The making of a shiny mauc: chapter 2

This continues last week's post The making of a shiny mauc, based on Greg McNulty's mauc blog. It utilizes the RStudio interface to R, Desktop version.

Recall, the goal is to make an online shiny app that will run Greg's code using his data, all supplied in his post. Today we will address what modifications are necessary to show his first plot (below) on a web page. In a subsequent post we will see how to display all Greg's plots. After that we will see how

The making of a shiny mauc

When an excess of loss (XOL) reinsurance pricing actuary has only indemnity to work with, how can s/he reflect allocated loss adjustment expense (ALAE) in final cost projections? Such is the situation addressed by Greg McNulty in his blog Modeling ALAE Using Copulas (MAUC). According to McNulty, the classical approach — loading the indemnity value of each claim with an average ALAE/indemnity ratio — rests on "two very strong implicit assumptions": 1) ALAE and indemnity are "scaled copies" of each other and 2) ALAE and indemnity are "100% correlated." When those assumptions are questionable McNulty suggests an alternative approach.