Additional Specifications for the Group-level Random Effects of the DMPM
Source:R/random.R
random_spec.Rd
This function can be used as part of dynamiteformula()
to define
whether the group-level random effects should be modeled as correlated or
not.
Details
With a large number of time points random intercepts can become challenging sample with default priors. This is because with large group sizes the group-level intercepts tend to be behave similarly to fixed group-factor variable so the model becomes overparameterized given these and the common intercept term. Another potential cause for sampling problems is relatively large variation in the intercepts (large sigma_nu) compared to the sampling variation (sigma) in the Gaussian case.
See also
Model formula construction
dynamite()
,
dynamiteformula()
,
lags()
,
lfactor()
,
splines()
Examples
data.table::setDTthreads(1) # For CRAN
# two channel model with correlated random effects for responses x and y
obs(y ~ 1 + random(~1), family = "gaussian") +
obs(x ~ 1 + random(~1 + z), family = "poisson") +
random_spec(correlated = TRUE)
#> Family Formula
#> y gaussian y ~ 1 + random(~1)
#> x poisson x ~ 1 + random(~1 + z)
#>
#> Correlated random effects added for response(s): y, x