**Software for Exploratory Data Analysis and Statistical Modelling » R Environment**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

In mixture experiments there is a constraint that the variables are the proportions of components that are mixed together with the consequence that these proportions sum to one. When fitting regression models to data from mixture experiments we may be interested in reprenting the fitted model with a surface plot.

The constraint on proportions means that the mixture data can be described in one dimension lower than the total number of components. For example when there are three mixture components a two dimension plot can be used to represent the mixture within an equilateral triangle.

To create a surface within the mixture triangle we can create a grid of points and then convert these pairs of points into mixture triples.

mygrid = rbind( expand.grid( x = seq(0, 1.0, length.out = 500), y = seq(0, sqrt(3)/2, length.out = 500) ) ) |

The conversion formulae are shown below:

mygrid$a = (sqrt(3) * (mygrid$x - 0.5) + (mygrid$y - 0.5 * sqrt(3))) / (- sqrt(3) - 0.5 * sqrt(3)) mygrid$b = (- sqrt(3) * (mygrid$x - 0.5) + (mygrid$y - 0.5 * sqrt(3))) / (- sqrt(3) - 0.5 * sqrt(3)) mygrid$c = 1 - mygrid$a - mygrid$b |

The next step is to calculate our surface values, a trivial example of which is:

mygrid$z = 10 + 4 * mygrid$a + 3 * mygrid$b |

We then need to *trick* the plotting function by setting all invalid mixture combinations to be missing values.

mygrid$z[mygrid$a < 0 | mygrid$b < 0 | mygrid$c < 0] = NA mygrid$z[mygrid$a > 1 | mygrid$b > 1 | mygrid$c > 1] = NA |

Lastly we use the levelplot function in the lattice package.

trellis.par.set("axis.line",list(col=NA,lty=1,lwd=1)) levelplot(z ~ x*y, data = mygrid, col.regions = gray(101:0/101), scales = list(draw=FALSE), xlab = "", ylab = "", panel = function(x, y, z, ...) { panel.levelplot(x, y, z, ...) panel.lines(c(0,1), c(0,0), col = "black") panel.lines(c(0,0.5), c(0,sqrt(3)/2), col = "black") panel.lines(c(0.5,1), c(sqrt(3)/2,0), col = "black") panel.text(0.5, sqrt(3)/2, "C", pos=3) panel.text(0, 0, "A", pos=2) panel.text(1, 0, "B", pos=4) }, xlim = c(-0.2,1.2), ylim = c(-0.2, 0.2+sqrt(3)/2) ) |

This forms the basis of a ternary surface plot and various adjustments can be easily made to customise the plot.

Other useful resources are provided on the Supplementary Material page.

**leave a comment**for the author, please follow the link and comment on their blog:

**Software for Exploratory Data Analysis and Statistical Modelling » R Environment**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.