Dynamic Programming In R With RCppDynProg

John Mount has a new package available in R:

In the above we have an input (or independent variable) x and an observed outcome (or dependent variable) y_observed (portrayed as points). y_observed is the unobserved idea value y_ideal (portrayed by the dashed curve) plus independent noise. The modeling goal is to get close the y_ideal curve using the y_observed observations. Obviously this can be done with a smoothing spline, but let’s use RcppDynProg to find a piecewise linear fit.
To encode this as a dynamic programming problem we need to build a cost matrix that for every consecutive interval of x-values we have estimated the out-of sample quality of fit. This is supplied by the function RcppDynProg::lin_costs() (using the PRESS statistic), but lets take a quick look at the idea.

It’s an interesting package whose purpose is to turn an input data stream into a set of linear functions which approximate the stream. I’m not sure I’ll ever have a chance to use it, but it’s good to know that it’s there if I do ever need it.

Related Posts

Interactive ggplot Plots with plotly

Laura Ellis takes us through ggplotly: As someone very interested in storytelling, ggplot2 is easily my data visualization tool of choice. It is like the Swiss army knife for data visualization. One of my favorite features is the ability to pack a graph chock-full of dimensions. This ability is incredibly handy during the data exploration […]

Read More

Goodbye, gather and spread; Hello pivot_long and pivot_wide

John Mount covers a change in tidyr which mimics Mount and Nina Zumel’s pivot_to_rowrecs and unpivot_to_blocks functions in the cdata package: If you want to work in the above way we suggest giving our cdatapackage a try. We named the functions pivot_to_rowrecs and unpivot_to_blocks. The idea was: by emphasizing the record structure one might eventually internalize what the transforms […]

Read More


January 2019
« Dec Feb »