Type:	Package
Version:	0.7-2
Date:	2025-05-30
Title:	Dirichlet Regression
Description:	Implements Dirichlet regression models.
Encoding:	UTF-8
Depends:	R (≥ 3.0.0), Formula
Imports:	stats, graphics, methods, maxLik
Suggests:	rgl, knitr, rmarkdown, formatR, testthat
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/maiermarco/DirichletReg https://CRAN.R-project.org/package=DirichletReg
ByteCompile:	yes
LazyLoad:	yes
LazyData:	yes
ZipData:	yes
VignetteBuilder:	knitr
NeedsCompilation:	yes
Packaged:	2025-05-30 17:11:28 UTC; mm
Author:	Marco Johannes Maier [cre, aut]
Maintainer:	Marco Johannes Maier <marco_maier@posteo.de>
Repository:	CRAN
Date/Publication:	2025-05-31 00:20:02 UTC

Arctic Lake Data (Aitchison)

Description

These data are taken from Aitchison (2003) and contain information on the relation of sediment composition with depth in an Arctic lake.

Usage

ArcticLake

Format

A data frame with 39 observations on the following 4 variables:

sand, silt, clay

relative frequencies of sand, silt, and clay

depth

water depth in meters

Source

Aitchison, J. (2003). The Statistical Analysis of Compositional Data. The Blackburn Press, Caldwell, NJ.

Examples

head(ArcticLake)
AL <- DR_data(ArcticLake[,1:3])
plot(AL)
summary(AL)

Serum Protein Composition in Blood Samples

Description

These data (Aitchison, 2003) list blood samples' compositions of Albumin, Pre-Albumin, Globulin A, and Globulin B in relation to two types of diseases. 14 patients suffer from disease A, 16 from disease B and 6 are unclassified.

Usage

BloodSamples

Format

A data frame with 36 observations on the following 6 variables.

Albumin, Pre.Albumin, Globulin.A, Globulin.B

the amounts of Albumin, Pre-Albumin, Globulin A, and Globulin B.

Disease

diagnosis of disease A, B, or NA for unclassified observations.

New

a factor indicating whether the observations are old and classified (No) or new and unclassified (Yes).

Source

Aitchison, J. (2003). The Statistical Analysis of Compositional Data. The Blackburn Press, Caldwell, NJ.

Examples

head(BloodSamples)
Bl <- DR_data(BloodSamples[,1:4])
summary(Bl)

Fitting a Dirichlet Regression

Description

This function allows for fitting Dirichlet regression models using two different parametrizations.

Usage

DirichReg(formula, data, model = c("common", "alternative"),
          subset, sub.comp, base, weights, control, verbosity = 0)

Arguments

Details

Formula Specification and Models

formula determines the used predictors. The responses must be prepared by DR_data and can be optionally stored in the object containing all covariates which is then specified as the argument data. (Although “on-the-fly” processing of DR_data in a formula works, it is only intended for testing purposes and may be removed at any time – use at your own risk.)

There are two different parametrization (controlled by the argument model, see below):

• the “common” param. that models each \alpha by an (possibly individual) set of predictors, and

• the “alternative” param. that models expected values (\mu; as in multinomial logistic regression) and precision parameters (\phi) with two sets of predictors.

As the two models offer different modeling strategies, the specification of their formulae differ:

Formulae for the “Common” Model

The simplest possible model here is to include only an intercept for all components. If DV is the ‘dependent variable’ (i.e., compositional data) with three components, we can request this null-model by DV ~ 1. We always have at least two dependent variables, so simple formulae as the one given above will be expanded to DV ~ 1 | 1 | 1, because DV hast three components. Likewise, it is possible to specify a common set of predictors for all components, as in DV ~ p1 * p2, where p1 and p2 are predictors.

If the covariates of the components shall differ, one has to set up a complete formula for each subcomposition, using | as separators between the components, for example, DV ~ p1 | p1 + p2 | p1 * p2 will lead to a model where the first response in DV will be modeled using p1, the second will be predicted by p1 + p2 and the third by p1 * p2. Note that if you use the latter approach, the predictors have to be stated explicitly for all response variables.

Formulae for the “Alternative” Model

The simplest possible model here is to include an intercept for all components (except the base) and an intercept for precision. This can be achieved by DV ~ 1, which is expanded to DV ~ 1 | 1. The part modeling the ‘mean’ (first element on the right-hand side) is mandatory, if no specification for precision is included, an intercept will be added. Note that you need to set model = "alternative" to use this parametrization!

The alternative parametrization consists of two parts: modeled expected values (\mu) and their ‘precision’ (\phi). As in multinomial logistic regression, one response variable is omitted (by default the first, but this can be changed by the base argument in DR_data or DirichReg) and for the rest a set of predictors is used with a multinomial logit-link. For precisions, a different set of predictors can be set up using a log-link.

DV ~ p1 * p2 | p1 + p2 will set up a model where the expected values are predicted by p1 * p2 and precision are modeled using p1 + p2.

Data Preparation

The data argument accepts a data.frame that must include the dependent variable as a named element (see examples how to do this).

Changing the Base Component and Analyzing Subcompositions

The base-component (i.e., omitted component) is initially set during the stage of data preparation DR_data, but can easily be changed using the argument base which takes integer values from 1 to the maximum number of components.

If a data set contains a large number of components, of which only a few are relevant, the latter can be ‘sorted out’ and the irrelevant (i.e., not selected) components will be aggregated into a single variable (row sums) that automatically becomes the base category for the model, unless specified otherwise by base. The positioning of variables will necessarily change: the aggregated variable takes the first column and the others are appended in their order of selection.

Subsets and Weights

Using subset, the model can be fitted only to a part of the data, for more information about this functionality, see subset.
Note that, unlike in glm, weights are not treated as prior weights, but as frequency weights!

Optimization and Verbosity

Using the control argument, the settings passed to the optimizers can be altered. This argument takes a named list. To supply user-defined starting values, use control = list(sv=c(...)) and supply a vector containing initial values for all parameters. Optimizer-specific options include the number of iterations (iterlim = 1000) and convergence criteria for the BFGS- and NR-optimization ((tol1 = 1e-5) and (tol2 = 1e-10)).

Verbosity takes integer values from 0 to 4. 0, no information is printed (default). 1 prints information about 3 stages (preparation, starting values, estimation). 2 prints little information about optimization (verbosity values greater than one are passed to print.default = verbosity - 1 of maxBFGS and maxNR). 3 prints more information about optimization. 4 prints all information about optimization.

Value

Author(s)

Marco J. Maier

Examples

ALake <- ArcticLake
ALake$Y <- DR_data(ALake[,1:3])

# fit a quadratic Dirichlet regression models ("common")
res1 <- DirichReg(Y ~ depth + I(depth^2), ALake)

# fit a Dirichlet regression with quadratic predictor for the mean and
# a linear predictor for precision ("alternative")
res2 <- DirichReg(Y ~ depth + I(depth^2) | depth, ALake, model="alternative")

# test both models
anova(res1, res2)

res1
summary(res2)

The Dirichlet Distribution

Description

Density function and random number generation for the Dirichlet distribution

Usage

rdirichlet(n, alpha)

ddirichlet(x, alpha, log = FALSE, sum.up = FALSE)

ddirichlet_R(x, alpha, log = FALSE, sum.up = FALSE)

Arguments

Details

The Dirichlet distribution is a multidimensional generalization of the Beta distribution where each dimension is governed by an \alpha-parameter. Formally this is

% \mathcal{D}(\alpha_i)=\left[\left.\Gamma(\sum_{i}\alpha_i)\right/\prod_i\Gamma(\alpha_i)\right]\prod_{i}y_i^{\alpha_i-1}%

Usually, alpha is a vector thus the same parameters will be used for all observations. If alpha is a matrix, a complete set of \alpha-parameters must be supplied for each observation.

log returns the logarithm of the densities (therefore the log-likelihood) and sum.up returns the product or sum and thereby the likelihood or log-likelihood.

Dirichlet (log-)densities are by default computed using C-routines (ddirichlet_log_vector and ddirichlet_log_matrix), a version only using R is provided by ddirichlet_R. Caution: Although .C() can be used to call the C routines directly, R will crash or produce wrong values, if, e.g., data types are not set properly.

Value

Author(s)

Marco J. Maier

Examples

X1 <- rdirichlet(100, c(5, 5, 10))

a.mat <- cbind(1:10, 5, 10:1)
a.mat
X2 <- rdirichlet(10, a.mat)
# note how the probabilities in the first an last column relate to a.mat
round(X2, 2)

ddirichlet(X1, c(5, 5, 10))
ddirichlet(X2, a.mat)

ddirichlet(X2[1:3,], c(1, 2, -1))
ddirichlet(X2[1:3,], c(1, 2, -1), sum.up = TRUE)

The DirichletReg Package

Description

This package provides a functions to analyze compositional data using Dirichlet regression methods.

Author(s)

Marco J. Maier

Examples

  example(plot.DirichletRegData)
  example(DirichReg)

Prepare Compositional Data

Description

This function prepares a matrix with compositional variables for further processing in the DirichletReg package.

Usage

DR_data(Y, trafo = sqrt(.Machine$double.eps), base = 1,
    norm_tol = sqrt(.Machine$double.eps))

## S3 method for class 'DirichletRegData'
print(x, type = c("processed", "original"), ...)

## S3 method for class 'DirichletRegData'
summary(object, ...)

Arguments

Details

Y

Y is a matrix or data.frame containing compositional variables. If they do not sum up to 1 for all observations, normalization is forced where each row entry is divided by the row's sum (a warning will be issued that normalization was applied).
In case one row-entry (or more) is NA, the whole row will be returned as NA. Beta-distributed variables can be supplied as a single vector which, however, has to have values in the interval [0,\,1]. The second variable will be generated (1 - Y) and a matrix consisting of the columns 1 - Y and Y will be returned. A message will be issued that a beta-distributed variable was assumed and that this assumtion needs to be checked.

trafo

The transformation (done if trafo = TRUE) is a generalization of that proposed by Smithson and Verkuilen (2006) that transforms each component y of Y by computing y^{*}=\frac{y(n-1)+\frac{1}{2}}{n} where n is the number of observations in Y (this approach is also used in the package betareg, see Cribari-Neto & Zeileis, 2010).
For an arbitrary number of dimensions (or variables) d the transformation is y^{*}=\frac{y(n-1)+\frac{1}{d}}{n}.

base

To set the base (i.e., omitted) component of Y for the “alternative” (mean/precision) model, the argument base can be used. This is by default set to the first variable in Y (if a vector is be supplied, the column 1 - Y becomes the base component).
Note that the definition can be overruled in DirichReg.

x and object

Objects created by DR_data.

type

specifies for the print method whether the original or processed data are displayed.

Value

The function returns a matrix object of class DirichletRegData with the following attributes:

Author(s)

Marco J. Maier

References

Smithson, M. & Verkuilen, J. (2006). A Better Lemon Squeezer? Maximum-Likelihood Regression With Beta-Distributed Dependent Variables. Psychological Methods, 11(1), 54–71.

Cribari-Neto, F. & Zeileis, A. (2010). Beta Regression in R. Journal of Statistical Software, 34(2), 1–24.

Examples

# create a DirichletRegData object from the Arctic Lake data
head(ArcticLake[, 1:3])
AL <- DR_data(ArcticLake[, 1:3])
summary(AL)
head(AL)

Methods for the Class DirichletRegModel

Description

These are available methods for the results of Dirichlet regression models and objects of class DirichletRegModel. These methods contain functions for print and summary of the data, generate fitted values and predicting new values using predict. Various types of residuals are implemented and confint can be used to compute confidence intervals of the parameters. Furthermore logLik extracts the log-likelihood of the model and vcov extracts the covariance matrix of the parameter estimates.

Usage

## S3 method for class 'DirichletRegModel'
print(x, digits = max(3, getOption("digits") - 3), ...)

## S3 method for class 'DirichletRegModel'
summary(object, ...)

## S3 method for class 'DirichletRegModel'
fitted(object, mu = TRUE, alpha = FALSE, phi = FALSE, ...)

## S3 method for class 'DirichletRegModel'
predict(object, newdata, mu = TRUE, alpha = FALSE, phi = FALSE, ...)

## S3 method for class 'DirichletRegModel'
residuals(object, type = c("standardized", "composite", "raw"), ...)

## S3 method for class 'DirichletRegModel'
confint(object, parm, level, ..., type=c("all", "beta", "gamma"), exp = FALSE)

## S3 method for class 'DirichletRegConfint'
print(x, digits = 3, ...)

## S3 method for class 'DirichletRegModel'
logLik(object, ...)

## S3 method for class 'DirichletRegModel'
AIC(object, ..., k = 2)

## S3 method for class 'DirichletRegModel'
BIC(object, ...)

## S3 method for class 'DirichletRegModel'
nobs(object, ...)

## S3 method for class 'DirichletRegModel'
vcov(object, ...)

## S3 method for class 'DirichletRegModel'
update(object, formula., ..., evaluate = TRUE)

## S3 method for class 'DirichletRegModel'
drop1(object, scope, test = c("LRT", "none"), k = 2, sort = TRUE, ...)

Arguments

Author(s)

Marco J. Maier

Examples

ALake <- ArcticLake
ALake$AL <- DR_data(ArcticLake[, 1:3])

mod1 <- DirichReg(AL ~ depth + I(depth^2) | depth, data = ALake, model="alternative")

update(mod1, . ~ . | . + I(depth^2), evaluate = FALSE)
mod1

drop1(mod1)   ### issues a caveat when used for the first time in an R session

summary(mod1)

head(fitted(mod1))

predict(mod1, newdata = data.frame("depth" = seq(10, 100, 10)))

head(residuals(mod1))

confint(mod1)
confint(mod1, exp = TRUE)

logLik(mod1)
round(vcov(mod1), 5)

Glacial Tills

Description

Data from Aitchison (2003)

Usage

GlacialTills

Format

A data frame with 92 observations on the following 5 variables.

Red.Sandstone

a numeric vector

Gray.Sandstone

a numeric vector

Crystalline

a numeric vector

Miscellaneous

a numeric vector

Pcount

a numeric vector

Source

Aitchison, J. (2003). The Statistical Analysis of Compositional Data. The Blackburn Press, Caldwell, NJ.

Pammer and Kevan's Data on Reading Skills

Description

These data provide transformed reading accuracy scores predicted by IQ and diagnosed dyslexia.

Usage

ReadingSkills

Format

A data frame containing 44 observations on 3 variables.

accuracy

reading accuracy score transformed to fit into (0,\,1)

dyslexia

a factor with the diagnosis of dyslexia (“yes” or “no”)

iq

non-verbal IQ (z-scores; \mu=0, \sigma^2=1)

Source

Example 3 from http://www.michaelsmithson.online/stats/betareg/betareg.html

Aitchison's Rock Data

Description

A compilation of four datasets listed in Aitchison (2003)

Each type of rock has 25 observations – to use only a certain type of rock, see “Details”.

Usage

Rocks

Format

A data frame with 100 observations on the following 8 variables.

Albite, Blandite, Cornite, Daubite, Endite

numeric vectors

depth

a numeric vector

porosity

a numeric vector

type

a factor with levels Boxite Coxite Hongite Kongite

Source

Aitchison, J. (2003). The Statistical Analysis of Compositional Data. The Blackburn Press, Caldwell, NJ.

Transform Compositional Data for a Simplex

Description

These functions transform a matrix with three or four components to fit into a two- or three-dimensional simplex (triangle or tetrahedron).

Usage

toSimplex(x)

toTernary(abc)
toTernaryVectors(c1, c2, c3)

toQuaternary(abcd)
toQuaternaryVectors(c1, c2, c3, c4)

Arguments

Details

Most of these functions are only used internally, but sometimes it might be useful to plot “custom” ternary or quaternary graphics.

Note that, apart from toSimplex(), functions do not have any checks, so it is advisable to use this function if elements are added to plots or own graphics are created.

Value

The function returns a matrix object with coordinates in two or three dimensions

Note

In prior versions (up to 0.5-0), an unexported function coord.trafo() was used internally and could also be accessed via DirichletReg:::coord.trafo().

If you have used this in your code, you will get a message that the function is now deprecated and will become defunct in the future. Use toSimplex() instead.

Author(s)

Marco J. Maier

Examples

# create a DirichletRegData object from the Arctic Lake data
"to be added"

Compare Dirichlet Regression Models using an LRT

Description

This function allows for pairwise tests of Dirichlet regression models using a likelihood ratio test (LRT).

Usage

## S3 method for class 'DirichletRegModel'
anova(object, ..., sorted = FALSE)

Arguments

Details

The test statistic is computed LR=-2\left[\log(L_a)-\log(L_b)\right] where L_i is the likelihood of model i with df equal to the difference of the number of parameters in the models.

Author(s)

Marco J. Maier

Examples

ALake <- ArcticLake
ALake$AL <- DR_data(ArcticLake[,1:3])
mod0 <- DirichReg(AL ~ 1, ALake)
mod1 <- DirichReg(AL ~ depth, ALake)
mod2 <- DirichReg(AL ~ depth + I(depth^2), ALake)
anova(mod1, mod0, mod2, sorted = TRUE)

Plot Dirichlet-Distributed Data

Description

With this function you can plot Dirichlet-distributed data in 2, 3 and 4 dimensions.

Usage

## S3 method for class 'DirichletRegData'
plot(x, dims, ticks = TRUE, ref.lines = NULL, dim.labels, a2d = list(colored =
  TRUE, c.grid = TRUE, col.scheme = c("dims", "entropy"), entropy.contours =
  FALSE, entropy.colors = FALSE), a3d = list(rgl = TRUE, ...), rug = TRUE,
  reset_par = TRUE, ...)

Arguments

Author(s)

Marco J. Maier

Examples

# plot of "Sand" in the Arctic Lake data set
plot(DR_data(ReadingSkills[, 1]), main="Reading Accuracy")

# ternary plot of Arctic Lake data
plot(DR_data(ArcticLake[, 1:3]), a2d = list(colored = FALSE))