Introduction

The International Vocabulary of Metrology (VIM) defines a quantity as “a property of a phenomenon, body, or substance, where the property has a magnitude that can be expressed as a number and a reference”, where most typically the number is a quantity value, attributed to a measurand and experimentally obtained via some measurement procedure, and the reference is a measurement unit (BIPM et al. 2012).

Additionally, any quantity value must accommodate some indication about the quality of the measurement, a quantifiable attribute known as uncertainty. The Guide to the Expression of Uncertainty in Measurement (GUM) defines uncertainty as “a parameter, associated with the result of a measurement, that characterises the dispersion of the values that could reasonably be attributed to the measurand” (BIPM et al. 2008). Uncertainty can be mainly classified into standard uncertainty, which is the result of a direct measurement (e.g., electrical voltage measured with a voltmeter, or current measured with a amperemeter), and combined standard uncertainty, which is the result of an indirect measurement (i.e., the standard uncertainty when the result is derived from a number of other quantities by the means of some mathematical relationship; e.g., electrical power as a product of voltage and current). Therefore, provided a set of quantities with known uncertainties, the process of obtaining the uncertainty of a derived measurement is called propagation of uncertainty.

Traditionally, computational systems have treated these three components (quantity values, measurement units and uncertainty) separately. Data consisted of bare numbers, and mathematical operations applied to them solely. Units were just metadata, and uncertainty propagation was an unpleasant task requiring additional effort and complex operations. Nowadays though, many software libraries have formalised quantity calculus as method of including units within the scope of mathematical operations, thus preserving dimensional correctness and protecting us from computing nonsensical combinations of quantities. However, these libraries rarely integrate uncertainty handling and propagation (Flater 2018).

Within the R environment, the units package (Pebesma and Mailund 2017; Pebesma, Mailund, and Hiebert 2016) defines a class for associating unit metadata to numeric vectors, which enables transparent quantity derivation, simplification and conversion. This approach is a very comfortable way of managing units with the added advantage of eliminating an entire class of potential programming mistakes. Unfortunately, neither units nor any other package address the integration of uncertainties into quantity calculus.

This article presents errors (Ucar 2018), a package that defines a framework for associating uncertainty metadata to R vectors, matrices and arrays, thus providing transparent, lightweight and automated propagation of uncertainty. This implementation also enables ongoing developments for integrating units and uncertainty handling into a complete solution.

Propagation of uncertainty

There are two main methods for propagation of uncertainty: the Taylor series method (TSM) and the Monte Carlo method (MCM). The TSM, also called the delta method, is based on a Taylor expansion of the mathematical expression that produces the output variables. As for the MCM, it can deal with generalised input distributions and propagates the uncertainty by Monte Carlo simulation.

Taylor series method

The TSM is a flexible and simple method of propagation of uncertainty that offers a good degree of approximation in most cases. In the following, we will provide a brief description. A full derivation, discussion and examples can be found in Arras (1998).

Mathematically, an indirect measurement is obtained as a function of n direct or indirect measurements, Y = f(X₁, ..., X_n), where the distribution of X_n is unknown a priori. Usually, the sources of random variability are many, independent and probably unknown as well. Thus, the central limit theorem establishes that an addition of a sufficiently large number of random variables tends to a normal distribution. As a result, the first assumption states that X_n are normally distributed.

The second assumption presumes linearity, i.e., that f can be approximated by a first-order Taylor series expansion around μ_Xn (see Figure 1). Then, given a set of n input variables X and a set of m output variables Y, the first-order uncertainty propagation law establishes that where Σ is the covariance matrix and J is the Jacobian operator.

Figure 1: Illustration of linearity in an interval ± one standard deviation around the mean.

In practice, as recommended in the GUM (BIPM et al. 2008), this first-order approximation is good even if f is non-linear, provided that the non-linearity is negligible compared to the magnitude of the uncertainty, i.e., $\E[f(X)]\approx f(\E[X])$. Also, this weaker condition is distribution-free: no assumptions are needed on the probability density functions (PDF) of X_n, although they must be reasonably symmetric.

If we consider Equation (1) for pairwise computations, i.e., Y = f(X₁, X₂), we can write the propagation of the uncertainty σ_Y as follows:

The cross-covariances for the output Y and any other variable Z can be simplified as follows: where, notably, if Z = X_i, one of the covariances above results in σ_Xi². Finally, and for the sake of completeness, the correlation coefficient can be obtained as r_YZ = σ_YZ/(σ_Yσ_Z).

Reporting uncertainty

The GUM (BIPM et al. 2008) defines four ways of reporting standard uncertainty and combined standard uncertainty. For instance, if the reported quantity is assumed to be a mass m_S of nominal value 100 g:

1. m_S = 100.02147 g with (a combined standard uncertainty) u_c = 0.35 mg.
2. m_S = 100.02147(35) g, where the number in parentheses is the numerical value of (the combined standard uncertainty) u_c referred to the corresponding last digits of the quoted result.
3. m_S = 100.02147(0.00035) g, where the number in parentheses is the numerical value of (the combined standard uncertainty) u_c expressed in the unit of the quoted result.
4. m_S = (100.02147 ± 0.00035) g, where the number following the symbol ± is the numerical value of (the combined standard uncertainty) u_c and not a confidence interval.

Schemes (2, 3) and (4) would be referred to as parenthesis notation and plus-minus notation respectively throughout this document. Although (4) is a very extended notation, the GUM explicitly discourages its use to prevent confusion with confidence intervals.

Related work

Several R packages are devoted to or provide methods for propagation of uncertainties. The car (Fox and Weisberg 2016, 2011) and msm (C. Jackson 2016; C. H. Jackson 2011) packages provide the functions deltaMethod() and deltamethod() respectively. Both of them implement a first-order TSM with a similar syntax, requiring a formula, a vector of values and a covariance matrix, thus being able to deal with dependency across variables.

The metRology (Ellison 2017) and propagate (Spiess 2014) packages stand out as very comprehensive sets of tools specifically focused on this topic, including both TSM and MCM. The metRology package implements TSM using algebraic or numeric differentiation, with support for correlation. It also provides a function for assessing the statistical performance of GUM uncertainty (TSM) using attained coverage probability. The propagate package implements TSM up to second order. It provides a unified interface for both TSM and MCM through the propagate() function, which requires an expression and a data frame or matrix as input. Unfortunately, as in the case of car and msm, these packages are limited to work only with expressions, which does not solve the issue of requiring a separate workflow to deal with uncertainties.

The spup package (Sawicka and Heuvelink 2017) focuses on uncertainty analysis in spatial environmental modelling, where the spatial cross-correlation between variables becomes important. The uncertainty is described with probability distributions and propagated using MCM.

Finally, the distr package (Kohl 2017; Ruckdeschel et al. 2006) takes this idea one step further by providing an S4-based object-oriented implementation of probability distributions, with which one can operate arithmetically or apply mathematical functions. It implements all kinds of probability distributions and has more methods for computing the distribution of derived quantities. Also, distr is the base of a whole family of packages, such as distrEllipse, distrEx, distrMod, distrRmetrics, distrSim and distrTeach.

All these packages provide excellent tools for uncertainty analysis and propagation. However, none of them addresses the issue of an integrated workflow, as units does for unit metadata by assigning units directly to R vectors, matrices and arrays. As a result, units can be added to any existing R computation in a very straightforward way. On the other hand, existing tools for uncertainty propagation require building specific expressions or data structures, and then some more work to extract the results out and to report them properly, with an appropriate number of significant digits.

Automated uncertainty handling in R: the errors package

The errors package aims to provide easy and lightweight handling of measurement with errors, including uncertainty propagation using the first-order TSM presented in the previous section and a formally sound representation.

Package description and usage

Standard uncertainties, can be assigned to numeric vectors, matrices and arrays, and then all the mathematical and arithmetic operations are transparently applied to both the values and the associated uncertainties:

library(errors)

x <- 1:5 + rnorm(5, sd = 0.01)
y <- 1:5 + rnorm(5, sd = 0.02)
errors(x) <- 0.01
errors(y) <- 0.02
x; y

## Errors: 0.01 0.01 0.01 0.01 0.01
## [1] 1.009314 2.002629 3.007546 3.998674 5.003387

## Errors: 0.02 0.02 0.02 0.02 0.02
## [1] 0.9964423 2.0056511 3.0024418 3.9955924 5.0339563

(z <- x / y)

## Errors: 0.022672724 0.011135401 0.007457628 0.005599789 0.004420403
## [1] 1.0129181 0.9984931 1.0017000 1.0007712 0.9939274

The errors() method assigns or retrieves a vector of uncertainties, which is stored as an attribute of the class errors, along with a unique object identifier:

str(x)

##  Errors: num [1:5] 1.01(1) 2.00(1) 3.01(1) 4.00(1) 5.00(1)

Correlations (and thus covariances) between pairs of variables can be set and retrieved using the correl() and covar() methods. These correlations are stored in an internal hash table indexed by the unique object identifier assigned to each errors object. If an object is removed, its associated correlations are cleaned up automatically.

correl(x, x) # one, cannot be changed

## [1] 1 1 1 1 1

correl(x, y) # NULL, not defined yet

## NULL

correl(x, y) <- runif(length(x), -1, 1)
correl(x, y)

## [1] -0.23887367  0.70379942 -0.05583015 -0.35912856 -0.33108658

covar(x, y)

## [1] -4.777473e-05  1.407599e-04 -1.116603e-05 -7.182571e-05 -6.621732e-05

Internally, errors provides S3 methods for the generics belonging to the groups Math and Ops, which propagate the uncertainty and the covariance using Equations (2) and (3) respectively.

z # previous computation without correlations

## Errors: 0.022672724 0.011135401 0.007457628 0.005599789 0.004420403
## [1] 1.0129181 0.9984931 1.0017000 1.0007712 0.9939274

(z_correl <- x / y)

## Errors: 0.024729103 0.007356542 0.007622187 0.006353158 0.004973368
## [1] 1.0129181 0.9984931 1.0017000 1.0007712 0.9939274

Other many S3 methods are also provided, such as generics belonging to the Summary group, subsetting operators ([, [<-, [[, [[<-), concatenation (c()), differentiation (diff), row and column binding (rbind, cbind), coercion to list, data frame and matrix, and more. Such methods mutate the errors object, and thus return a new one with no correlations associated. There are also setters defined as an alternative to the assignment methods (set_*() instead of errors<-, correl<- and covar<-), primarily intended for their use in conjunction with the pipe operator (%>%) from the magrittr (Bache and Wickham 2014) package.

Additionally, other useful summaries are provided, namely, the mean, the weighted mean and the median. The uncertainty of any measurement of central tendency cannot be smaller than the uncertainty of the individual measurements. Therefore, the uncertainty assigned to the mean is computed as the maximum between the standard deviation of the mean and the mean of the individual uncertainties (weighted, in the case of the weighted mean). As for the median, its uncertainty is computed as $\sqrt{\pi/2}\approx1.253$ times the standard deviation of the mean, where this constant comes from the asymptotic variance formula (Hampel et al. 2011).

It is worth noting that both values and uncertainties are stored with all the digits. However, when a single measurement or a column of measurements in a data frame are printed, there are S3 methods for format() and print() defined to properly format the output and display a single significant digit for the uncertainty. This default representation can be overridden using the digits option, and it is globally controlled with the option errors.digits.

# the elementary charge
e <- set_errors(1.6021766208e-19, 0.0000000098e-19)
print(e, digits = 2)

## 1.6021766208(98)e-19

The parenthesis notation, in which the number in parentheses is the uncertainty referred to the corresponding last digits of the quantity value (scheme 2 from the GUM, widely used in physics due to its compactness), is used by default, but this can be overridden through the appropriate option in order to use the plus-minus notation instead.

options(errors.notation = "plus-minus")
print(e, digits = 2)

## (1.6021766208 ± 0.0000000098)e-19

options(errors.notation = "parenthesis")

Finally, errors also facilitates plotting of error bars. In the following, we first assign a 2% of uncertainty to all the numeric variables in the iris data set and then we plot it using base graphics and ggplot2 (Wickham and Chang 2016; Wickham 2009). The result is shown in Figure 2.

iris.e <- iris
iris.e[1:4] <- lapply(iris.e[1:4], function(x) set_errors(x, x*0.02))

head(iris.e)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1       5.1(1)     3.50(7)      1.40(3)    0.200(4)  setosa
## 2       4.9(1)     3.00(6)      1.40(3)    0.200(4)  setosa
## 3      4.70(9)     3.20(6)      1.30(3)    0.200(4)  setosa
## 4      4.60(9)     3.10(6)      1.50(3)    0.200(4)  setosa
## 5       5.0(1)     3.60(7)      1.40(3)    0.200(4)  setosa
## 6       5.4(1)     3.90(8)      1.70(3)    0.400(8)  setosa

plot(Sepal.Width ~ Sepal.Length, iris.e, col=Species)
legend(6.2, 4.4, unique(iris.e[["Species"]]),
       col=1:length(iris.e[["Species"]]), pch=1)

library(ggplot2)

ggplot(iris.e) + aes(Sepal.Length, Sepal.Width, color=Species) +
  geom_point() + geom_errors() + theme_bw() + theme(legend.position=c(0.6, 0.8))

Figure 2: Base plot with error bars (left) and ggplot2’s version (right).

In base graphics, the error bars are automatically plotted when an object of class errors is passed. Additionally, we provide the convenience functions errors_min(x) and errors_max(x) for obtaining the boundaries of the interval in ggplot2 and other plotting packages, instead of writing x - errors(x) and x + errors(x) respectively.

V   <- mean(set_errors(GUM.H.2$V))
I   <- mean(set_errors(GUM.H.2$I))
phi <- mean(set_errors(GUM.H.2$phi))

correl(V, I)   <- with(GUM.H.2, cor(V, I))
correl(V, phi) <- with(GUM.H.2, cor(V, phi))
correl(I, phi) <- with(GUM.H.2, cor(I, phi))

print(R <- (V / I) * cos(phi), digits = 2, notation = "plus-minus")

## 127.732 ± 0.071

print(X <- (V / I) * sin(phi), digits = 3, notation = "plus-minus")

## 219.847 ± 0.296

print(Z <- (V / I), digits = 3, notation = "plus-minus")

## 254.260 ± 0.236

correl(R, X); correl(R, Z); correl(X, Z)

## [1] -0.5884298

## [1] -0.4852592

## [1] 0.9925116

fit <- lm(bk ~ I(tk - 20), data = GUM.H.3)

y1 <- set_errors(coef(fit)[1], sqrt(vcov(fit)[1, 1]))
y2 <- set_errors(coef(fit)[2], sqrt(vcov(fit)[2, 2]))
covar(y1, y2) <- vcov(fit)[1, 2]

print(b.30 <- y1 + y2 * set_errors(30 - 20), digits = 2, notation = "plus-minus")

## -0.1494 ± 0.0041

Discussion

The errors package provides the means for defining numeric vectors, matrices and arrays with errors in R, as well as to operate with them in a transparent way. Propagation of uncertainty implements the commonly used first-order TSM formula from Equation (1). This method has been pre-computed and expanded for each operation in the S3 groups Math and Ops, instead of differentiating symbolic expressions on demand or using functions from other packages for this task. The advantages of this approach are twofold. On the one hand, it is faster, as it does not involve simulation nor symbolic computation, and very lightweight in terms of package dependencies.

Another advantage of errors is the built-in consistent and formally sound representation of measurements with errors, rounding the uncertainty to one significant digit by default and supporting two widely used notations: parenthesis (e.g., 5.00(1)) and plus-minus (e.g., 5.00 ± 0.01). These notations are applied for single numbers and data frames, as well as tbl_df data frames from the tibble (Wickham, Francois, and Müller 2017) package.

Full support is provided for both data.frame and tbl_df, as well as matrices and arrays. However, some operations on those data structures may drop uncertainties (i.e., object class and attributes). More specifically, there are six common data wrangling operations: row subsetting, row ordering, column transformation, row aggregation, column joining and (un)pivoting. Table 1 shows the correspondence between these operations and R base functions, as well as the compatibility with errors.

Overall, errors is fully compatible with data wrangling operations embed in R base, and this is because those functions are mainly based on the subsetting generics, for which errors provides the corresponding S3 methods. Nonetheless, special attention must be paid to aggregations, which store partial results in lists that are finally simplified. Such simplification is made with unlist, which drops all the input attributes, including custom classes. However, all these aggregation functions provide the argument simplify (sometimes SIMPLIFY), which if set to FALSE, prevents this destructive simplification, and lists are returned. Such lists can be simplified non-destructively by calling do.call(c, ...).

unlist <- function(x) if (is.list(x)) do.call(c, x) else x
iris.e.agg <- aggregate(. ~ Species, data = iris.e, mean, simplify=FALSE)
as.data.frame(lapply(iris.e.agg, unlist), col.names=colnames(iris.e.agg))

##      Species Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1     setosa       5.0(1)     3.43(7)      1.46(3)     0.25(1)
## 2 versicolor       5.9(1)     2.77(6)      4.26(9)     1.33(3)
## 3  virginica       6.6(1)     2.97(6)       5.6(1)     2.03(4)

Summary and future work

We have introduced errors, a lightweight R package for managing numeric data with associated standard uncertainties. The new class errors provides numeric operations with automated propagation of uncertainty through a first-order TSM, and a formally sound representation of measurements with errors. Using this package makes the process of computing indirect measurements easier and less error-prone.

Future work includes importing and exporting data with uncertainties and providing the user with an interface for plugging uncertainty propagation methods from other packages. Finally, errors enables ongoing developments for integrating units and uncertainty handling into a complete solution for quantity calculus. Having a unified workflow for managing measurements with units and errors would be an interesting addition to the R ecosystem with very few precedents in other programming languages.