pp_check error with exgaussian()

[rosskempner]

Hello, I am fitting an exgaussian() model and experiencing an issue when it comes to using pp_check() on the brmsfit.

formula_exgaussian1 = bf(formula = reaction_time | trunc(ub = 550) ~ 
                        1 + 
                        all_random_mode +
                        bonus_for_speed + 
                        all_random_mode * bonus_for_speed +
                        block_num + 
                        block_num * all_random_mode + 
                        block_num * bonus_for_speed + 
                        block_num * bonus_for_speed * all_random_mode +
                        bigram_ideal_first_surprisal  +  
                        bigram_ideal_first_surprisal * all_random_mode + 
                        bigram_ideal_first_surprisal * bonus_for_speed + 
                        bigram_ideal_first_surprisal * bonus_for_speed * all_random_mode +
                        finger_transition +
                        (1 + block_num + bigram_ideal_first_surprisal | participant_id)
                      )

get_prior(formula = formula_exgaussian1, data = experiment_df, family = exgaussian())
priors_exgaussian1 = c(
    set_prior("normal(400,0.3)", class = "Intercept"),
    set_prior("normal(0.5,0.2)", class = "sigma"),
    set_prior("normal(-0.01,0.01)", class = "b", coef = "block_num"),
    set_prior("normal(1,0.4)", class = "b", coef = "bigram_ideal_first_surprisal"),
    set_prior("normal(0.1,0.1)", class = "sd", coef = "bigram_ideal_first_surprisal", group = "participant_id"),
    set_prior("normal(0,0.01)", class = "sd", coef = "block_num", group = "participant_id"),
    set_prior("normal(0.4,1)", class = "sd", coef = "Intercept", group = "participant_id"),
    set_prior("normal(0,0.2)", class = "b", coef = "all_random_mode"),
    set_prior("normal(0,0.2)", class = "b", coef = "bonus_for_speed"),
    set_prior("normal(0,0.1)", class = "b", coef = "all_random_mode:bonus_for_speed"),
    set_prior("normal(0,0.0001)", class = "b", coef = "all_random_mode:block_num"),
    set_prior("normal(0,0.0001)", class = "b", coef = "bonus_for_speed:block_num"),
    set_prior("normal(0,0.0001)", class = "b", coef = "all_random_mode:bonus_for_speed:block_num"),
    set_prior("normal(0,0.0001)", class = "b", coef = "all_random_mode:bigram_ideal_first_surprisal"),
    set_prior("normal(0,0.001)", class = "b", coef = "bonus_for_speed:bigram_ideal_first_surprisal"),
    set_prior("normal(0,0.001)", class = "b", coef = "all_random_mode:bonus_for_speed:bigram_ideal_first_surprisal"),
    set_prior("normal(0,0.1)", class = "b", coef = "finger_transitiondj"),
    set_prior("normal(0,0.1)", class = "b", coef = "finger_transitiondk"),
    set_prior("normal(0,0.1)", class = "b", coef = "finger_transitionfd"),
    set_prior("normal(0,0.1)", class = "b", coef = "finger_transitionfj"),
    set_prior("normal(0,0.1)", class = "b", coef = "finger_transitionfk"),
    set_prior("normal(0,0.1)", class = "b", coef = "finger_transitionjd"),
    set_prior("normal(0,0.1)", class = "b", coef = "finger_transitionjf"),
    set_prior("normal(0,0.1)", class = "b", coef = "finger_transitionjk"),
    set_prior("normal(0,0.1)", class = "b", coef = "finger_transitionkd"),
    set_prior("normal(0,0.1)", class = "b", coef = "finger_transitionkf"),
    set_prior("normal(0,0.1)", class = "b", coef = "finger_transitionkj"),
    set_prior("lkj(2)", class = "cor")
)        
fit_exgaussian1 <- brm(formula = formula_exgaussian1,
                        data = experiment_df,
                        family = exgaussian(),
                        prior = priors_exgaussian1,
  sample_prior = "only")

pp_check(fit_exgaussian1) The fitting process works, plotting works and summary on the brms fit object works but an error comes at pp_check, Below is the error message :

Error in qexgaussian(.x1, mu = .x2, sigma = .x3, beta = .x4) : could not find function "qexgaussian"
18.
eval(expr, envir, ...)
17.
eval(expr, envir, ...)
16.
eval2(call, envir = args, enclos = parent.frame())
15.
do_call(qdist, c(list(out), args))
14.
rcontinuous(n = prep$nsamples, dist = "exgaussian", mu = get_dpar(prep, "mu", i = i), sigma = get_dpar(prep, "sigma", i = i), beta = get_dpar(prep, "beta", i = i), lb = prep$data$lb[i], ub = prep$data$ub[i])
13.
FUN(X[[i]], ...)
12.
lapply(X, FUN, ...)
11.
plapply(seq_len(N), pp_fun, cores = cores, prep = object, ...)
10.
posterior_predict.brmsprep(prep, transform = transform, sort = sort, ntrys = ntrys, negative_rt = negative_rt, cores = cores, summary = FALSE)
9.
posterior_predict(prep, transform = transform, sort = sort, ntrys = ntrys, negative_rt = negative_rt, cores = cores, summary = FALSE)
8.
posterior_predict.brmsfit(.x1, newdata = .x2, resp = .x3, subset = .x4)
7.
posterior_predict(.x1, newdata = .x2, resp = .x3, subset = .x4)
6.
eval(expr, envir, ...)
5.
eval(expr, envir, ...)
4.
eval2(call, envir = args, enclos = parent.frame())
3.
do_call(method, pred_args)
2.
pp_check.brmsfit(fit_exgaussian1)
1.
pp_check(fit_exgaussian1)

Operating System: MAC OS Catalina brms Version: 2.14.0++

[jgabry]

Hi @rosskempner, thanks for sharing the full error message. Based on that it looks like this error is actually happening in brms before it gets to bayesplot, so I recommend opening this issue over at https://github.com/paul-buerkner/brms/issues.

[rosskempner]

Will do thanks!