Plotting model parameter outputs after subsampling

After using the subsampling methods we mostly obtain the estimated model parameter estimates. Here, they are summarised as histogram plots.

Usage

plot_Beta(object)

Arguments

object

Any object after subsampling from our subsampling functions

Value

The output is a faceted ggplot result

Details

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.