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CarbonExample1Data
Carbon, C. C. (2013), data set #1
CarbonExample2Data
Carbon, C. C. (2013), data set #2
CarbonExample3Data
Carbon, C. C. (2013), data set #3
coef(<tridim_transformation> )
Posterior distributions for transformation coefficients
in full or summarized form.
EyegazeData
Eye gaze calibration data
Face3D_M010
Face landmarks, male, #010
Face3D_M101
Face landmarks, male, #101
Face3D_M244
Face landmarks, male, #244
Face3D_M92
Face landmarks, male, #092
Face3D_W070
Face landmarks, female, #070
Face3D_W097
Face landmarks, female, #097
Face3D_W182
Face landmarks, female, #182
Face3D_W243
Face landmarks, female, #243
fit_transformation(<formula> )
Fitting Bidimensional or Tridimensional Regression / Geometric Transformation Models via Formula.
fit_transformation_df()
Fitting Bidimensional or Tridimensional Regression / Geometric Transformation Models via Two Tables.
FriedmanKohlerData1
Friedman & Kohler (2003), data set #1
FriedmanKohlerData2
Friedman & Kohler (2003), data set #2
is.tridim_transformation()
Checks if argument is a tridim_transformation object
loo(<tridim_transformation> )
Computes an efficient approximate leave-one-out
cross-validation via loo library. It can be used
for a model comparison via loo::loo_compare() function.
NakayaData
Nakaya (1997)
plot(<tridim_transformation> )
Posterior interval plots for key parameters. Uses bayesplot::mcmc_intervals.
predict(<tridim_transformation> )
Computes posterior samples for the posterior predictive distribution.
print(<tridim_transformation> )
Prints out tridim_transformation object
R2(<tridim_transformation> )
Computes R-squared using Bayesian R-squared approach.
For detail refer to:
Andrew Gelman, Ben Goodrich, Jonah Gabry, and Aki Vehtari (2018).
R-squared for Bayesian regression models. The American Statistician,
doi:10.1080/00031305.2018.1549100.
summary(<tridim_transformation> )
Summary for a tridim_transformation object
TriDimRegression-package
The 'TriDimRegression' package.
tridim_transformation-class
Class tridim_transformation.
waic(<tridim_transformation> )
Computes widely applicable information criterion
(WAIC).