| Title: | Robust Model Fitting Using the RANSAC Algorithm |
| Version: | 0.1.0 |
| Description: | Provides tools for robust regression model fitting using the RANSAC (Random Sample Consensus) algorithm. RANSAC is an iterative method to estimate parameters of a model from a dataset that contains outliers. This package allows fitting both linear lm and nonlinear nls models using RANSAC, helping users obtain more reliable models in the presence of noisy or corrupted data. The methods are particularly useful in contexts where traditional least squares regression fails due to the influence of outliers. Implementations include support for performance metrics such as RMSE, MAE, and R² based on the inlier subset. For further details, see Fischler and Bolles (1981) <doi:10.1145/358669.358692>. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Depends: | R (≥ 4.0.0) |
| Imports: | stats |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2025-05-06 10:45:59 UTC; Jadson |
| Author: | Jadson Abreu [aut, cre] |
| Maintainer: | Jadson Abreu <jadson.ap@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-05-07 10:30:06 UTC |
Performance Metrics for RANSAC Models
Description
Calculates the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²) for a model fitted using the RANSAC algorithm.
Usage
metrics_ransac(model, data)
Arguments
model |
A model fitted via RANSAC ('ransac_model' or 'ransac_nls'). |
data |
Data frame containing the model variables. |
Value
A data frame with RMSE, MAE, and R² calculated on the inliers.
Examples
set.seed(123)
D <- seq(10, 50, by = 5)
H <- seq(15, 30, length.out = length(D))
V <- 0.01 * D^2 * H + rnorm(length(D), sd = 5)
V[c(3, 7)] <- V[c(3, 7)] + 50 # add outliers
data <- data.frame(D = D, H = H, V = V)
model <- ransac_nls(V ~ a * D^b * H^c, data = data,
start = list(a = 0.01, b = 2, c = 1),
n_min = 4, n_iter = 100, tol = 10)
metrics_ransac(model, data)
Robust Nonlinear Model Fitting via RANSAC
Description
Fits a robust nonlinear model ('nls') using the RANSAC algorithm.
Usage
ransac_nls(
formula,
data,
start,
n_min,
n_iter = 100,
tol = 0.2,
verbose = FALSE
)
Arguments
formula |
Model formula. |
data |
Data frame containing the model variables. |
start |
Named list of initial parameter estimates (as required by 'nls'). |
n_min |
Minimum number of points to fit the model. |
n_iter |
Number of iterations (higher values make the model more robust). |
tol |
Absolute tolerance to consider a point as an inlier. |
verbose |
If 'TRUE', shows progress messages. |
Value
An 'nls' model fitted only to the inliers, with an additional class '"ransac_nls"' and an '"inliers"' attribute.
Examples
set.seed(123)
D <- seq(10, 50, by = 5)
H <- seq(15, 30, length.out = length(D))
V <- 0.01 * D^2 * H + rnorm(length(D), sd = 5)
V[c(3, 7)] <- V[c(3, 7)] + 50 # add outliers
data <- data.frame(D = D, H = H, V = V)
model <- ransac_nls(V ~ a * D^b * H^c, data = data,
start = list(a = 0.01, b = 2, c = 1),
n_min = 4, n_iter = 100, tol = 10)
summary(model)
Robust Linear Model Fitting via RANSAC
Description
Fits a robust linear model ('lm') using the RANSAC algorithm.
Usage
ransac_reg(formula, data, n_min, n_iter = 100, tol = 0.2, verbose = FALSE)
Arguments
formula |
Model formula (as in 'lm'). |
data |
Data frame containing the model variables. |
n_min |
Minimum number of points to fit the model (e.g., 2 for a straight line). |
n_iter |
Number of iterations (higher values make the model more robust). |
tol |
Absolute tolerance to consider a point as an inlier. |
verbose |
If 'TRUE', shows progress messages. |
Value
An 'lm' model fitted only to the inliers, with an additional class '"ransac_model"' and an '"inliers"' attribute.
Examples
set.seed(123)
x <- 1:10
y <- c(1, 2, 3, 4, 5, 6, 7, 8, 50, 60) # some outliers
data <- data.frame(x = x, y = y)
model <- ransac_reg(y ~ x, data = data, n_min = 2, tol = 5)
summary(model)