fit_transformation_df {TriDimRegression} | R Documentation |
Fitting Bidimensional or Tridimensional Regression / Geometric Transformation Models via Two Tables.
Description
Fits Bidimensional or Tridimensional regression / geometric transformation models using
Stan engine. Two sets of coordinates are supplied via iv
(for an independent variable)
and dv
(for the dependent one). The two tables must have the same dimensions
(both N×2 or N×3).
For the 2D data, you can fit "translation"
(2 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")
Usage
fit_transformation_df(
iv,
dv,
transformation,
priors = NULL,
chains = 1,
cores = NULL,
...
)
Arguments
iv |
a data frame containing independent variable, must by numeric only, N×2 or N×3. |
dv |
a data frame containing dependent variable, must by numeric only, N×2 or N×3. |
transformation |
the transformation to be used: |
priors |
named list of parameters for prior distributions of parameters |
chains |
Number of chains for sampling. |
cores |
Number of CPU cores to use for sampling. If omitted, all available cores are used. |
... |
Additional arguments passed to |
Value
A tridim_transformation object
See Also
Examples
# Geometric transformations of 2D data
euc2 <- fit_transformation_df(NakayaData[, 1:2], NakayaData[, 3:4], 'euclidean')
tr3 <- fit_transformation_df(Face3D_W070, Face3D_W097, transformation ='translation')