In mcomas/coda.base: A Basic Set of Functions for Compositional Data Analysis

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In this vignette we show how to define log-ratio coordinates using coda.base package and its function coordinates with parameters X, a composition, and basis, defining the independent log-contrasts for building the coordinates.

In this vignette we work with a subcomposition of the results obtained in different regions of Catalonia in 2017's parliament elections:

library(coda.base)
data('parliament2017')
X = parliament2017[,c('erc','jxcat','psc','cs')]

Log-ratio coordinates with `coda.base`

The additive logratio (alr) coordinates

The alr coordinates are accessible by setting the parameter basis='alr' or by using the building function alr_basis().

If you don't want the last part in the denominator, the easiest way to define an alr-coordinates is to set basis='alr':

H1.alr = coordinates(X, basis = 'alr')
head(H1.alr)

It defines an alr-coordinates were the last part is used in the denominator. We can obtain the basis used to build the coordinates with function basis():

basis(H1.alr)

The basis can be reproduced using the function alr_basis:

alr_basis(dim = 4)

In fact, function alr_basis allows to define any type of alr-like coordinate by defining the numerator and the denominator:

B.alr = alr_basis(dim = 4, numerator = c(4,2,3), denominator = 1)
B.alr

The log-contrast matrix can be used as basis parameter in coordinates() function:

H2.alr = coordinates(X, basis = B.alr)
basis(H2.alr)

The centered logratio (clr) coordinates

Building centered log-ratio coordinates can be accomplished by setting parameter basis='clr' or

H.clr = coordinates(X, basis = 'clr')
head(H.clr)

The isometric logratio (ilr) coordinates

coda.base allows to define a wide variety of ilr-coordinates: principal components (pc) coordinates, specific user balances coordinates, principal balances (pb) coordinates, balanced coordinates (default's CoDaPack's coordinates).

The default ilr coordinate used by coda.base are accessible by simply calling function coordinates without parameters:

H1.ilr = coordinates(X)
head(H1.ilr)

Parameter basis is set to ilr by default:

all.equal( coordinates(X, basis = 'ilr'),
           H1.ilr )

Other ilr-coordinates: Principal Components and Principal balances

Other easily accessible coordinates are the Principal Component (PC) coordinates. PC coordinates define the first coordinate as the log-contrast with highest variance, the second the one independent from the first and with highest variance and so on:

H2.ilr = coordinates(X, basis = 'pc')
head(H2.ilr)
barplot(apply(H2.ilr, 2, var))

Note that the PC coordinates are independent:

cov(H2.ilr)

The Principal Balance coordinates are similar to PC coordinates but with the restriction that the log contrast are balances

H3.ilr = coordinates(X, basis = 'pb')
head(H3.ilr)
barplot(apply(H3.ilr, 2, var))

Moreover, they are not independent:

cor(H3.ilr)

Principal Balances are hard to compute when the number of components is very high. coda.base allows to build PB approximations using different algorithms.

X100 = exp(matrix(rnorm(1000*100), ncol = 100))

Hierarchical clustering based algorithm.

PB1.ward = pb_basis(X100, method = 'cluster')

Constrained search algorithm

PB1.constrained = pb_basis(X100, method = 'constrained')

We can compare they performance (variance explained by the first balance) with respect to the principal components.

PC_approx = coordinates(X100, cbind(pc_basis(X100)[,1], PB1.ward[,1], PB1.constrained[,1]))
names(PC_approx) = c('PC', 'Ward', 'Constrained')
apply(PC_approx, 2, var)

Finally, coda.base allows to define the default CoDaPack basis which consists in defining well balanced balances, i.e. equal number of branches in each balance.

H4.ilr = coordinates(X, basis = 'cdp')
head(H4.ilr)

Defining coordinates manually

Defining coordinates with an specific basis

We can define the coordinates directly by providing the log-contrast matrix.

B = matrix(c(-1,-1,2,0,
             1,0,-0.5,-0.5,
             -0.5,0.5,0,0), ncol = 3)
H1.man = coordinates(X, basis = B)
head(H1.man)

Defining coordinates using balances

We can also define balances using formula numerator~denominator:

B.man = sbp_basis(list(b1 = erc~jxcat,
                       b2 = psc~cs,
                       b3 = erc+jxcat~psc+cs), 
                  data=X)
H2.man = coordinates(X, basis = B.man)
head(H2.man)

With sbp_basis we do not need to define neither a basis nor a system generator

B = sbp_basis(list(b1 = erc+jxcat~psc+cs), 
              data=X)
H3.man = coordinates(X, basis = B)
head(H3.man)

B = sbp_basis(list(b1 = erc~jxcat+psc~cs, 
                   b2 = jxcat~erc+psc+cs,
                   b3 = psc~erc+jxcat+cs,
                   b4 = cs~erc+jxcat+psc),
              data=X)
H4.man = coordinates(X, basis = B)
head(H4.man)

If interested, we can complete a sequential binary partition giving only some partitions

B = sbp_basis(list(b1 = erc+jxcat~psc), 
              data=X, fill = TRUE)
sign(B)

We can also define sequential binary partition using a matrix. By using a matrix we don't need to include a dataset. The number of components is obtained with the number of rows and component names from row names (if available).

P =  matrix(c(1, 1,-1,-1,
              1,-1, 0, 0,
              0, 0, 1,-1), ncol= 3)
B = sbp_basis(P)
H5.man = coordinates(X, basis = B)
head(H5.man)