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Data Smoothing

Author: Mitch Richling
Updated: 2022-06-04 16:17:47

Copyright 2020-2021 Mitch Richling. All rights reserved.

Table of Contents

1. Metadata

The home for this HTML file is: https://richmit.github.io/ex-R/smoothing.html

Files related to this document may be found on github: https://github.com/richmit/ex-R

Directory contents:

src - The org-mode file that generated this HTML document
docs - This html document
data - Data files
tangled - Tangled R code from this document

2. Example Data

nPts <- 150
xDat <- 1:nPts
yDat <- rnorm(nPts, mean=0, sd=10)+xDat*.2

3. Various Smoothers

3.1. Compute various smoothed data sets

Some methods produce objects with new X data while others just produce new Y data. 

smDatRunMed     <- runmed(yDat, 15)                      # Running Median
smDatRunAvg2    <- stats::filter(yDat,                   # Two sided, running average
                                 rep(1/15,15), 
                                 method='convolution', 
                                 sides=2)
smDatRunAvg1    <- stats::filter(yDat,                   # One sided, running average 
                                 rep(1/7,7), 
                                 method='convolution', 
                                 sides=1)
smDatTuk3RS3R   <- smooth(yDat, kind = "3RS3R")          # Tukey Smoother
smDatLowess0d10 <- lowess(xDat, yDat, f=.1)              # Cleveland lowess smoother
smDatSupSmu     <- supsmu(xDat, yDat, span=.1)           # Friedman's SuperSmoother

3.2. Put it all in a data.frame for ggplot

allDat <- rbind(data.frame(smoother=rep('actual',   nPts), x=xDat,           y=yDat),
                data.frame(smoother=rep('runMed',   nPts), x=xDat,           y=smDatRunMed),
                data.frame(smoother=rep('runAvg2',  nPts), x=xDat,           y=smDatRunAvg2),
                data.frame(smoother=rep('runAvg1',  nPts), x=xDat,           y=smDatRunAvg1),
                data.frame(smoother=rep('tuk3RS3R', nPts), x=xDat,           y=as.vector(smDatTuk3RS3R)),
                data.frame(smoother=rep('lowess',   nPts), x=smDatLowess0d10$x, y=smDatLowess0d10$y),
                data.frame(smoother=rep('supsmu',   nPts), x=smDatSupSmu$x,     y=smDatSupSmu$y)
                )

3.3. Plot them all

ggplot(allDat, aes(x=x, y=y, col=smoother)) +
  geom_line(data=subset(allDat,  smoother!='actual')) +
  geom_point(data=subset(allDat, smoother=='actual'))

dif.png

4. Lowess f values

4.1. Compute three lowess smoothings with different f values

smDatLowess0d05   <- lowess(xDat, yDat, f=.05)
smDatLowess0d20   <- lowess(xDat, yDat, f=.20)
smDatLowess0d50   <- lowess(xDat, yDat, f=.50)

4.2. Put all of our lowess curves into a data.frame with the original data so we can plot it all with ggplot

allDat <- rbind(data.frame(smoother=rep('actual',       nPts), x=xDat,           y=yDat),
                data.frame(smoother=rep('lowess0d05',   nPts), x=smDatLowess0d05$x, y=smDatLowess0d05$y),
                data.frame(smoother=rep('lowess0d10',   nPts), x=smDatLowess0d10$x, y=smDatLowess0d10$y),
                data.frame(smoother=rep('lowess0d20',   nPts), x=smDatLowess0d20$x, y=smDatLowess0d20$y),
                data.frame(smoother=rep('lowess0d50',   nPts), x=smDatLowess0d50$x, y=smDatLowess0d50$y)
                )

4.3. Plot them all

ggplot(allDat, aes(x=x, y=y, col=smoother)) +
  geom_line(data=subset(allDat,  smoother!='actual')) +
  geom_point(data=subset(allDat, smoother=='actual')) +
  labs(title='Lowess At Various f Values')

low.png