Spread MCMC chain draws to data.frame on a wide format

Useful to convert MCMC chain draws of particular parameters or output from the model object to a wide format for further data wrangling

Usage

spread_draws(mod, ..., transformed = FALSE)

Arguments

mod: An object of class "plm0", "plm", "gplm0" or "gplm".
...: Any number of character vectors containing valid names of parameters in the model or "rating_curve" and "rating_curve_mean". Also accepts "latent_parameters" and "hyperparameters".
transformed: A boolean value determining whether the output is to be represented on the transformed scale used for sampling in the MCMC chain or the original scale. Defaults to FALSE.

Value

A data frame with columns:

chain: The chain number.
iter: The iteration number.
param: The parameter name.
value: The parameter value.

References

Hrafnkelsson, B., Sigurdarson, H., Rögnvaldsson, S., Jansson, A. Ö., Vias, R. D., and Gardarsson, S. M. (2022). Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling, Environmetrics, 33(2):e2711. doi: https://doi.org/10.1002/env.2711

Examples

# \donttest{
 data(krokfors)
 set.seed(1)
 plm0.fit <- plm0(formula=Q~W,data=krokfors,num_cores=2)
#> Progress:
#> Initializing Metropolis MCMC algorithm...
#> Multiprocess sampling (4 chains in 2 jobs) ...
#> 
#> MCMC sampling completed!
#> 
#> Diagnostics:
#> Acceptance rate: 36.09%.
#> ✔ All chains have mixed well (Rhat < 1.1).
#> ✔ Effective sample sizes sufficient (eff_n_samples > 400).
 hyp_samples <- spread_draws(plm0.fit,'hyperparameters')
 head(hyp_samples)
#>   chain iter        c sigma_eps
#> 1     1    1 7.693275 0.2421924
#> 2     1    2 7.708797 0.2256351
#> 3     1    3 7.704167 0.1679654
#> 4     1    4 7.666575 0.1807933
#> 5     1    5 7.658464 0.1570101
#> 6     1    6 7.709366 0.1968574
 rating_curve_samples <- spread_draws(plm0.fit,'rating_curve','rating_curve_mean')
 head(rating_curve_samples)
#>   chain iter        h rating_curve rating_curve_mean
#> 1     1    1 7.673811 0.000000e+00      0.000000e+00
#> 2     1    2 7.673811 0.000000e+00      0.000000e+00
#> 3     1    3 7.673811 0.000000e+00      0.000000e+00
#> 4     1    4 7.673811 1.094841e-06      1.018866e-06
#> 5     1    5 7.673811 6.537614e-06      6.335393e-06
#> 6     1    6 7.673811 0.000000e+00      0.000000e+00
# }