TriDimRegression

Package to calculate the bidimensional and tridimensional regression between two 2D/3D configurations.

Installation from Github

library("devtools"); install_github("alexander-pastukhov/tridim-regression", dependencies=TRUE)

If you want vignettes, us

devtools::install_github("alexander-pastukhov/tridim-regression",
                         dependencies=TRUE,
                         build_vignettes = TRUE)

Using TriDimRegression

You can call the main function either via a formula that specifies dependent and independent variables with the data table or by supplying two tables one containing all independent variables and one containing all dependent variables. The former call is

euc2 <- fit_transformation(depV1 + depV2 ~ indepV1 + indepV2, NakayaData, 'euclidean')

whereas the latter is

euc3 <- fit_transformation_df(Face3D_W070, Face3D_W097, transformation ='translation')

See also vignette("calibration", package="TriDimRegression") for an example of using TriDimRegression for 2D eye gaze data and vignette("comparing_faces", package="TriDimRegression") for an example of working with 3D facial landmarks data.

For the 2D data, you can fit "translation" (2 parameters for translation only), "euclidean" (4 parameters: 2 for translation, 1 for scaling, and 1 for rotation), "affine" (6 parameters: 2 for translation and 4 that jointly describe scaling, rotation and sheer), or "projective" (8 parameters: affine plus 2 additional parameters to account for projection). For 3D data, you can fit "translation" (3 for translation only), "euclidean_x", "euclidean_y", "euclidean_z" (5 parameters: 3 for translation scale, 1 for rotation, and 1 for scaling), "affine" (12 parameters: 3 for translation and 9 to account for scaling, rotation, and sheer), and "projective" (15 parameters: affine plus 3 additional parameters to account for projection). transformations. For details on how matrices are constructed, see vignette("transformation_matrices", package="TriDimRegression").

Once the data is fitted, you can extract the transformation coefficients via coef() function and the matrix itself via transformation_matrix(). Predicted data, either based on the original data or on the new data, can be generated via predict(). Bayesian R-squared can be computed with or without adjustment via R2() function. In all three cases, you have choice between summary (mean + specified quantiles) or full posterior samples. loo() and waic() provide corresponding measures that can be used for comparison via loo::loo_compare() function.

References

• Tobler, W. R. (1965). Computation of the corresponding of geographical patterns. Papers of the Regional Science Association, 15, 131-139.
• Tobler, W. R. (1966). Medieval distortions: Projections of ancient maps. Annals of the Association of American Geographers, 56(2), 351-360.
• Tobler, W. R. (1994). Bidimensional regression. Geographical Analysis, 26(3), 187-212.
• Friedman, A., & Kohler, B. (2003). Bidimensional regression: Assessing the configural similarity and accuracy of cognitive maps and other two-dimensional data sets. Psychological Methods, 8(4), 468-491.
• Nakaya, T. (1997). Statistical inferences in bidimensional regression models. Geographical Analysis, 29(2), 169-186.
• Waterman, S., & Gordon, D. (1984). A quantitative-comparative approach to analysis of distortion in mental maps. Professional Geographer, 36(3), 326-337.

License

All code is licensed under the GPL 3.0 license.