Hello,

I fit a model with the custom beta binomial distribution from vignette(“brms_customfamilies”). I’m trying to get the fitted values from this model using a new data frame with argument “newdata.” However, I’m encountering an error whenever the “newdata” argument is supplied to either the fitted or predict functions. Both functions work fine predicting from the original data frame with no new data supplied. I’ve encountered the same error using the example from vignette(“brms_customfamilies”) so here is an example:

```
require(brms)
#> Loading required package: brms
#> Loading required package: Rcpp
#> Loading required package: ggplot2
#> Loading 'brms' package (version 2.6.0). Useful instructions
#> can be found by typing help('brms'). A more detailed introduction
#> to the package is available through vignette('brms_overview').
#> Run theme_set(theme_default()) to use the default bayesplot theme.
data("cbpp", package = "lme4")
beta_binomial2 <- custom_family(
"beta_binomial2", dpars = c("mu", "phi"),
links = c("logit", "log"), lb = c(NA, 0),
type = "int", vars = "trials[n]"
)
stan_funs <- "
real beta_binomial2_lpmf(int y, real mu, real phi, int T) {
return beta_binomial_lpmf(y | T, mu * phi, (1 - mu) * phi);
}
int beta_binomial2_rng(real mu, real phi, int T) {
return beta_binomial_rng(T, mu * phi, (1 - mu) * phi);
}
"
stanvars <- stanvar(scode = stan_funs, block = "functions") +
stanvar(as.integer(cbpp$size), name = "trials")
fit2 <- brm(
incidence ~ period + (1|herd), data = cbpp,
family = beta_binomial2, stanvars = stanvars
)
#> Compiling the C++ model
#> Start sampling
#>
#> SAMPLING FOR MODEL '5b724f4e4312a2f504f358414aeb3092' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.000142 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.42 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 2.73569 seconds (Warm-up)
#> Chain 1: 2.01796 seconds (Sampling)
#> Chain 1: 4.75364 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL '5b724f4e4312a2f504f358414aeb3092' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.000112 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1.12 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 2.65138 seconds (Warm-up)
#> Chain 2: 2.42912 seconds (Sampling)
#> Chain 2: 5.0805 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL '5b724f4e4312a2f504f358414aeb3092' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 0.000136 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 1.36 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 2.54499 seconds (Warm-up)
#> Chain 3: 2.31139 seconds (Sampling)
#> Chain 3: 4.85638 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL '5b724f4e4312a2f504f358414aeb3092' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 0.000105 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 1.05 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 2.6117 seconds (Warm-up)
#> Chain 4: 2.1002 seconds (Sampling)
#> Chain 4: 4.7119 seconds (Total)
#> Chain 4:
expose_functions(fit2, vectorize = TRUE)
log_lik_beta_binomial2 <- function(i, draws) {
mu <- draws$dpars$mu[, i]
phi <- draws$dpars$phi
N <- draws$data$trials[i]
y <- draws$data$Y[i]
beta_binomial2_lpmf(y, mu, phi, N)
}
predict_beta_binomial2 <- function(i, draws, ...) {
mu <- draws$dpars$mu[, i]
phi <- draws$dpars$phi
N <- draws$data$trials[i]
beta_binomial2_rng(mu, phi, N)
}
fitted_beta_binomial2 <- function(draws) {
mu <- draws$dpars$mu
trials <- draws$data$trials
trials <- matrix(trials, nrow = nrow(mu), ncol = ncol(mu), byrow = TRUE)
mu * trials
}
#Create newdata for predict or fitted
newdat<-expand.grid(incidence=unique(cbpp$incidence), size=unique(cbpp$size), period=unique(cbpp$period))
#errors
fit2prednew<-predict(fit2, newdata=newdat, re_formula = NA)
#> Error in (function (mu, phi, T, base_rng__ = <pointer: 0x7fef19b78cc0>, : Exception: beta_binomial_rng: Population size parameter is -2147483648, but must be >= 0! (in 'unknown file name' at line 8)
fit2fitnew<-fitted(fit2, newdata=newdat, re_formula = NA)
#> Warning in matrix(trials, nrow = nrow(mu), ncol = ncol(mu), byrow = TRUE):
#> data length [56] is not a sub-multiple or multiple of the number of rows
#> [4000]
#Works fine
fit2pred<-predict(fit2, re_formula = NA)
fit2fit<-fitted(fit2, re_formula = NA)
```

^{Created on 2018-12-17 by the reprex package (v0.2.1)}

I would appreciate any help. R and all of my packages are up to date.

- Operating System: macOS Mojave
- brms Version: 2.6.0

Thank you very much,

Sam