The quest for certainty is the biggest obstacle to becoming risk savvy. (Gerd Gigerenzer)

^{1}

A major challenge in mastering risk literacy is coping with inevitable uncertainty. Fortunately, uncertainty in the form of *risk* can be expressed in terms of probabilities and thus be measured and calculated or “reckoned” with (Gigerenzer, 2002). Nevertheless, probabilistic information is often difficult to understand, even for experts in risk management and statistics. A smart and effective way to communicate probabilities is by expressing them in terms of frequencies.

The problems addressed by **riskyr** and the scientific discussion surrounding them can be framed in terms of two representational formats: Basically, risk-related information can be expressed in terms of *frequencies* or in terms of *probabilities* (see the user guide for background information.)

**riskyr** reflects this basic division by distinguishing between the same two data types and hence provides objects that contain frequencies (specifically, a list called `freq`

) and objects that contain probabilities (a list called `prob`

). But before we explain their contents, it is important to realize that any such separation is an abstract and artificial one. It may make sense to distinguish frequencies from probabilities for conceptual and educational reasons, but both in theory and in reality both representations are intimately intertwined.^{2}

In the following, we first consider frequencies and probabilities by themselves, before showing how both are related. As a sneak preview, the following prism plot shows both frequencies (as nodes) and probabilities (as edges connecting the nodes) from two perspectives:

```
library("riskyr") # load the "riskyr" package
plot_prism(prev = .01, sens = .80, spec = NA, fart = .096, # 3 essential probabilities
N = 1000, # 1 frequency
area = "no", # same size for all boxes
p_lbl = "abb", # show abbreviated names of probabilities on edges
title_lbl = "Example")
```

For our purposes, frequencies simply are numbers that can be counted — either 0 or positive integers.^{3}

The following 11 frequencies are distinguished by **riskyr** and contained in `freq`

:

Nr. | Variable | Definition |
---|---|---|

1. | `N` |
The number of cases (or individuals) in the population. |

2. | `cond_true` |
The number of cases for which the condition is present (`TRUE` ). |

3. | `cond_false` |
The number of cases for which the condition is absent (`FALSE` ). |

4. | `dec_pos` |
The number of cases for which the decision is positive (`TRUE` ). |

5. | `dec_neg` |
The number of cases for which the decision is negative (`FALSE` ). |

6. | `dec_cor` |
The number of cases for which the decision is correct (correspondence between decision and condition). |

7. | `dec_err` |
The number of cases for which the decision is erroneous (lack of correspondence between decision and condition). |

8. | `hi` |
The number of hits or true positives: condition present (`TRUE` ) & decision positive (`TRUE` ). |

9. | `mi` |
The number of misses or false negatives: condition present (`TRUE` ) & decision negative (`FALSE` ). |

10. | `fa` |
The number of false alarms or false positives: condition absent (`FALSE` ) & decision positive (`TRUE` ). |

11. | `cr` |
The number of correct rejections or true negatives: condition absent (`FALSE` ) & decision negative (`FALSE` ). |

The frequencies contained in `freq`

can be viewed (or parsed) from two perspectives:

**Top-down:**From the entire population to different parts or subgroups:

Whereas`N`

specifies the population size, the other 10 frequencies denote the number of individuals or cases in some subset. For instance, the frequency`dec_pos`

denotes individuals for which the decision or diagnosis is positive. As this frequency is contained within the population, its numeric value must range from 0 to`N`

.**Bottom-up:**From the 4 essential subgroups to various combinations of them:

As the 4 frequencies`hi`

,`mi`

,`fa`

, and`cr`

are not further split into subgroups, we can think of them as atomic elements or four*essential*frequencies. All other frequencies in`freq`

are sums of various combinations of these four essential frequencies. This implies that the entire network of frequencies and probabilities (shown in the network diagram above) can be reconstructed from these four essential frequencies.

The following relationships hold among the 11 frequencies:

The population size

`N`

can be split into several subgroups by classifying individuals by 4 different criteria:- by condition (
`cd`

); - by decision (
`dc`

); - by accuracy (
`ac`

) (i.e., the correspondence of decisions to conditions); - by the combination of condition and decision (i.e., a joint category).

- by condition (

Depending on the criterion used, the following relationships hold:

\[ \begin{aligned} \texttt{N} &= \texttt{cond_true} + \texttt{cond_false} & \textrm{(a)}\\ &= \texttt{dec_pos} + \texttt{dec_neg} & \textrm{(b)}\\ &= \texttt{dec_cor} + \texttt{dec_err} & \textrm{(c)}\\ &= \texttt{hi} + \texttt{mi} + \texttt{fa} + \texttt{cr} & \textrm{(d)}\\ \end{aligned} \]

Similarly, each of the subsets resulting from using the splits by *condition* (`cd`

), by *decision* (`dc`

), or by *accuracy* (`ac`

), can also be expressed as a sum of two of the four essential frequencies. This results in three different ways of grouping the four essential frequencies:

- by condition (
`cd`

) (corresponding to the two columns of the confusion matrix):

\[ \begin{aligned} \texttt{N} \ &= \ \texttt{cond_true} & +\ \ \ \ \ &\texttt{cond_false} & \textrm{(a)} \\ \ &= \ (\texttt{hi} + \texttt{mi}) & +\ \ \ \ \ &(\texttt{fa} + \texttt{cr}) \\ \end{aligned} \]

- by decision (
`dc`

) (corresponding to the two rows of the confusion matrix):

\[ \begin{aligned} \texttt{N} \ &= \ \texttt{dec_pos} & +\ \ \ \ \ &\texttt{dec_neg} & \ \ \ \ \ \textrm{(b)} \\ \ &= \ (\texttt{hi} + \texttt{fa}) & +\ \ \ \ \ &(\texttt{mi} + \texttt{cr}) \\ \end{aligned} \]

- by accuracy (
`ac`

) (or the correspondence of decisions to conditions, corresponding to the two diagonals of the confusion matrix):

\[ \begin{aligned} \texttt{N} \ &= \ \texttt{dec_cor} & +\ \ \ \ \ &\texttt{dec_err} & \ \ \ \ \textrm{(c)} \\ \ &= \ (\texttt{hi} + \texttt{cr}) & +\ \ \ \ \ &(\texttt{mi} + \texttt{fa}) \\ \end{aligned} \]

It may be tempting to refer to instances of `dec_cor`

and `dec_err`

as “true decisions” and “false decisions”. However, these terms invite conceptual confusion, as “true decisions” actually include `cond_false`

cases (TN or `cr`

cases) and “false decisions” actually include `cond_true`

cases (FN or `mi`

cases).

The notions of *probability* is as elusive as ubiquitous (see Hájek, 2012, for a solid exposition of its different concepts and interpretations). For our present purposes, *probabilities* are simply numbers between 0 and 1. These numbers are defined to reflect particular quantities and can be expressed as percentages, as functions of and ratios between other numbers (frequencies or probabilities).

**riskyr** distinguishes between 13 probabilities (see `prob`

for current values):

Nr. | Variable | Name | Definition |
---|---|---|---|

1. | `prev` |
prevalence | The probability of the condition being `TRUE` . |

2. | `sens` |
sensitivity | The conditional probability of a positive decision provided that the condition is `TRUE` . |

3. | `mirt` |
miss rate | The conditional probability of a negative decision provided that the condition is `TRUE` . |

4. | `spec` |
specificity | The conditional probability of a negative decision provided that the condition is `FALSE` . |

5. | `fart` |
false alarm rate | The conditional probability of a positive decision provided that the condition is `FALSE` . |

6. | `ppod` |
proportion of positive decisions | The proportion (baseline probability or rate) of the decision being positive (but not necessarily `TRUE` ). |

7. | `PPV` |
positive predictive value | The conditional probability of the condition being `TRUE` provided that the decision is positive. |

8. | `FDR` |
false detection rate | The conditional probability of the condition being `FALSE` provided that the decision is positive. |

9. | `NPV` |
negative predictive value | The conditional probability of the condition being `FALSE` provided that the decision is negative. |

10. | `FOR` |
false omission rate | The conditional probability of the condition being `TRUE` provided that the decision is negative. |

11. | `acc` |
accuracy | The probability of a correct decision (i.e., correspondence of decisions to conditions). |

12. | `p_acc_hi` |
– | The conditional probability of the condition being `TRUE` provided that a decision or prediction is accurate. |

13. | `p_err_fa` |
– | The conditional probability of the condition being `FALSE` provided that a decision or prediction is inaccurate or erroneous. |

Note that the prism diagram (`plot_prism`

) shows a total of 18 probabilities: 3 perspectives (`by = "cd"`

, `by = "dc"`

, and `by = "ac"`

) and 6 edges denoting the (marginal and conditional) probabilities for each perspective. However, we currently do not explicitly identify all possible probabilities.^{4}

Note that a typical **riskyr** scenario contains several *marginal* or *non-conditional* probabilities:

- The prevalence
`prev`

(1.) only depends on features of the*condition*. - The proportion of positive decisions
`ppod`

(or bias) (6.) only depends on features of the*decision*. - The accuracy
`acc`

(11.) depends on`prev`

and`ppod`

, but unconditionally dissects a population into two groups (`dec_cor`

vs.`dec_err`

).

The other probabilities are *conditional* probabilities based on three perspectives:

- by
*condition*: conditional probabilities (2. to 5.) depend on the*condition*’s`prev`

and features of the*decision*.

- by
*decision*: conditional probabilities (7. to 10.) depend on the*decision*’s`ppod`

and features of the*condition*.

- by
*accuracy*: conditional probabilities based on*accuracy*`acc`

are currently computed, but – in the absence of a commonly accepted term — named`p_acc_hi`

and`p_err_fa`

(12. and 13.).

The following relationships hold among the conditional probabilities:

- The sensitivity
`sens`

and miss rate`mirt`

are complements:

\[
\texttt{sens} = 1 - \texttt{mirt}
\] - The specificity `spec`

and false alarm rate `fart`

are complements:

\[
\texttt{spec} = 1 - \texttt{fart}
\] - The positive predictive value `PPV`

and false detection rate `FDR`

are complements:

\[
\texttt{PPV} = 1 - \texttt{FDR}
\] - The negative predictive value `NPV`

and false omission rate `FOR`

are complements:

\[ \texttt{NPV} = 1 - \texttt{FOR} \]

It is possible to adapt Bayes’ formula to define `PPV`

and `NPV`

in terms of `prev`

, `sens`

, and `spec`

:

\[ \texttt{PPV} = \frac{\texttt{prev} \cdot \texttt{sens}}{\texttt{prev} \cdot \texttt{sens} + (1 - \texttt{prev}) \cdot (1 - \texttt{sens})}\\ \\ \\ \texttt{NPV} = \frac{(1 - \texttt{prev}) \cdot \texttt{spec}}{\texttt{prev} \cdot (1 - \texttt{sens}) + (1 - \texttt{prev}) \cdot \texttt{spec}} \]

Although this is how the functions `comp_PPV`

and `comp_NPV`

compute the desired conditional probability, it is difficult to remember and think in these terms. Instead, we recommend thinking about and defining all conditional probabilities in terms of frequencies and relations between frequencies (see Neth et al., 2021 for details).

The easiest way to think about, define, and compute the probabilities (contained in `prob`

) is in terms of frequencies (contained in `freq`

):

Nr. | Variable | Name | Definition | as Frequencies |
---|---|---|---|---|

1. | `prev` |
prevalence | The probability of the condition being `TRUE` . |
`prev` = `cond_true` /`N` |

2. | `sens` |
sensitivity | The conditional probability of a positive decision provided that the condition is `TRUE` . |
`sens` = `hi` /`cond_true` |

3. | `mirt` |
miss rate | The conditional probability of a negative decision provided that the condition is `TRUE` . |
`mirt` = `mi` /`cond_true` |

4. | `spec` |
specificity | The conditional probability of a negative decision provided that the condition is `FALSE` . |
`spec` = `cr` /`cond_false` |

5. | `fart` |
false alarm rate | The conditional probability of a positive decision provided that the condition is `FALSE` . |
`fart` = `fa` /`cond_false` |

6. | `ppod` |
proportion of positive decisions | The proportion (baseline probability or rate) of the decision being positive (but not necessarily `TRUE` ). |
`ppod` = `dec_pos` /`N` |

7. | `PPV` |
positive predictive value | The conditional probability of the condition being `TRUE` provided that the decision is positive. |
`PPV` = `hi` /`dec_pos` |

8. | `FDR` |
false detection rate | The conditional probability of the condition being `FALSE` provided that the decision is positive. |
`FDR` = `fa` /`dec_pos` |

9. | `NPV` |
negative predictive value | The conditional probability of the condition being `FALSE` provided that the decision is negative. |
`NPV` = `cr` /`dec_neg` |

10. | `FOR` |
false omission rate | The conditional probability of the condition being `TRUE` provided that the decision is negative. |
`FOR` = `mi` /`dec_neg` |

11. | `acc` |
accuracy | The probability of a correct decision (i.e., correspondence of decisions to conditions). |
`acc` = `dec_cor` /`N` |

12. | `p_acc_hi` |
– | The conditional probability of the condition being `TRUE` provided that a decision or prediction is accurate. |
`p_acc_hi` = `hi` /`dec_cor` |

13. | `p_err_fa` |
– | The conditional probability of the condition being `FALSE` provided that a decision or prediction is inaccurate or erroneous. |
`p_err_fa` = `fa` /`dec_err` |

Note that the ratios between frequencies are straightforward consequences of the probabilities’ definitions:

The unconditional probabilities (1., 6. and 11.) are proportions of the entire population:

`prev`

=`cond_true`

/`N`

`ppod`

=`dec_pos`

/`N`

`acc`

=`dec_cor`

/`N`

The conditional probabilities (2.–5., 7.–10., and 11.–12.) can be computed as a proportion of the reference group on which they are conditional. More specifically, if we schematically read each definition as “The conditional probability of \(X\) provided that \(Y\)”, then the ratio of the corresponding frequencies is

`X & Y`

/`Y`

. More explicitly,

- the ratio’s numerator is the frequency of the joint occurrence (i.e., both
`X & Y`

) being the case; - the ratio’s denominator is the frequency of the condition (
`Y`

) being the case.

When computing probabilities from rounded frequencies, their numeric values may deviate from the true underlying probabilities, particularly for small population sizes `N`

. (Use the `scale`

argument of many **riskyr** plotting functions to control whether probabilities are based on frequencies.)

The following prism (or network) diagram is based on the following inputs:

- a condition’s prevalence of 50% (
`prev = .50`

); - a decision’s sensitivity of 80% (
`sens = .80`

); - a decision’s specificity of 60% (
`spec = .60`

); - a population size of 10 individuals (
`N = 10`

);

and illustrates the relationship between frequencies and probabilities:

```
plot_prism(prev = .50, sens = .80, spec = .60, # 3 essential probabilities
N = 10, # population frequency
scale = "f", # scale by frequency, rather than probability ("p")
area = "sq", # boxes as squares, with sizes scaled by current scale
p_lbl = "num", # show numeric probability values on edges
title_lbl = "Probabilities as ratios between frequencies")
#> Argument 'title_lbl' is deprecated. Please use 'main' instead.
```

Verify that the probabilities (shown as numeric values on the edges) match the ratios of the corresponding frequencies (shown in the boxes). What are the names of these probabilities?

What is the frequency of

`dec_cor`

and`dec_err`

cases? Where do these cases appear in the diagram?The parameter values in the example do not require any rounding of frequencies. Change them (e.g., to

`N = 5`

) and explore what happens when alternating between`scale = "f"`

and`scale = "p"`

.

Gigerenzer, G. (2002).

*Reckoning with risk: Learning to live with uncertainty*. London, UK: Penguin.Gigerenzer, G. (2014).

*Risk savvy: How to make good decisions*. New York, NY: Penguin.Gigerenzer, G., & Hoffrage, U. (1999). Overcoming difficulties in Bayesian reasoning: A reply to Lewis and Keren (1999) and Mellers and McGraw (1999).

*Psychological Review*,*106*, 425–430.Hájek, A (2012) Interpretations of Probability. In Edward N. Zalta (Ed.),

*The Stanford Encyclopedia of Philosophy*. URL: https://plato.stanford.edu/entries/probability-interpret/ 2012 ArchiveHoffrage, U., Gigerenzer, G., Krauss, S., & Martignon, L. (2002). Representation facilitates reasoning: What natural frequencies are and what they are not.

*Cognition*,*84*, 343–352.Neth, H., Gradwohl, N., Streeb, D., Keim, D.A., & Gaissmaier, W. (2021). Perspectives on the 2x2 matrix: Solving semantically distinct problems based on a shared structure of binary contingencies.

*Frontiers in Psychology: Cognition*,*11*, 567817. doi: 10.3389/fpsyg.2020.567817 (Available online)Trevethan, R. (2017). Sensitivity, specificity, and predictive values: Foundations, pliabilities, and pitfalls in research and practice.

*Frontiers in Public Health*,*5*, 307. (Available online)

The following resources and versions are currently available:

Type: | Version: | URL: |
---|---|---|

A. riskyr (R package): |
Release version | https://CRAN.R-project.org/package=riskyr |

Development version | https://github.com/hneth/riskyr/ | |

B. riskyrApp (R Shiny code): |
Online version | https://riskyr.org/ |

Development version | https://github.com/hneth/riskyrApp/ | |

C. Online documentation: | Release version | https://hneth.github.io/riskyr/ |

Development version | https://hneth.github.io/riskyr/dev/ |

We appreciate your feedback, comments, or questions.

Please report any

**riskyr**-related issues at https://github.com/hneth/riskyr/issues/.Contact us at contact.riskyr@gmail.com with any comments, questions, or suggestions.

Nr. | Vignette | Content |
---|---|---|

A. | User guide | Motivation and general instructions |

B. | Data formats | Data formats: Frequencies and probabilities |

C. | Confusion matrix | Confusion matrix and accuracy metrics |

D. | Functional perspectives | Adopting functional perspectives |

E. | Quick start primer | Quick start primer |

Gigerenzer, G. (2014).

*Risk savvy: How to make good decisions*. New York, NY: Penguin. (p. 21).↩︎For instance, we can distinguish between various types of probabilities (e.g.,

*joint*,*marginal*, and*conditional*probabilities) and*natural frequencies*provide a relational concept for expressing*probabilities*. See Neth et al., 2021 for details.↩︎It seems plausible that the notion of a

*frequency*is simpler than the notion of*probability*. Nevertheless, confusion is possible and typically causes serious scientific disputes. See Gigerenzer & Hoffrage, 1999, and Hoffrage et al., 2002, for different types of frequencies and the notion of*natural frequencies*.↩︎For instance, some probabilities are the complements of others and do not have explicit names. Also,

*joint*probabilities (e.g., the probabilities of`hi`

,`mi`

,`fa`

, and`cr`

) are not included in`prob`

, but generated when needed (e.g., see`plot_fnet()`

).↩︎