Extract point estimates of compositions from fitted model.
get_fitted(fitted_model, conf_int = 0.05)The fitted model returned as an rstan object from the call to zoid
Parameter controlling confidence intervals calculated, defaults to 0.05 for 95% intervals
A list containing the posterior summaries of estimated parameters, with
element mu (the predicted values in normal space). For predictions
in transformed space, or overdispersion, see get_pars()
# \donttest{
y <- matrix(c(3.77, 6.63, 2.60, 0.9, 1.44, 0.66, 2.10, 3.57, 1.33),
nrow = 3, byrow = TRUE
)
# fit a model with no covariates
fit <- fit_zoid(data_matrix = y)
#>
#> SAMPLING FOR MODEL 'dirichregmod' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 2.3e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#>
#> SAMPLING FOR MODEL 'dirichregmod' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 2.3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2:
#>
#> SAMPLING FOR MODEL 'dirichregmod' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 2.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.24 seconds.
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#> Chain 3: Elapsed Time: 0.095 seconds (Warm-up)
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#> Chain 3: 0.196 seconds (Total)
#> Chain 3:
p_hat <- get_fitted(fit)
# }