I’m on R version 6.0 and using version brms 2.9.0
I have a handful of questions, but I’ll start simple. I’m trying to create a psychometric function to extract threshold values. Reading “Fitting the Psychometric Function” (Bernhard Treutwein): https://link.springer.com/article/10.3758/BF03211951, my understanding of psychometric functions is that fit is enhanced by specifying priors of lapse, guess, threshold, and spread parameters.
I’m trying to fit psychometric functions for an adaptive paradigm of eight tasks, with two conditions (except for one task which has four conditions). The adaptivity component (and independent variable) is response window (increases with incorrect answer, decreases with correct answer). There are roughly 1000 participants (3rd, 5th, and 7th graders). First and foremost, I want to be able to extract a threshold response window for each participant at probability = .8.
I found this link to a posting on this site, which has been helpful: https://discourse.mc-stan.org/c/interfaces/brms.
I’m new to brms but have tried to familiarize myself with the terms, looking through the Cran.r page: https://cran.r-project.org/web/packages/brms/brms.pdf.
I can’t seem to implement threshold or spread parameters in this model. I get the following error:
Error: The parameter 'threshold' is not a valid distributional or non-linear parameter. Did you forget to set 'nl = TRUE'?
So that’s my first issue. My second issue is that when I include more than one participant I get this error for the four chains:
Stan model '547eb5715049d22db6f829f32c7c94cd' does not contain samples.
Here’s my model. I’m not sure what I’m doing wrong. I’ve included a dput of my data below. I’ve kept it to only one condition of one task for simplicity on this site (the dataset is a flanker task and comprises only 11 participants), but I would ideally like to calculate this 80% threshold for all conditions of every task. Is this better done in separate models, or all in the same go?
BF <- bf(
response~guess + (1-guess-lapse) * Phi(eta),
eta ~ 1 + pid/rw, #what is eta?,
#threshold~1,
#spread ~1,
guess ~ 1,
lapse ~ 1,
family = bernoulli(link="identity"),
nl = TRUE
)
priors <- c(
prior(beta(7, 3),class = "b", nlpar = "eta"),
#prior(beta(7, 3),class = "b", nlpar = "threshold"),
#prior(beta(1.4,1.4), nlpar = "spread"),
prior(beta(.5, 8), nlpar = "lapse", lb = 0, ub = .1),
prior(beta(1, 5), nlpar = "guess", lb = 0, ub = .1)
)
fit <- brm(
BF,
data = flanker.psych,
inits = 'random', #what is this for?
control = list(adapt_delta = 0.99), #what is this for?
prior = priors,
iter = 1000,
chains = 4,
cores = 4
)
Thanks!
Dataset:
structure(list(pid = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L), .Label = c("ADMIN-UCSF-bo001",
"ADMIN-UCSF-bo002", "ADMIN-UCSF-bo004", "ADMIN-UCSF-bo005", "ADMIN-UCSF-bo008",
"ADMIN-UCSF-bo009", "ADMIN-UCSF-bo010", "ADMIN-UCSF-bo011",
"ADMIN-UCSF-se104", "ADMIN-UCSF-se105"), class = "factor"), module = structure(c(5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L
), .Label = c("BACKWARDSSPATIALSPAN", "BOXED", "BRT", "FILTER",
"FLANKER", "ISHIHARA", "SAAT", "SPATIALSPAN", "STROOP", "TASKSWITCH",
"TNT"), class = "factor"), condition = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
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1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("CONGRUENT",
"Conjunction_12", "Conjunction_4", "Feature_12", "Feature_4",
"impulsive", "INCONGRUENT", "Left", "R2B0", "R2B2", "R2B4", "R4B0",
"R4B2", "Right", "Start", "Stay", "sustained", "Switch", "Tap & Trace",
"Tap Only"), class = "factor"), correct_button = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L,
3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
), .Label = c("correct", "incorrect", "no_response"), class = "factor"),
rw = c(710L, 800L, 820L, 690L, 710L, 760L, 700L, 840L, 680L,
780L, 770L, 640L, 720L, 780L, 840L, 830L, 790L, 830L, 760L,
700L, 670L, 800L, 740L, 670L, 800L, 600L, 840L, 760L, 920L,
910L, 940L, 830L, 900L, 840L, 870L, 320L, 920L, 950L, 820L,
440L, 600L, 910L, 790L, 930L, 800L, 880L, 910L, 360L, 800L,
280L, 1120L, 1190L, 1030L, 1110L, 1320L, 1250L, 1150L, 1180L,
950L, 1030L, 1190L, 1170L, 1180L, 870L, 910L, 1170L, 1240L,
960L, 800L, 840L, 880L, 1110L, 1150L, 1210L, 1280L, 950L,
890L, 860L, 800L, 870L, 1070L, 820L, 1030L, 1110L, 880L,
790L, 810L, 960L, 830L, 1090L, 1100L, 950L, 850L, 780L, 790L,
800L, 990L, 880L, 1030L, 840L, 900L, 880L, 1460L, 1480L,
910L, 960L, 920L, 940L, 920L, 1440L, 950L, 1470L, 1460L,
880L, 880L, 1470L, 1480L, 880L, 840L, 840L, 1000L, 1440L,
1160L, 920L, 800L, 1150L, 840L, 2260L, 2250L, 1130L, 1140L,
1070L, 1110L, 990L, 2270L, 1150L, 2190L, 2230L, 2110L, 800L,
830L, 870L, 910L, 990L, 1030L, 1310L, 1070L, 1630L, 1950L,
1990L, 840L, 810L, 850L, 1070L, 830L, 800L, 990L, 980L, 1000L,
1060L, 1000L, 960L, 1050L, 990L, 1210L, 770L, 810L, 840L,
880L, 920L, 1030L, 1090L, 1200L, 1130L, 1240L, 950L, 790L,
790L, 790L, 850L, 940L, 880L, 910L, 780L, 800L, 870L, 820L,
670L, 800L, 760L, 630L, 830L, 930L, 710L, 870L, 790L, 840L,
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800L, 880L, 480L, 1620L, 320L, 940L, 960L, 1630L, 920L, 950L,
960L, 800L, 800L, 1590L, 400L, 880L, 640L, 840L), rt = c(468.641996383667,
489.506006240845, 498.21001291275, 500.387012958527, 513.499021530151,
537.150025367737, 567.131042480469, 578.942000865936, 585.861027240753,
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