Package 'DHARMa.helpers'

Check Bayesian models fitted with brms

Description

Check Bayesian models fitted with brms

Usage

dh_check_brms(
  model,
  resp = NULL,
  integer = FALSE,
  plot = TRUE,
  nsamples = 1000,
  ntrys = 5,
  ...
)

Arguments

model	A fitted model brms::brmsfit-class(). Categorical and ordinal models not supported by now.
resp	Optional name of response variable (for multivariate models).
integer	Logical (TRUE/FALSE), indicating if response is an integer, as in Poisson and binomial models
plot	Logical. Plot residual checks? Default is TRUE.
nsamples	Integer. Number of samples to draw from the posterior.
ntrys	Integer. Number of trys to use for truncated distributions. See brms::posterior_predict().
...	Further arguments for DHARMa::plotResiduals()

Value

An object of type DHARMa. See DHARMa::createDHARMa() for more details.

See Also

https://frodriguezsanchez.net/post/using-dharma-to-check-bayesian-models-fitted-with-brms/

Examples

#' # Example models taken from brms::brm()

# Poisson regression for the number of seizures in epileptic patients
fit1 <- brm(count ~ zAge + zBase * Trt + (1|patient),
           data = epilepsy, family = poisson())
simres <- dh_check_brms(fit1, integer = TRUE)
plot(simres, form = epilepsy$zAge)
testDispersion(simres)


# Probit regression using the binomial family
ntrials <- sample(1:10, 100, TRUE)
success <- rbinom(100, size = ntrials, prob = 0.4)
x <- rnorm(100)
data4 <- data.frame(ntrials, success, x)
fit4 <- brm(success | trials(ntrials) ~ x, data = data4,
            family = binomial("probit"))
summary(fit4)
simres <- dh_check_brms(fit4, integer = TRUE)
plot(simres, form = data4$x)


# Multivariate (multiresponse) model
data("BTdata", package = "MCMCglmm")
bf_tarsus <- bf(tarsus ~ sex + (1|p|fosternest) + (1|q|dam))
bf_back <- bf(back ~ hatchdate + (1|p|fosternest) + (1|q|dam))
fit <- brm(bf_tarsus + bf_back + set_rescor(TRUE),
            data = BTdata, chains = 2, cores = 2)
dh_check_brms(fit, resp = "tarsus")
brms::pp_check(fit, resp = "tarsus")
dh_check_brms(fit, resp = "back")
brms::pp_check(fit, resp = "back")

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