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README.Rmd
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README.Rmd
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---
output: github_document
bibliography: inst/REFERENCES.bib
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
library(inspector)
```
# inspector: Validation of Arguments and Objects in User-Defined Functions
<!-- badges: start -->
[](https://CRAN.R-project.org/package=inspector)
[](https://travis-ci.com/ptfonseca/inspector)
[](https://github.com/ptfonseca/inspector/actions)
[](https://codecov.io/gh/ptfonseca/inspector)
[](https://opensource.org/licenses/MIT)
<!-- badges: end -->
## Overview
The `inspector` package provides utility functions that implement and automate common sets of validation tasks, namely:
- `inspect_prob()` checks if an object is a numeric vector of valid probability values.
- `inspect_log_base()` checks if an object is a valid logarithmic base.
- `inspect_true_or_false()` checks if an object is a non-missing logical value.
- `inspect_bfactor()` checks if an object is a numeric vector of valid Bayes factors values.
- `inspect_bfactor_log()` checks if an object is a numeric vector of valid logarithmic Bayes factors values.
- `inspect_bfactor_scale()` validates Bayes factor interpretation scales (from the [`pcal`](https://ptfonseca.github.io/pcal/) package).
- `inspect_categories()` validates factor levels.
- `inspect_character()` validates character vectors.
- `inspect_character_match()` validates character values with predefined allowed values.
- `inspect_data_dichotomous()` validates dichotomous data
- `inspect_data_categorical()` and `inspect_data_cat_as_dichotom()` validate categorical data.
- `inspect_par_bernoulli()` validates parameters for the Bernoulli and Binomial distributions.
- `inspect_par_multinomial()` validates parameters for the Multinomial distribution.
- `inspect_par_beta()` validates parameters for the Beta distribution.
- `inspect_par_dirichlet()` validates parameters for the Dirichlet distribution.
- `inspect_par_haldane()` validates parameters for the Haldane distribution.
These functions are particularly useful to validate inputs, intermediate objects and output values in user-defined functions, resulting in tidier and less verbose functions.
## Installation
The development version of `inspector` can be installed from
Github with the [`devtools`](https://devtools.r-lib.org) package:
```{r, eval=FALSE}
# install.packages("devtools")
devtools::install_github("ptfonseca/inspector")
```
## Usage
Imagine we want to write a function that simulates `n` flips of the same coin. Assuming that `bias` is the probability of the "heads" outcome:
```{r}
set.seed(123)
flip_coins <- function(n, bias) {
sample(x = c("heads", "tails"), size = n, replace = TRUE)
}
flip_coins(n = 5, bias = 0.5)
```
Since `bias` is a probability, it is natural that we require `flip_coins()`
to only accept values of `bias` between 0 and 1. Furthermore, we may want to ensure that `bias` is not null, not missing, and is a numeric vector of length 1. This results an a quite verbose function body:
```{r}
set.seed(123)
flip_coins <- function(n, bias) {
if (is.null(bias)) {
stop(paste("Invalid argument: bias is NULL."))
}
if (any(isFALSE(is.atomic(bias)), isFALSE(is.vector(bias)))) {
stop(paste("Invalid argument: bias must be an atomic vector."))
}
if (isFALSE(length(bias) == 1)) {
stop(paste("Invalid argument: bias must be of length 1."))
}
if (is.na(bias)) {
stop(paste("Invalid argument: bias is NA or NaN."))
}
if (isFALSE(is.numeric(bias))) {
stop(paste("Invalid argument: bias must be numeric."))
}
if (any(bias >= 1, bias <= 0)) {
stop(paste("Invalid argument: bias must be in the (0, 1) interval."))
}
sample(x = c("heads", "tails"), size = n, replace = TRUE)
}
flip_coins(n = 5, bias = 0.5)
```
The `inspector` package was built to automate this kind of validation task. In the `flip_coins()` example, to perform an equivalent validation of inputs we can use `inspect_par_bernoulli`, since `bias` is the parameter of a Bernoulli distribution:
```{r}
set.seed(123)
flip_coins <- function(n, bias) {
inspect_par_bernoulli(bias)
sample(x = c("heads", "tails"), size = n, replace = TRUE)
}
flip_coins(n = 5, bias = 0.5)
```
This results in a tidier function body since the validation of `bias` is abstracted away from the body of the function.
Now imagine we want to implement equation 4 from @bergerDelampady1987, a formula that calculates posterior probabilities using prior probabilities and Bayes factors as input. In this case we need to validate a vector of Bayes factors, lets call it `bf`, and a vector of prior probabilities, lets call it `prior_prob`. Since `bf` is expected to contain valid Bayes factor values, we need to ensure that only non-empty numeric vectors, containing only non-negative values, are accepted. Since `prior_prob` is a vector of probabilities, we need to check if it is a non-empty numeric vector containing only values between 0 and 1. Since we are now validating two inputs, the function body would be even more verbose than in the `flip_coins()` example:
```{r}
bfactor_to_prob <- function(bf, prior_prob = .5) {
if (is.null(bf)) {
stop(paste("Invalid argument: bf is NULL."))
}
if (any(isFALSE(is.atomic(bf)), isFALSE(is.vector(bf)))) {
stop(paste("Invalid argument: bf must be an atomic vector."))
}
if (length(bf) == 0) {
stop(paste("Invalid argument: bf is empty."))
}
if (all(is.na(bf))) {
stop(paste("Invalid argument: all elements of bf are NA or NaN."))
}
if (isFALSE(is.numeric(bf))) {
stop(paste("Invalid argument: the type of bf must be numeric."))
}
if (any(bf[!is.na(bf)] < 0)) {
stop(paste("Invalid argument: all elements of bf must be non-negative."))
}
if (is.null(prior_prob)) {
stop(paste("Invalid argument:", output_name, "is NULL."))
}
if (any(isFALSE(is.atomic(prior_prob)), isFALSE(is.vector(prior_prob)))) {
stop(paste("Invalid argument:", output_name, "must be an atomic vector."))
}
if (length(prior_prob) == 0) {
stop(paste("Invalid argument:", output_name, "is empty."))
}
if (all(is.na(prior_prob))) {
stop(paste("Invalid argument: all elements of", output_name, "are NA or NaN."))
}
if (isFALSE(is.numeric(prior_prob))) {
stop(paste("Invalid argument: the type of", output_name, "must be numeric."))
}
if (any(prior_prob[!is.na(prior_prob)] < 0, prior_prob[!is.na(prior_prob)] > 1)) {
stop(paste("Invalid argument: all elements of", output_name, "must be in the [0, 1] interval."))
}
(1 + (1 - prior_prob) / prior_prob * (1 / bf)) ^(-1)
}
bfactor_to_prob(c(2.1, 0.5, 11))
```
Now lets use `inspector` instead. To perform an equivalent validation of inputs we can use `inspect_bfactor()` to validate `bf` and `inspect_prob()` to validate `prior_prob`:
```{r}
bfactor_to_prob <- function(bf, prior_prob = .5) {
inspect_bfactor(bf)
inspect_prob(prior_prob)
(1 + (1 - prior_prob) / prior_prob * (1 / bf)) ^ (-1)
}
bfactor_to_prob(c(2.1, 0.5, 11))
```
## Getting Help
If you find a bug, please file an issue with a minimal reproducible
example on [GitHub](https://github.com/ptfonseca/inspector/issues).
Feature requests are also welcome. You can find me at
<ptfonseca@iseg.ulisboa.pt>.
## References