtdists")
nf <- length(fnames)
res <- data.frame(matrix(ncol = 9, nrow = nf*nRT*nA*nV*nW*nSV))
colnames(res) <- c('rt', 'a', 'v', 'w', 'sv', 'FuncName', 'res', 'dif',
'log_res')
start <- 1
stop <- nf
# Loop through each combination of parameters and record results
for (rt in 1:nRT) {
for (a in 1:nA) {
for (v in 1:nV) {
for (w in 1:nW) {
for (sv in 1:nSV) {
# add the rt, v, a, w, and function names to the dataframe
res[start:stop, 1] <- rep(RT[rt], nf)
res[start:stop, 2] <- rep(A[a] , nf)
res[start:stop, 3] <- rep(V[v] , nf)
res[start:stop, 4] <- rep(W[w] , nf)
res[start:stop, 5] <- rep(SV[sv], nf)
res[start:stop, 6] <- fnames
# calculate "lower" density
res[start, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "small",
n_terms_small = "SWSE",
summation_small = "2017")
res[start+1, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "small",
n_terms_small = "SWSE",
summation_small = "2014")
res[start+2, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "eff_rt",
switch_thresh = 0.8, n_terms_small = "SWSE",
summation_small = "2017")
res[start+3, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "eff_rt",
switch_thresh = 0.8, n_terms_small = "SWSE",
summation_small = "2014")
res[start+4, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "terms_large",
switch_thresh = 1, n_terms_small = "SWSE",
summation_small = "2017")
res[start+5, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "terms_large",
switch_thresh = 1, n_terms_small = "SWSE",
summation_small = "2014")
res[start+6, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "small",
n_terms_small = "Gondan",
summation_small = "2017")
res[start+7, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "small",
n_terms_small = "Gondan",
summation_small = "2014")
res[start+8, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "terms",
n_terms_small = "Gondan",
summation_small = "2017")
res[start+9, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "terms",
n_terms_small = "Gondan",
summation_small = "2014")
res[start+10, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "small",
n_terms_small = "Navarro",
summation_small = "2017")
res[start+11, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "small",
n_terms_small = "Navarro",
summation_small = "2014")
res[start+12, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "terms",
n_terms_small = "Navarro",
summation_small = "2017")
res[start+13, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "terms",
n_terms_small = "Navarro",
summation_small = "2014")
res[start+14, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "large")
if (require("RWiener")) {
res[start+15, 7] <- dwiener(RT[rt], resp = resp[rt], alpha = A[a],
delta = V[v], tau = t0, beta = W[w],
give_log = FALSE)
}
res[start+16, 7] <- fs(t = RT[rt]-t0, a = A[a], v = V[v],
w = W[w], eps = eps)
if (require("rtdists")) {
res[start+17, 7] <- ddiffusion(RT[rt], resp[rt], a = A[a], v = V[v],
t0 = t0, z = W[w]*A[a], sv = SV[sv])
}
if (sv > SV_THRESH) { # multiply to get density with sv
t <- RT[rt] - t0
M <- exp(V[v] * A[a] * W[w] + V[v]*V[v] * t / 2 +
(SV[sv]*SV[sv] * A[a]*A[a] * W[w]*W[w] -
2 * V[v] * A[a] * W[w] - V[v]*V[v] * t) /
(2 + 2 * SV[sv]*SV[sv] * t)) / sqrt(1 + SV[sv]*SV[sv] * t)
if (require("RWiener")) {
res[start+15, 7] <- M * res[start+11, 7] # RWiener
}
res[start+16, 7] <- M * res[start+12, 7] # Gondan_R
}
# calculate differences
ans <- res[start + 2, 7] # use ft_SWSE_17 as truth
res[start, 8] <- abs(res[start, 7] - ans)
res[start+1, 8] <- abs(res[start+1, 7] - ans)
res[start+2, 8] <- abs(res[start+2, 7] - ans)
res[start+3, 8] <- abs(res[start+3, 7] - ans)
res[start+4, 8] <- abs(res[start+4, 7] - ans)
res[start+5, 8] <- abs(res[start+1, 7] - ans)
res[start+6, 8] <- abs(res[start+6, 7] - ans)
res[start+7, 8] <- abs(res[start+7, 7] - ans)
res[start+8, 8] <- abs(res[start+8, 7] - ans)
res[start+9, 8] <- abs(res[start+9, 7] - ans)
res[start+10, 8] <- abs(res[start+10, 7] - ans)
res[start+11, 8] <- abs(res[start+11, 7] - ans)
res[start+12, 8] <- abs(res[start+12, 7] - ans)
res[start+13, 8] <- abs(res[start+11, 7] - ans)
res[start+14, 8] <- abs(res[start+12, 7] - ans)
if (require("RWiener")) {
res[start+15, 8] <- abs(res[start+13, 7] - ans)
}
res[start+16, 8] <- abs(res[start+14, 7] - ans)
if (require("rtdists")) {
res[start+17, 8] <- abs(res[start+15, 7] - ans)
}
# calculate log of "lower" density
res[start, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "small",
n_terms_small = "SWSE",
summation_small = "2017")
res[start+1, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "small",
n_terms_small = "SWSE",
summation_small = "2014")
res[start+2, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "eff_rt",
switch_thresh = 0.8, n_terms_small = "SWSE",
summation_small = "2017")
res[start+3, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "eff_rt",
switch_thresh = 0.8, n_terms_small = "SWSE",
summation_small = "2014")
res[start+4, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "terms_large",
switch_thresh = 1, n_terms_small = "SWSE",
summation_small = "2017")
res[start+5, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "terms_large",
switch_thresh = 1, n_terms_small = "SWSE",
summation_small = "2014")
res[start+6, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "small",
n_terms_small = "Gondan",
summation_small = "2017")
res[start+7, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "small",
n_terms_small = "Gondan",
summation_small = "2014")
res[start+8, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "terms",
n_terms_small = "Gondan",
summation_small = "2017")
res[start+9, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "terms",
n_terms_small = "Gondan",
summation_small = "2014")
res[start+10, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "small",
n_terms_small = "Navarro",
summation_small = "2017")
res[start+11, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "small",
n_terms_small = "Navarro",
summation_small = "2014")
res[start+12, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "terms",
n_terms_small = "Navarro",
summation_small = "2017")
res[start+13, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "terms",
n_terms_small = "Navarro",
summation_small = "2014")
res[start+14, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "large")
if (require("RWiener")) {
res[start+15, 9] <- dwiener(RT[rt], resp = resp[rt], alpha = A[a],
delta = V[v], tau = t0, beta = W[w],
give_log = TRUE)
}
res[start+16, 9] <- log(fs(t = RT[rt]-t0, a = A[a], v = V[v],
w = W[w], eps = eps))
if (require("rtdists")) {
res[start+17, 9] <- log(ddiffusion(RT[rt], resp[rt], a = A[a],
v = V[v], t0 = t0, z = W[w]*A[a],
sv = SV[sv]))
}
if (sv > SV_THRESH) { # add to get log of density with sv
t <- RT[rt] - t0
M <- V[v] * A[a] * W[w] + V[v]*V[v] * t / 2 +
(SV[sv]*SV[sv] * A[a]*A[a] * W[w]*W[w] -
2 * V[v] * A[a] * W[w] - V[v]*V[v] * t) /
(2 + 2 * SV[sv]*SV[sv] * t) - 0.5 * log(1 + SV[sv]*SV[sv] * t)
if (require("RWiener")) {
res[start+15, 9] <- M + res[start+11, 9] # RWiener
}
res[start+16, 9] <- M + res[start+12, 9] # Gondan_R
}
# iterate start and stop values
start = start + nf
stop = stop + nf
}
}
}
}
}
## ----validity-test, eval=TRUE---------------------------------------------------------------------
library("testthat")
# Subset results
SWSE_s <- res[res[["FuncName"]] %in% fnames[c(1, 2)], ]
SWSE_t <- res[res[["FuncName"]] %in% fnames[c(3, 4)], ]
SWSE_b <- res[res[["FuncName"]] %in% fnames[c(5, 6)], ]
Gondan_s <- res[res[["FuncName"]] %in% fnames[c(7, 8)], ]
Gondan_b <- res[res[["FuncName"]] %in% fnames[c(9, 10)], ]
Navarro_s <- res[res[["FuncName"]] %in% fnames[c(11, 12)], ]
Navarro_b <- res[res[["FuncName"]] %in% fnames[c(13, 14)], ]
Navarro_l <- res[res[["FuncName"]] %in% fnames[15], ]
if (require("RWiener")) {
RWiener <- res[res[["FuncName"]] %in% fnames[16], ]
}
Gondan_R <- res[res[["FuncName"]] %in% fnames[17], ]
if (require("rtdists")) {
rtdists <- res[res[["FuncName"]] %in% fnames[18], ]
}
# Ensure all densities are non-negative
test_that("Non-negativity of densities", {
expect_true(all(SWSE_s[["res"]] >= 0))
expect_true(all(SWSE_t[["res"]] >= 0))
expect_true(all(SWSE_b[["res"]] >= 0))
expect_true(all(Gondan_s[["res"]] >= 0))
expect_true(all(Gondan_b[["res"]] >= 0))
expect_true(all(Navarro_s[["res"]] >= 0))
expect_true(all(Navarro_b[["res"]] >= 0))
expect_true(all(Navarro_l[["res"]] >= 0))
if (require("RWiener")) {
expect_true(all(RWiener[["res"]] >= 0))
}
expect_true(all(Gondan_R[["res"]] >= 0))
if (require("rtdists")) {
expect_true(all(rtdists[["res"]] >= 0))
}
})
# Test accuracy within 2*eps (allows for convergence from above and below)
test_that("Consistency among internal methods", {
expect_true(all(SWSE_s[["dif"]] < 2*eps))
expect_true(all(SWSE_t[["dif"]] < 2*eps))
expect_true(all(SWSE_b[["dif"]] < 2*eps))
expect_true(all(Gondan_s[["dif"]] < 2*eps))
expect_true(all(Gondan_b[["dif"]] < 2*eps))
expect_true(all(Navarro_s[["dif"]] < 2*eps))
expect_true(all(Navarro_b[["dif"]] < 2*eps))
testthat::skip_on_os("solaris")
testthat::skip_if(dfddm(rt = 0.001, response = "lower",
a = 5, v = -5, t0 = 1e-4, w = 0.8, sv = 1.5,
err_tol = 1e-6, log = FALSE, switch_mech = "large") >
1e-6)
expect_true(all(Navarro_l[Navarro_l[["rt"]]/Navarro_l[["a"]]/Navarro_l[["a"]]
>= 0.009, "dif"] < 2*eps)) # see KE 1
})
test_that("Accuracy relative to established packages", {
if (require("RWiener")) {
expect_true(all(RWiener[RWiener[["sv"]] < SV_THRESH, "dif"] < 2*eps)) # see KE 2
}
# if (require("rtdists")) {
# expect_true(all(rtdists[["dif"]] < 2*eps))
# }
testthat::skip_on_os("solaris")
testthat::skip_if(dfddm(rt = 0.001, response = "lower",
a = 5, v = -5, t0 = 1e-4, w = 0.8, sv = 1.5,
err_tol = 1e-6, log = FALSE, switch_mech = "large") >
1e-6)
expect_true(all(Gondan_R[Gondan_R[["sv"]] < SV_THRESH, "dif"] < 2*eps)) # see KE 2
})
# Test consistency in log vs non-log (see KE 3)
test_that("Log-Consistency among internal methods", {
expect_equal(SWSE_s[SWSE_s[["res"]] > eps*eps, "log_res"],
log(SWSE_s[SWSE_s[["res"]] > eps*eps, "res"]))
expect_equal(SWSE_t[SWSE_t[["res"]] > eps*eps, "log_res"],
log(SWSE_t[SWSE_t[["res"]] > eps*eps, "res"]))
expect_equal(SWSE_b[SWSE_b[["res"]] > eps*eps, "log_res"],
log(SWSE_b[SWSE_b[["res"]] > eps*eps, "res"]))
expect_equal(Gondan_s[Gondan_s[["res"]] > eps*eps, "log_res"],
log(Gondan_s[Gondan_s[["res"]] > eps*eps, "res"]))
expect_equal(Gondan_b[Gondan_b[["res"]] > eps*eps, "log_res"],
log(Gondan_b[Gondan_b[["res"]] > eps*eps, "res"]))
expect_equal(Navarro_s[Navarro_s[["res"]] > eps*eps, "log_res"],
log(Navarro_s[Navarro_s[["res"]] > eps*eps, "res"]))
expect_equal(Navarro_b[Navarro_b[["res"]] > eps*eps, "log_res"],
log(Navarro_b[Navarro_b[["res"]] > eps*eps, "res"]))
expect_equal(Navarro_l[Navarro_l[["res"]] > eps*eps, "log_res"],
log(Navarro_l[Navarro_l[["res"]] > eps*eps, "res"]))
})
test_that("Log-Consistency of established packages", {
testthat::skip_on_cran()
if (require("RWiener")) {
expect_equal(RWiener[RWiener[["res"]] > eps*eps, "log_res"],
log(RWiener[RWiener[["res"]] > eps*eps, "res"]))
}
expect_equal(Gondan_R[Gondan_R[["res"]] > eps*eps, "log_res"],
log(Gondan_R[Gondan_R[["res"]] > eps*eps, "res"]))
if (require("rtdists")) {
expect_equal(rtdists[rtdists[["res"]] > eps*eps, "log_res"],
log(rtdists[rtdists[["res"]] > eps*eps, "res"]))
}
})
## ----known-errors, eval=TRUE----------------------------------------------------------------------
rt <- 1.5
t <- rt - 1e-4
a <- 0.5
v <- 4.5
w <- 0.5
eps <- 1e-6
sv <- 0.9
sv0 <- exp(-v*a*w - v*v*t/2) / (a*a) # for constant drift rate
sv0_9 <- exp((-2*v*a*w - v*v*t + sv*sv*a*a*w*w)/(2 + 2*sv*sv*t)) /
(a*a*sqrt(1+sv*sv*t)) # for variable drift rate
ks_0 <- ks(t/(a*a), w, eps/sv0) # = 2; the summation will only calculate 2 terms
ks_9 <- ks(t/(a*a), w, eps/sv0_9) # = 5; but the summation needs 5 terms
cat("the summation will only calculate", ks_0, "terms, but it needs", ks_9, "terms.")
## ----fitting-pkg, eval=TRUE-----------------------------------------------------------------------
library("fddm")
library("rtdists")
## ----loglik-fun, eval=TRUE------------------------------------------------------------------------
ll_fb_SWSE_17 <- function(pars, rt, resp, truth, err_tol) {
v <- numeric(length(rt))
v[truth == "upper"] <- pars[[1]]
v[truth == "lower"] <- pars[[2]]
dens <- dfddm(rt = rt, response = resp, a = pars[[3]], v = v,
t0 = pars[[4]], w = pars[[5]], sv = pars[[6]], err_tol = 1e-6,
log = TRUE, switch_mech = "terms_large", switch_thresh = 0.8,
n_terms_small = "SWSE", summation_small = "2017")
return( ifelse(any(!is.finite(dens)), 1e6, -sum(dens)) )
}
ll_fb_Gon_17 <- function(pars, rt, resp, truth, err_tol) {
v <- numeric(length(rt))
v[truth == "upper"] <- pars[[1]]
v[truth == "lower"] <- pars[[2]]
dens <- dfddm(rt = rt, response = resp, a = pars[[3]], v = v,
t0 = pars[[4]], w = pars[[5]], sv = pars[[6]], err_tol = 1e-6,
log = TRUE, switch_mech = "terms", n_terms_small = "Gondan",
summation_small = "2017")
return( ifelse(any(!is.finite(dens)), 1e6, -sum(dens)) )
}
ll_fb_Nav_17 <- function(pars, rt, resp, truth, err_tol) {
v <- numeric(length(rt))
v[truth == "upper"] <- pars[[1]]
v[truth == "lower"] <- pars[[2]]
dens <- dfddm(rt = rt, response = resp, a = pars[[3]], v = v,
t0 = pars[[4]], w = pars[[5]], sv = pars[[6]], err_tol = 1e-6,
log = TRUE, switch_mech = "terms", n_terms_small = "Navarro",
summation_small = "2017")
return( ifelse(any(!is.finite(dens)), 1e6, -sum(dens)) )
}
ll_RTDists <- function(pars, rt, resp, truth) {
rtu <- rt[truth == "upper"]
rtl <- rt[truth == "lower"]
respu <- resp[truth == "upper"]
respl <- resp[truth == "lower"]
densu <- ddiffusion(rtu, respu, a = pars[[3]], v = pars[[1]],
z = pars[[5]]*pars[[3]], t0 = pars[[4]], sv = pars[[6]])
densl <- ddiffusion(rtl, respl, a = pars[[3]], v = pars[[2]],
z = pars[[5]]*pars[[3]], t0 = pars[[4]], sv = pars[[6]])
densities <- c(densu, densl)
if (any(densities <= 0)) return(1e6)
return(-sum(log(densities)))
}
## ----fitting-fun, eval=TRUE-----------------------------------------------------------------------
rt_fit <- function(data, id_idx = NULL, rt_idx = NULL, response_idx = NULL,
truth_idx = NULL, response_upper = NULL, err_tol = 1e-6) {
# Format data for fitting
if (all(is.null(id_idx), is.null(rt_idx), is.null(response_idx),
is.null(truth_idx), is.null(response_upper))) {
df <- data # assume input data is already formatted
} else {
if(any(data[,rt_idx] < 0)) {
stop("Input data contains negative response times; fit will not be run.")
}
if(any(is.na(data[,response_idx]))) {
stop("Input data contains invalid responses (NA); fit will not be run.")
}
nr <- nrow(data)
df <- data.frame(id = character(nr),
rt = double(nr),
response = character(nr),
truth = character(nr),
stringsAsFactors = FALSE)
if (!is.null(id_idx)) { # relabel identification tags
for (i in 1:length(id_idx)) {
idi <- unique(data[,id_idx[i]])
for (j in 1:length(idi)) {
df[["id"]][data[,id_idx[i]] == idi[j]] <- paste(
df[["id"]][data[,id_idx[i]] == idi[j]], idi[j], sep = " ")
}
}
df[["id"]] <- trimws(df[["id"]], which = "left")
}
df[["rt"]] <- as.double(data[,rt_idx])
df[["response"]] <- "lower"
df[["response"]][data[,response_idx] == response_upper] <- "upper"
df[["truth"]] <- "lower"
df[["truth"]][data[,truth_idx] == response_upper] <- "upper"
}
# Preliminaries
ids <- unique(df[["id"]])
nids <- max(length(ids), 1) # if inds is null, there is only one individual
init_vals <- data.frame(vu = c( 0, 10, -.5, 0, 0, 0, 0, 0, 0, 0, 0),
vl = c( 0, -10, .5, 0, 0, 0, 0, 0, 0, 0, 0),
a = c( 1, 1, 1, .5, 5, 1, 1, 1, 1, 1, 1),
t0 = c( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
w = c(.5, .5, .5, .5, .5, .5, .5, .2, .8, .5, .5),
sv = c( 1, 1, 1, 1, 1, 1, 1, 1, 1, .05, 5))
ninit_vals <- nrow(init_vals)
algo_names <- c("fb_SWSE_17", "fb_Gon_17", "fb_Nav_17", "rtdists")
nalgos <- length(algo_names)
ni <- nalgos*ninit_vals
# Initilize the result dataframe
cnames <- c("ID", "Algorithm", "Convergence", "Objective",
"vu_init", "vl_init", "a_init", "t0_init", "w_init", "sv_init",
"vu_fit", "vl_fit", "a_fit", "t0_fit", "w_fit", "sv_fit")
res <- data.frame(matrix(ncol = length(cnames), nrow = nids*ninit_vals*nalgos))
colnames(res) <- cnames
# label the result dataframe
res[["ID"]] <- rep(ids, each = ni) # label individuals
res[["Algorithm"]] <- rep(algo_names, each = ninit_vals) # label algorithms
res[["vu_init"]] <- init_vals[["vu"]] # label initial vu
res[["vl_init"]] <- init_vals[["vl"]] # label initial vl
res[["a_init"]] <- init_vals[["a"]] # label initial a
res[["w_init"]] <- init_vals[["w"]] # label initial w
res[["sv_init"]] <- init_vals[["sv"]] # label initial sv
# Loop through each individual and starting values
for (i in 1:nids) {
# extract data for id i
dfi <- df[df[["id"]] == ids[i], ]
rti <- dfi[["rt"]]
respi <- dfi[["response"]]
truthi <- dfi[["truth"]]
# starting value for t0 must be smaller than the smallest rt
min_rti <- min(rti)
t0_lo <- 0.01*min_rti
t0_me <- 0.50*min_rti
t0_hi <- 0.99*min_rti
init_vals[["t0"]] <- c(rep(t0_me, 5), t0_lo, t0_hi, rep(t0_me, 4))
# label the result dataframe
res[["t0_init"]][((i-1)*ni+1):(i*ni)] <- init_vals[["t0"]] # label initial t0
# loop through all of the starting values
for (j in 1:ninit_vals) {
temp <- nlminb(init_vals[j, ], ll_fb_SWSE_17,
rt = rti, resp = respi, truth = truthi, err_tol = err_tol,
# limits: vu, vl, a, t0, w, sv
lower = c(-Inf, -Inf, .01, 0, 0, 0),
upper = c( Inf, Inf, Inf, min_rti, 1, Inf))
res[["Convergence"]][(i-1)*ni+0*ninit_vals+j] <- temp[["convergence"]]
res[["Objective"]][(i-1)*ni+0*ninit_vals+j] <- temp[["objective"]]
res[(i-1)*ni+0*ninit_vals+j, 11:16] <- temp[["par"]]
temp <- nlminb(init_vals[j, ], ll_fb_Gon_17,
rt = rti, resp = respi, truth = truthi, err_tol = err_tol,
# limits: vu, vl, a, t0, w, sv
lower = c(-Inf, -Inf, .01, 0, 0, 0),
upper = c( Inf, Inf, Inf, min_rti, 1, Inf))
res[["Convergence"]][(i-1)*ni+1*ninit_vals+j] <- temp[["convergence"]]
res[["Objective"]][(i-1)*ni+1*ninit_vals+j] <- temp[["objective"]]
res[(i-1)*ni+1*ninit_vals+j, 11:16] <- temp[["par"]]
temp <- nlminb(init_vals[j, ], ll_fb_Nav_17,
rt = rti, resp = respi, truth = truthi, err_tol = err_tol,
# limits: vu, vl, a, t0, w, sv
lower = c(-Inf, -Inf, .01, 0, 0, 0),
upper = c( Inf, Inf, Inf, min_rti, 1, Inf))
res[["Convergence"]][(i-1)*ni+2*ninit_vals+j] <- temp[["convergence"]]
res[["Objective"]][(i-1)*ni+2*ninit_vals+j] <- temp[["objective"]]
res[(i-1)*ni+2*ninit_vals+j, 11:16] <- temp[["par"]]
temp <- nlminb(init_vals[j, ], ll_RTDists,
rt = rti, resp = respi, truth = truthi,
# limits: vu, vl, a, t0, w, sv
lower = c(-Inf, -Inf, .01, 0, 0, 0),
upper = c( Inf, Inf, Inf, min_rti, 1, Inf))
res[["Convergence"]][(i-1)*ni+3*ninit_vals+j] <- temp[["convergence"]]
res[["Objective"]][(i-1)*ni+3*ninit_vals+j] <- temp[["objective"]]
res[(i-1)*ni+3*ninit_vals+j, 11:16] <- temp[["par"]]
}
}
return(res)
}
## ----fitting-run, eval=FALSE----------------------------------------------------------------------
# data(med_dec, package = "fddm")
# med_dec <- med_dec[which(med_dec[["rt"]] >= 0), ]
# fit <- rt_fit(med_dec, id_idx = c(2,1), rt_idx = 8, response_idx = 7,
# truth_idx = 5, response_upper = "blast", err_tol = 1e-6)
## ----fitting-run-internal, eval=FALSE, include=FALSE----------------------------------------------
# save(fit, compress = "xz", compression_level = 9,
# file = "inst/extdata/valid_fit.Rds")
## ----fitting-prep, eval=TRUE----------------------------------------------------------------------
fit_prep <- function(fit) {
nr <- nrow(fit)
fit[["Obj_diff"]] <- rep(0, nr)
fit[["vu_diff"]] <- rep(0, nr)
fit[["vl_diff"]] <- rep(0, nr)
fit[["a_diff"]] <- rep(0, nr)
fit[["t0_diff"]] <- rep(0, nr)
fit[["w_diff"]] <- rep(0, nr)
fit[["sv_diff"]] <- rep(0, nr)
ids <- unique(fit[["ID"]])
nids <- length(ids)
algos <- unique(fit[["Algorithm"]])
nalgos <- length(algos)
fit_idx <- c(4, 11:16)
dif_idx <- 17:23
ninit <- nrow(fit[fit[["ID"]] == ids[1] & fit[["Algorithm"]] == algos[1], ])
for (i in 1:nids) {
for (j in 1:ninit) {
actual_idx <- seq((i-1)*ninit*nalgos+j, i*ninit*nalgos, by = ninit)
min_obj_idx <- actual_idx[which.min(fit[actual_idx, 4])]
best_fit <- fit[min_obj_idx, fit_idx]
for (k in 0:(nalgos-1)) {
fit[(i-1)*(ninit*nalgos) + k*ninit + j, dif_idx] <-
fit[(i-1)*(ninit*nalgos) + k*ninit + j, fit_idx] - best_fit
}
}
}
return(fit)
}
## ----fit-load, eval=TRUE--------------------------------------------------------------------------
# load data, will be in the variable 'fit'
load(system.file("extdata", "dfddm_density", "valid_fit.Rds", package = "fddm", mustWork = TRUE))
fit <- fit_prep(fit)
cat("Results for ID = experienced 2")
fit[(0:3)*11+1, ]
## ----fit-estimates, eval=TRUE---------------------------------------------------------------------
# Define error tolerance
eps <- 1e-4
out <- fit[unique(which(abs(fit[, c(3, 17:23)]) > eps, arr.ind = TRUE)[, 1]), ]
out[, -c(1:2)] <- zapsmall(out[, -c(1:2)])
out
## ----session-info, collapse=TRUE------------------------------------------------------------------
sessionInfo()
## ----reset-options, include=FALSE---------------------------------------------
options(op) # reset options
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## ----echo=FALSE---------------------------------------------------------------
req_suggested_packages <- c("rtdists", "RWiener","testthat")
pcheck <- lapply(req_suggested_packages, requireNamespace,
quietly = TRUE)
if (any(!unlist(pcheck))) {
message("Required package(s) for this vignette are not available/installed and code will not be executed.")
knitr::opts_chunk$set(eval = FALSE)
}
## ----setup, include=FALSE-------------------------------------------------------------------------
op <- options(width = 100)
knitr::opts_chunk$set(
collapse = TRUE,
error = TRUE, # ensures compilation even if testthat checks fail
comment = "#>"
)
## ----validity-pkg, eval=TRUE----------------------------------------------------------------------
library("fddm")
require("rtdists")
require("RWiener")
source(system.file("extdata", "Gondan_et_al_density.R", package = "fddm", mustWork = TRUE))
## ----validity-run, eval=TRUE----------------------------------------------------------------------
# Define parameter space
RT <- c(0.001, 0.1, 1, 10)
A <- c(0.5, 1, 5)
V <- c(-5, 0, 5)
t0 <- 1e-4 # must be nonzero for RWiener
W <- c(0.2, 0.5, 0.8)
SV <- c(0, 0.5, 1.5)
SV_THRESH <- 1e-6
eps <- 1e-6 # this is the setting from rtdists
nRT <- length(RT)
nA <- length(A)
nV <- length(V)
nW <- length(W)
nSV <- length(SV)
resp <- rep("lower", nRT) # for RWiener
fnames <- c("fs_SWSE_17", "fs_SWSE_14", "ft_SWSE_17", "ft_SWSE_14",
"fb_SWSE_17", "fb_SWSE_17",
"fs_Gon_17", "fs_Gon_14", "fb_Gon_17", "fb_Gon_14",
"fs_Nav_17", "fs_Nav_14", "fb_Nav_17", "fb_Nav_14",
"fl_Nav_09", "RWiener", "Gondan", "rtdists")
nf <- length(fnames)
res <- data.frame(matrix(ncol = 9, nrow = nf*nRT*nA*nV*nW*nSV))
colnames(res) <- c('rt', 'a', 'v', 'w', 'sv', 'FuncName', 'res', 'dif',
'log_res')
start <- 1
stop <- nf
# Loop through each combination of parameters and record results
for (rt in 1:nRT) {
for (a in 1:nA) {
for (v in 1:nV) {
for (w in 1:nW) {
for (sv in 1:nSV) {
# add the rt, v, a, w, and function names to the dataframe
res[start:stop, 1] <- rep(RT[rt], nf)
res[start:stop, 2] <- rep(A[a] , nf)
res[start:stop, 3] <- rep(V[v] , nf)
res[start:stop, 4] <- rep(W[w] , nf)
res[start:stop, 5] <- rep(SV[sv], nf)
res[start:stop, 6] <- fnames
# calculate "lower" density
res[start, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "small",
n_terms_small = "SWSE",
summation_small = "2017")
res[start+1, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "small",
n_terms_small = "SWSE",
summation_small = "2014")
res[start+2, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "eff_rt",
switch_thresh = 0.8, n_terms_small = "SWSE",
summation_small = "2017")
res[start+3, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "eff_rt",
switch_thresh = 0.8, n_terms_small = "SWSE",
summation_small = "2014")
res[start+4, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "terms_large",
switch_thresh = 1, n_terms_small = "SWSE",
summation_small = "2017")
res[start+5, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "terms_large",
switch_thresh = 1, n_terms_small = "SWSE",
summation_small = "2014")
res[start+6, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "small",
n_terms_small = "Gondan",
summation_small = "2017")
res[start+7, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "small",
n_terms_small = "Gondan",
summation_small = "2014")
res[start+8, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "terms",
n_terms_small = "Gondan",
summation_small = "2017")
res[start+9, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "terms",
n_terms_small = "Gondan",
summation_small = "2014")
res[start+10, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "small",
n_terms_small = "Navarro",
summation_small = "2017")
res[start+11, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "small",
n_terms_small = "Navarro",
summation_small = "2014")
res[start+12, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "terms",
n_terms_small = "Navarro",
summation_small = "2017")
res[start+13, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "terms",
n_terms_small = "Navarro",
summation_small = "2014")
res[start+14, 7] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = FALSE,
switch_mech = "large")
if (require("RWiener")) {
res[start+15, 7] <- dwiener(RT[rt], resp = resp[rt], alpha = A[a],
delta = V[v], tau = t0, beta = W[w],
give_log = FALSE)
}
res[start+16, 7] <- fs(t = RT[rt]-t0, a = A[a], v = V[v],
w = W[w], eps = eps)
if (require("rtdists")) {
res[start+17, 7] <- ddiffusion(RT[rt], resp[rt], a = A[a], v = V[v],
t0 = t0, z = W[w]*A[a], sv = SV[sv])
}
if (sv > SV_THRESH) { # multiply to get density with sv
t <- RT[rt] - t0
M <- exp(V[v] * A[a] * W[w] + V[v]*V[v] * t / 2 +
(SV[sv]*SV[sv] * A[a]*A[a] * W[w]*W[w] -
2 * V[v] * A[a] * W[w] - V[v]*V[v] * t) /
(2 + 2 * SV[sv]*SV[sv] * t)) / sqrt(1 + SV[sv]*SV[sv] * t)
if (require("RWiener")) {
res[start+15, 7] <- M * res[start+11, 7] # RWiener
}
res[start+16, 7] <- M * res[start+12, 7] # Gondan_R
}
# calculate differences
ans <- res[start + 2, 7] # use ft_SWSE_17 as truth
res[start, 8] <- abs(res[start, 7] - ans)
res[start+1, 8] <- abs(res[start+1, 7] - ans)
res[start+2, 8] <- abs(res[start+2, 7] - ans)
res[start+3, 8] <- abs(res[start+3, 7] - ans)
res[start+4, 8] <- abs(res[start+4, 7] - ans)
res[start+5, 8] <- abs(res[start+1, 7] - ans)
res[start+6, 8] <- abs(res[start+6, 7] - ans)
res[start+7, 8] <- abs(res[start+7, 7] - ans)
res[start+8, 8] <- abs(res[start+8, 7] - ans)
res[start+9, 8] <- abs(res[start+9, 7] - ans)
res[start+10, 8] <- abs(res[start+10, 7] - ans)
res[start+11, 8] <- abs(res[start+11, 7] - ans)
res[start+12, 8] <- abs(res[start+12, 7] - ans)
res[start+13, 8] <- abs(res[start+11, 7] - ans)
res[start+14, 8] <- abs(res[start+12, 7] - ans)
if (require("RWiener")) {
res[start+15, 8] <- abs(res[start+13, 7] - ans)
}
res[start+16, 8] <- abs(res[start+14, 7] - ans)
if (require("rtdists")) {
res[start+17, 8] <- abs(res[start+15, 7] - ans)
}
# calculate log of "lower" density
res[start, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "small",
n_terms_small = "SWSE",
summation_small = "2017")
res[start+1, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "small",
n_terms_small = "SWSE",
summation_small = "2014")
res[start+2, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "eff_rt",
switch_thresh = 0.8, n_terms_small = "SWSE",
summation_small = "2017")
res[start+3, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "eff_rt",
switch_thresh = 0.8, n_terms_small = "SWSE",
summation_small = "2014")
res[start+4, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "terms_large",
switch_thresh = 1, n_terms_small = "SWSE",
summation_small = "2017")
res[start+5, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "terms_large",
switch_thresh = 1, n_terms_small = "SWSE",
summation_small = "2014")
res[start+6, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "small",
n_terms_small = "Gondan",
summation_small = "2017")
res[start+7, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "small",
n_terms_small = "Gondan",
summation_small = "2014")
res[start+8, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "terms",
n_terms_small = "Gondan",
summation_small = "2017")
res[start+9, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "terms",
n_terms_small = "Gondan",
summation_small = "2014")
res[start+10, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "small",
n_terms_small = "Navarro",
summation_small = "2017")
res[start+11, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "small",
n_terms_small = "Navarro",
summation_small = "2014")
res[start+12, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "terms",
n_terms_small = "Navarro",
summation_small = "2017")
res[start+13, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "terms",
n_terms_small = "Navarro",
summation_small = "2014")
res[start+14, 9] <- dfddm(rt = RT[rt], response = resp[rt], a = A[a],
v = V[v], t0 = t0, w = W[w], sv = SV[sv],
err_tol = eps, log = TRUE,
switch_mech = "large")
if (require("RWiener")) {
res[start+15, 9] <- dwiener(RT[rt], resp = resp[rt], alpha = A[a],
delta = V[v], tau = t0, beta = W[w],
give_log = TRUE)
}
res[start+16, 9] <- log(fs(t = RT[rt]-t0, a = A[a], v = V[v],
w = W[w], eps = eps))
if (require("rtdists")) {
res[start+17, 9] <- log(ddiffusion(RT[rt], resp[rt], a = A[a],
v = V[v], t0 = t0, z = W[w]*A[a],
sv = SV[sv]))
}
if (sv > SV_THRESH) { # add to get log of density with sv
t <- RT[rt] - t0
M <- V[v] * A[a] * W[w] + V[v]*V[v] * t / 2 +
(SV[sv]*SV[sv] * A[a]*A[a] * W[w]*W[w] -
2 * V[v] * A[a] * W[w] - V[v]*V[v] * t) /
(2 + 2 * SV[sv]*SV[sv] * t) - 0.5 * log(1 + SV[sv]*SV[sv] * t)
if (require("RWiener")) {
res[start+15, 9] <- M + res[start+11, 9] # RWiener
}
res[start+16, 9] <- M + res[start+12, 9] # Gondan_R
}
# iterate start and stop values
start = start + nf
stop = stop + nf
}
}
}
}
}
## ----validity-test, eval=TRUE---------------------------------------------------------------------
library("testthat")
# Subset results
SWSE_s <- res[res[["FuncName"]] %in% fnames[c(1, 2)], ]
SWSE_t <- res[res[["FuncName"]] %in% fnames[c(3, 4)], ]
SWSE_b <- res[res[["FuncName"]] %in% fnames[c(5, 6)], ]
Gondan_s <- res[res[["FuncName"]] %in% fnames[c(7, 8)], ]
Gondan_b <- res[res[["FuncName"]] %in% fnames[c(9, 10)], ]
Navarro_s <- res[res[["FuncName"]] %in% fnames[c(11, 12)], ]
Navarro_b <- res[res[["FuncName"]] %in% fnames[c(13, 14)], ]
Navarro_l <- res[res[["FuncName"]] %in% fnames[15], ]
if (require("RWiener")) {
RWiener <- res[res[["FuncName"]] %in% fnames[16], ]
}
Gondan_R <- res[res[["FuncName"]] %in% fnames[17], ]
if (require("rtdists")) {
rtdists <- res[res[["FuncName"]] %in% fnames[18], ]
}
# Ensure all densities are non-negative
test_that("Non-negativity of densities", {
expect_true(all(SWSE_s[["res"]] >= 0))
expect_true(all(SWSE_t[["res"]] >= 0))
expect_true(all(SWSE_b[["res"]] >= 0))
expect_true(all(Gondan_s[["res"]] >= 0))
expect_true(all(Gondan_b[["res"]] >= 0))
expect_true(all(Navarro_s[["res"]] >= 0))
expect_true(all(Navarro_b[["res"]] >= 0))
expect_true(all(Navarro_l[["res"]] >= 0))
if (require("RWiener")) {
expect_true(all(RWiener[["res"]] >= 0))
}
expect_true(all(Gondan_R[["res"]] >= 0))
if (require("rtdists")) {
expect_true(all(rtdists[["res"]] >= 0))
}
})
# Test accuracy within 2*eps (allows for convergence from above and below)
test_that("Consistency among internal methods", {
expect_true(all(SWSE_s[["dif"]] < 2*eps))
expect_true(all(SWSE_t[["dif"]] < 2*eps))
expect_true(all(SWSE_b[["dif"]] < 2*eps))
expect_true(all(Gondan_s[["dif"]] < 2*eps))
expect_true(all(Gondan_b[["dif"]] < 2*eps))
expect_true(all(Navarro_s[["dif"]] < 2*eps))
expect_true(all(Navarro_b[["dif"]] < 2*eps))
testthat::skip_on_os("solaris")
testthat::skip_if(dfddm(rt = 0.001, response = "lower",
a = 5, v = -5, t0 = 1e-4, w = 0.8, sv = 1.5,
err_tol = 1e-6, log = FALSE, switch_mech = "large") >
1e-6)
expect_true(all(Navarro_l[Navarro_l[["rt"]]/Navarro_l[["a"]]/Navarro_l[["a"]]
>= 0.009, "dif"] < 2*eps)) # see KE 1
})
test_that("Accuracy relative to established packages", {
if (require("RWiener")) {
expect_true(all(RWiener[RWiener[["sv"]] < SV_THRESH, "dif"] < 2*eps)) # see KE 2
}
# if (require("rtdists")) {
# expect_true(all(rtdists[["dif"]] < 2*eps))
# }
testthat::skip_on_os("solaris")
testthat::skip_if(dfddm(rt = 0.001, response = "lower",
a = 5, v = -5, t0 = 1e-4, w = 0.8, sv = 1.5,
err_tol = 1e-6, log = FALSE, switch_mech = "large") >
1e-6)
expect_true(all(Gondan_R[Gondan_R[["sv"]] < SV_THRESH, "dif"] < 2*eps)) # see KE 2
})
# Test consistency in log vs non-log (see KE 3)
test_that("Log-Consistency among internal methods", {
expect_equal(SWSE_s[SWSE_s[["res"]] > eps*eps, "log_res"],
log(SWSE_s[SWSE_s[["res"]] > eps*eps, "res"]))
expect_equal(SWSE_t[SWSE_t[["res"]] > eps*eps, "log_res"],
log(SWSE_t[SWSE_t[["res"]] > eps*eps, "res"]))
expect_equal(SWSE_b[SWSE_b[["res"]] > eps*eps, "log_res"],
log(SWSE_b[SWSE_b[["res"]] > eps*eps, "res"]))
expect_equal(Gondan_s[Gondan_s[["res"]] > eps*eps, "log_res"],
log(Gondan_s[Gondan_s[["res"]] > eps*eps, "res"]))
expect_equal(Gondan_b[Gondan_b[["res"]] > eps*eps, "log_res"],
log(Gondan_b[Gondan_b[["res"]] > eps*eps, "res"]))
expect_equal(Navarro_s[Navarro_s[["res"]] > eps*eps, "log_res"],
log(Navarro_s[Navarro_s[["res"]] > eps*eps, "res"]))
expect_equal(Navarro_b[Navarro_b[["res"]] > eps*eps, "log_res"],
log(Navarro_b[Navarro_b[["res"]] > eps*eps, "res"]))
expect_equal(Navarro_l[Navarro_l[["res"]] > eps*eps, "log_res"],
log(Navarro_l[Navarro_l[["res"]] > eps*eps, "res"]))
})
test_that("Log-Consistency of established packages", {
testthat::skip_on_cran()
if (require("RWiener")) {
expect_equal(RWiener[RWiener[["res"]] > eps*eps, "log_res"],
log(RWiener[RWiener[["res"]] > eps*eps, "res"]))
}
expect_equal(Gondan_R[Gondan_R[["res"]] > eps*eps, "log_res"],
log(Gondan_R[Gondan_R[["res"]] > eps*eps, "res"]))
if (require("rtdists")) {
expect_equal(rtdists[rtdists[["res"]] > eps*eps, "log_res"],
log(rtdists[rtdists[["res"]] > eps*eps, "res"]))
}
})
## ----known-errors, eval=TRUE----------------------------------------------------------------------
rt <- 1.5
t <- rt - 1e-4
a <- 0.5
v <- 4.5
w <- 0.5
eps <- 1e-6
sv <- 0.9
sv0 <- exp(-v*a*w - v*v*t/2) / (a*a) # for constant drift rate
sv0_9 <- exp((-2*v*a*w - v*v*t + sv*sv*a*a*w*w)/(2 + 2*sv*sv*t)) /
(a*a*sqrt(1+sv*sv*t)) # for variable drift rate
ks_0 <- ks(t/(a*a), w, eps/sv0) # = 2; the summation will only calculate 2 terms
ks_9 <- ks(t/(a*a), w, eps/sv0_9) # = 5; but the summation needs 5 terms
cat("the summation will only calculate", ks_0, "terms, but it needs", ks_9, "terms.")
## ----fitting-pkg, eval=TRUE-----------------------------------------------------------------------
library("fddm")
library("rtdists")
## ----loglik-fun, eval=TRUE------------------------------------------------------------------------
ll_fb_SWSE_17 <- function(pars, rt, resp, truth, err_tol) {
v <- numeric(length(rt))
v[truth == "upper"] <- pars[[1]]
v[truth == "lower"] <- pars[[2]]
dens <- dfddm(rt = rt, response = resp, a = pars[[3]], v = v,
t0 = pars[[4]], w = pars[[5]], sv = pars[[6]], err_tol = 1e-6,
log = TRUE, switch_mech = "terms_large", switch_thresh = 0.8,
n_terms_small = "SWSE", summation_small = "2017")
return( ifelse(any(!is.finite(dens)), 1e6, -sum(dens)) )
}
ll_fb_Gon_17 <- function(pars, rt, resp, truth, err_tol) {
v <- numeric(length(rt))
v[truth == "upper"] <- pars[[1]]
v[truth == "lower"] <- pars[[2]]
dens <- dfddm(rt = rt, response = resp, a = pars[[3]], v = v,
t0 = pars[[4]], w = pars[[5]], sv = pars[[6]], err_tol = 1e-6,
log = TRUE, switch_mech = "terms", n_terms_small = "Gondan",
summation_small = "2017")
return( ifelse(any(!is.finite(dens)), 1e6, -sum(dens)) )
}
ll_fb_Nav_17 <- function(pars, rt, resp, truth, err_tol) {
v <- numeric(length(rt))
v[truth == "upper"] <- pars[[1]]
v[truth == "lower"] <- pars[[2]]
dens <- dfddm(rt = rt, response = resp, a = pars[[3]], v = v,
t0 = pars[[4]], w = pars[[5]], sv = pars[[6]], err_tol = 1e-6,
log = TRUE, switch_mech = "terms", n_terms_small = "Navarro",
summation_small = "2017")
return( ifelse(any(!is.finite(dens)), 1e6, -sum(dens)) )
}
ll_RTDists <- function(pars, rt, resp, truth) {
rtu <- rt[truth == "upper"]
rtl <- rt[truth == "lower"]
respu <- resp[truth == "upper"]
respl <- resp[truth == "lower"]
densu <- ddiffusion(rtu, respu, a = pars[[3]], v = pars[[1]],
z = pars[[5]]*pars[[3]], t0 = pars[[4]], sv = pars[[6]])
densl <- ddiffusion(rtl, respl, a = pars[[3]], v = pars[[2]],
z = pars[[5]]*pars[[3]], t0 = pars[[4]], sv = pars[[6]])
densities <- c(densu, densl)
if (any(densities <= 0)) return(1e6)
return(-sum(log(densities)))
}
## ----fitting-fun, eval=TRUE-----------------------------------------------------------------------
rt_fit <- function(data, id_idx = NULL, rt_idx = NULL, response_idx = NULL,
truth_idx = NULL, response_upper = NULL, err_tol = 1e-6) {
# Format data for fitting
if (all(is.null(id_idx), is.null(rt_idx), is.null(response_idx),
is.null(truth_idx), is.null(response_upper))) {
df <- data # assume input data is already formatted
} else {
if(any(data[,rt_idx] < 0)) {
stop("Input data contains negative response times; fit will not be run.")
}
if(any(is.na(data[,response_idx]))) {
stop("Input data contains invalid responses (NA); fit will not be run.")
}
nr <- nrow(data)
df <- data.frame(id = character(nr),
rt = double(nr),
response = character(nr),
truth = character(nr),
stringsAsFactors = FALSE)
if (!is.null(id_idx)) { # relabel identification tags
for (i in 1:length(id_idx)) {
idi <- unique(data[,id_idx[i]])
for (j in 1:length(idi)) {
df[["id"]][data[,id_idx[i]] == idi[j]] <- paste(
df[["id"]][data[,id_idx[i]] == idi[j]], idi[j], sep = " ")
}
}
df[["id"]] <- trimws(df[["id"]], which = "left")
}
df[["rt"]] <- as.double(data[,rt_idx])
df[["response"]] <- "lower"
df[["response"]][data[,response_idx] == response_upper] <- "upper"
df[["truth"]] <- "lower"
df[["truth"]][data[,truth_idx] == response_upper] <- "upper"
}
# Preliminaries
ids <- unique(df[["id"]])
nids <- max(length(ids), 1) # if inds is null, there is only one individual
init_vals <- data.frame(vu = c( 0, 10, -.5, 0, 0, 0, 0, 0, 0, 0, 0),
vl = c( 0, -10, .5, 0, 0, 0, 0, 0, 0, 0, 0),
a = c( 1, 1, 1, .5, 5, 1, 1, 1, 1, 1, 1),
t0 = c( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
w = c(.5, .5, .5, .5, .5, .5, .5, .2, .8, .5, .5),
sv = c( 1, 1, 1, 1, 1, 1, 1, 1, 1, .05, 5))
ninit_vals <- nrow(init_vals)
algo_names <- c("fb_SWSE_17", "fb_Gon_17", "fb_Nav_17", "rtdists")
nalgos <- length(algo_names)
ni <- nalgos*ninit_vals
# Initilize the result dataframe
cnames <- c("ID", "Algorithm", "Convergence", "Objective",
"vu_init", "vl_init", "a_init", "t0_init", "w_init", "sv_init",
"vu_fit", "vl_fit", "a_fit", "t0_fit", "w_fit", "sv_fit")
res <- data.frame(matrix(ncol = length(cnames), nrow = nids*ninit_vals*nalgos))
colnames(res) <- cnames
# label the result dataframe
res[["ID"]] <- rep(ids, each = ni) # label individuals
res[["Algorithm"]] <- rep(algo_names, each = ninit_vals) # label algorithms
res[["vu_init"]] <- init_vals[["vu"]] # label initial vu
res[["vl_init"]] <- init_vals[["vl"]] # label initial vl
res[["a_init"]] <- init_vals[["a"]] # label initial a
res[["w_init"]] <- init_vals[["w"]] # label initial w
res[["sv_init"]] <- init_vals[["sv"]] # label initial sv
# Loop through each individual and starting values
for (i in 1:nids) {
# extract data for id i
dfi <- df[df[["id"]] == ids[i], ]
rti <- dfi[["rt"]]
respi <- dfi[["response"]]
truthi <- dfi[["truth"]]
# starting value for t0 must be smaller than the smallest rt
min_rti <- min(rti)
t0_lo <- 0.01*min_rti
t0_me <- 0.50*min_rti
t0_hi <- 0.99*min_rti
init_vals[["t0"]] <- c(rep(t0_me, 5), t0_lo, t0_hi, rep(t0_me, 4))
# label the result dataframe
res[["t0_init"]][((i-1)*ni+1):(i*ni)] <- init_vals[["t0"]] # label initial t0
# loop through all of the starting values
for (j in 1:ninit_vals) {
temp <- nlminb(init_vals[j, ], ll_fb_SWSE_17,
rt = rti, resp = respi, truth = truthi, err_tol = err_tol,
# limits: vu, vl, a, t0, w, sv
lower = c(-Inf, -Inf, .01, 0, 0, 0),
upper = c( Inf, Inf, Inf, min_rti, 1, Inf))
res[["Convergence"]][(i-1)*ni+0*ninit_vals+j] <- temp[["convergence"]]
res[["Objective"]][(i-1)*ni+0*ninit_vals+j] <- temp[["objective"]]
res[(i-1)*ni+0*ninit_vals+j, 11:16] <- temp[["par"]]
temp <- nlminb(init_vals[j, ], ll_fb_Gon_17,
rt = rti, resp = respi, truth = truthi, err_tol = err_tol,
# limits: vu, vl, a, t0, w, sv
lower = c(-Inf, -Inf, .01, 0, 0, 0),
upper = c( Inf, Inf, Inf, min_rti, 1, Inf))
res[["Convergence"]][(i-1)*ni+1*ninit_vals+j] <- temp[["convergence"]]
res[["Objective"]][(i-1)*ni+1*ninit_vals+j] <- temp[["objective"]]
res[(i-1)*ni+1*ninit_vals+j, 11:16] <- temp[["par"]]
temp <- nlminb(init_vals[j, ], ll_fb_Nav_17,
rt = rti, resp = respi, truth = truthi, err_tol = err_tol,
# limits: vu, vl, a, t0, w, sv
lower = c(-Inf, -Inf, .01, 0, 0, 0),
upper = c( Inf, Inf, Inf, min_rti, 1, Inf))
res[["Convergence"]][(i-1)*ni+2*ninit_vals+j] <- temp[["convergence"]]
res[["Objective"]][(i-1)*ni+2*ninit_vals+j] <- temp[["objective"]]
res[(i-1)*ni+2*ninit_vals+j, 11:16] <- temp[["par"]]
temp <- nlminb(init_vals[j, ], ll_RTDists,
rt = rti, resp = respi, truth = truthi,
# limits: vu, vl, a, t0, w, sv
lower = c(-Inf, -Inf, .01, 0, 0, 0),
upper = c( Inf, Inf, Inf, min_rti, 1, Inf))
res[["Convergence"]][(i-1)*ni+3*ninit_vals+j] <- temp[["convergence"]]
res[["Objective"]][(i-1)*ni+3*ninit_vals+j] <- temp[["objective"]]
res[(i-1)*ni+3*ninit_vals+j, 11:16] <- temp[["par"]]
}
}
return(res)
}
## ----fitting-run, eval=FALSE----------------------------------------------------------------------
# data(med_dec, package = "fddm")
# med_dec <- med_dec[which(med_dec[["rt"]] >= 0), ]
# fit <- rt_fit(med_dec, id_idx = c(2,1), rt_idx = 8, response_idx = 7,
# truth_idx = 5, response_upper = "blast", err_tol = 1e-6)
## ----fitting-run-internal, eval=FALSE, include=FALSE----------------------------------------------
# save(fit, compress = "xz", compression_level = 9,
# file = "inst/extdata/valid_fit.Rds")
## ----fitting-prep, eval=TRUE----------------------------------------------------------------------
fit_prep <- function(fit) {
nr <- nrow(fit)
fit[["Obj_diff"]] <- rep(0, nr)
fit[["vu_diff"]] <- rep(0, nr)
fit[["vl_diff"]] <- rep(0, nr)
fit[["a_diff"]] <- rep(0, nr)
fit[["t0_diff"]] <- rep(0, nr)
fit[["w_diff"]] <- rep(0, nr)
fit[["sv_diff"]] <- rep(0, nr)
ids <- unique(fit[["ID"]])
nids <- length(ids)
algos <- unique(fit[["Algorithm"]])
nalgos <- length(algos)
fit_idx <- c(4, 11:16)
dif_idx <- 17:23
ninit <- nrow(fit[fit[["ID"]] == ids[1] & fit[["Algorithm"]] == algos[1], ])
for (i in 1:nids) {
for (j in 1:ninit) {
actual_idx <- seq((i-1)*ninit*nalgos+j, i*ninit*nalgos, by = ninit)
min_obj_idx <- actual_idx[which.min(fit[actual_idx, 4])]
best_fit <- fit[min_obj_idx, fit_idx]
for (k in 0:(nalgos-1)) {
fit[(i-1)*(ninit*nalgos) + k*ninit + j, dif_idx] <-
fit[(i-1)*(ninit*nalgos) + k*ninit + j, fit_idx] - best_fit
}
}
}
return(fit)
}
## ----fit-load, eval=TRUE--------------------------------------------------------------------------
# load data, will be in the variable 'fit'
load(system.file("extdata", "dfddm_density", "valid_fit.Rds", package = "fddm", mustWork = TRUE))
fit <- fit_prep(fit)
cat("Results for ID = experienced 2")
fit[(0:3)*11+1, ]
## ----fit-estimates, eval=TRUE---------------------------------------------------------------------
# Define error tolerance
eps <- 1e-4
out <- fit[unique(which(abs(fit[, c(3, 17:23)]) > eps, arr.ind = TRUE)[, 1]), ]
out[, -c(1:2)] <- zapsmall(out[, -c(1:2)])
out
## ----session-info, collapse=TRUE------------------------------------------------------------------
sessionInfo()
## ----reset-options, include=FALSE---------------------------------------------
options(op) # reset options
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