TriDimRegression-package: The 'TriDimRegression' package.

Description References See Also Examples

Description

Fits 2D and 3D geometric transformations. Provides posterior via Stan. Includes computation of LOO and WAIC information criteria, R-squared.

To fit transformation, 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.

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 transformation matrices and computation of scale and rotation parameters please 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

Stan Development Team (2020). RStan: the R interface to Stan. R package version 2.19.3. https://mc-stan.org

See Also

fit_transformation fit_transformation_df tridim_transformation vignette("transformation_matrices", package = "TriDimRegression") vignette("calibration", package = "TriDimRegression") vignette("comparing_faces", package = "TriDimRegression")

Examples

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# Fitting via formula
euc2 <- fit_transformation(depV1 + depV2 ~ indepV1 + indepV2,
                           NakayaData, 'euclidean')
aff2 <- fit_transformation(depV1 + depV2 ~ indepV1 + indepV2,
                           NakayaData, 'affine')
prj2 <- fit_transformation(depV1 + depV2 ~ indepV1 + indepV2,
                           NakayaData, 'projective')

# summary of transformation coefficients
coef(euc2)

# statistical comparison via WAIC criterion
loo::loo_compare(waic(euc2), waic(aff2), waic(prj2))

# Fitting via two tables
euc2 <- fit_transformation_df(NakayaData[, 1:2], NakayaData[, 3:4],
  'euclidean')
tr3 <- fit_transformation_df(Face3D_W070, Face3D_W097, transformation ='translation')

TriDimRegression documentation built on May 4, 2021, 9:08 a.m.