After using the subsampling methods we mostly obtain the estimated model parameter estimates. Here, they are summarised as histogram plots.
plot_Beta(object)The output is a faceted ggplot result
Any object after subsampling from our subsampling functions
For local case control sampling the facets are for sample sizes and beta values.
For leverage sampling the facets are for sample sizes and beta values.
For A- and L-optimality criteria subsampling under Generalised Linear Models the facets are for sample sizes and beta values.
For A-optimality criteria subsampling under Gaussian Linear Models the facets are for sample sizes and beta values.
For A-optimality criteria subsampling under Generalised Linear Models with response variable not inclusive the facets are for sample sizes and beta values.
For A- and L-optimality criteria subsampling under Generalised Linear Models where multiple models can describe the data the facets are for sample sizes and beta values.
For A- and L-optimality criteria and LmAMSE subsampling under Generalised Linear Models with potential model misspecification the facets are for sample sizes and beta values.