baggr_compare {baggr} | R Documentation |

Compare multiple baggr models by either providing multiple already existing models as (named) arguments or passing parameters necessary to run a baggr model.

baggr_compare( ..., what = "pooling", compare = "groups", transform = NULL, plot = FALSE )

`...` |
Either some (at least 1) objects of class |

`what` |
One of |

`compare` |
When plotting, choose between comparison of |

`transform` |
a function (e.g. exp(), log()) to apply to the values of group (and hyper, if hyper=TRUE) effects before plotting; when working with effects that are on log scale, exponent transform is used automatically, you can plot on log scale by setting transform = identity |

`plot` |
logical; calls plot.baggr_compare when running |

If you pass parameters to the function you must specify what kind of comparison you want, either "pooling" which will run fully/partially/un-pooled models and compare them or "prior" which will generate estimates without the data and compare them to the model with the full data. For more details see baggr, specifically the PPD argument.

an object of class `baggr_compare`

Witold Wiecek, Brice Green

plot.baggr_compare and print.baggr_compare for working with results of this function

# Most basic comparison between no, partial and full pooling # (This will run the models) # run model with just prior and then full data for comparison # with the same arguments that are passed to baggr prior_comparison <- baggr_compare(schools, model = 'rubin', iter = 500, #this is just for illustration -- don't set it this low normally! prior_hypermean = normal(0, 3), prior_hypersd = normal(0,2), prior_hypercor = lkj(2), what = "prior") # print the aggregated treatment effects prior_comparison # plot the comparison of the two distributions plot(prior_comparison) # Now compare different types of pooling for the same model pooling_comparison <- baggr_compare(schools, model = 'rubin', iter = 500, #this is just for illustration -- don't set it this low normally! prior_hypermean = normal(0, 3), prior_hypersd = normal(0,2), prior_hypercor = lkj(2), what = "pooling", # You can automatically plot: plot = TRUE) # Compare existing models: bg1 <- baggr(schools, pooling = "partial") bg2 <- baggr(schools, pooling = "full") baggr_compare("Partial pooling model" = bg1, "Full pooling" = bg2) #' ...or simply draw prior predictive dist (note ppd=T) bg1 <- baggr(schools, ppd=TRUE) bg2 <- baggr(schools, prior_hypermean = normal(0, 5), ppd=TRUE) baggr_compare("Prior A, p.p.d."=bg1, "Prior B p.p.d."=bg2, compare = "effects") # Compare posterior effects as a function of priors (note ppd=FALSE) bg1 <- baggr(schools, prior_hypersd = uniform(0, 20)) bg2 <- baggr(schools, prior_hypersd = normal(0, 5)) baggr_compare("Uniform prior on SD"=bg1, "Normal prior on SD"=bg2, compare = "effects", plot = TRUE) # You can also compare different subsets of input data bg1_small <- baggr(schools[1:6,], pooling = "partial") baggr_compare("8 schools model" = bg1, "First 6 schools" = bg1_small, plot = TRUE)

[Package *baggr* version 0.6.4 Index]