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

Linear Regression Assumptions

Stephanie Glen has a chart which explains the four key assumptions behind when Ordinary Least Squares is the Best Linear Unbiased Estimator: If any of the main assumptions of linear regression are violated, any results or forecasts that you glean from your data will be extremely biased, inefficient or misleading. Navigating all of the different assumptions […]

Read More

Visualizing with Heatmaps in R

Anisa Dhana shows how you can create a quick heatmap plot in R: To give your own colors use the scale_fill_gradientn function.ggplot(dat, aes(Age, Race)) + geom_raster(aes(fill = BMI)) + scale_fill_gradientn(colours=c("white", "red")) This is a quick example using ggplot2 but there are other heatmap libraries available too.

Read More

Categories

January 2019
MTWTFSS
« Dec Feb »
 123456
78910111213
14151617181920
21222324252627
28293031