rowDist {ChemoSpecUtils} | R Documentation |

This function computes the distance between rows of a matrix using a number of methods.
It is primarily a wrapper for `Dist`

which provides many options.
However, cosine distance is calculated locally.
See the reference for an excellent summary of distances and similarities.
Keep in mind that distances are always positive by definition. Further, in the literature one
can find the same distance defined different ways. For instance, the definition of the
`"pearson"`

and `"correlation"`

distances differs slightly between the reference below
and `Dist`

. So please study the definitions carefully to get the one you want.
The example illustrates the behavior of some common distance definitions. Notice that `"pearson"`

and `"cosine"`

are mathemtically identical for the particular definition of `"pearson"`

used by `Dist`

.

rowDist(x, method)

`x` |
A matrix whose rows will be used for the distance calculation. |

`method` |
Character; one of |

An object of class `dist`

.

Bryan A. Hanson, DePauw University.

R. Todeschini, D. Ballabio, V. Consonni
"Distances and Similarity Measures in Chemometrics and Chemoinformatics"
in *Encyclopedia of Analytical Chemistry* Wiley and Sons, 2020
doi: 10.1002/9780470027318.a9438.pub2

# This examples imagines spectra as a series of vectors # on a half unit circle. # 1. Compute half of a unit circle theta <- seq(0, pi, length = 100) x = cos(theta) y = sin(theta) # 2. Compute some illustrative vectors # Get tail/origin & tip/head coordinates lt <- length(theta) set.seed(6) tips <- theta[c(1, sample(2:100, 5))] x0 <- y0 <- rep(0.0, lt) # tail/origin at 0,0 x1 <- cos(tips) # tips/heads y1 <- sin(tips) # 3. Compute the distance functions # Bounded distances RDcor <- rep(NA_real_, lt) # correlation distance RDpea <- rep(NA_real_, lt) # pearson distance RDabp <- rep(NA_real_, lt) # abspearson distance RDcos <- rep(NA_real_, lt) # cosine distance # Unbounded distances RDeuc <- rep(NA_real_, lt) # Euclidean distance RDman <- rep(NA_real_, lt) # manhattan distance # Compute all np <- 5 refVec <- c(seq(0.0, x[1], length.out = np), seq(0.0, y[1], length.out = np)) for (i in 1:lt) { Vec <- c(seq(0.0, x[i], length.out = np), seq(0.0, y[i], length.out = np)) M <- matrix(c(refVec, Vec), nrow = 2, byrow = TRUE) RDman[i] <- rowDist(M, method = "manhattan") RDeuc[i] <- rowDist(M, method = "euclidean") RDcos[i] <- rowDist(M, method = "cosine") RDcor[i] <- rowDist(M, method = "correlation") RDpea[i] <- rowDist(M, method = "pearson") RDabp[i] <- rowDist(M, method = "abspearson") } # 4. Plots # a. Unit circle w/representative vectors/spectra plot.new() plot.window(xlim = c(-1, 1), ylim = c(0, 1), asp = 1) title(main = "Representative 'Spectral' Vectors on a Unit Half Circle\nReference Vector in Red", sub = "Each 'spectrum' is represented by a series of x, y points") lines(x, y, col = "gray") # draw half circle lines(x = x[c(1,100)], y = y[c(1,100)], col = "gray") # line across bottom arrows(x0, y0, x1, y1, angle = 5) # add arrows & a red reference vector arrows(x0[1], y0[1], x1[1], y1[1], col = "red", angle = 5, lwd = 2) # b. Distances degrees <- theta*180/pi plot(degrees, RDman, type = "l", xlab = "Angle Between Spectral Vectors and Reference Vector in Degrees", ylab = "Distance", main = "Spectral Distance Comparisons\nUsing ChemoSpecUtils::rowDist") abline(h = c(1.0, 2.0), col = "gray") lines(degrees, RDeuc, col = "blue") lines(degrees, RDcos, col = "green", lwd = 4) lines(degrees, RDcor, col = "red") lines(degrees, RDabp, col = "black", lty = 2) lines(degrees, RDpea, col = "black", lty = 3) leg.txt <- c("manhattan", "euclidean", "correlation", "cosine", "pearson", "abspearson") leg.col <- c("black", "blue", "red", "green", "black", "black") leg.lwd <- c(1, 1, 1, 4, 1, 1) leg.lty <- c(1, 1, 1, 1, 3, 2) legend("topleft", legend = leg.txt, col = leg.col, lwd = leg.lwd, lty = leg.lty)

[Package *ChemoSpecUtils* version 0.4.96 Index]