This function provides a print method for the class "plsRbetamodel"
Usage
# S3 method for class 'plsRbetamodel'
print(x, ...)References
Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. https://ojs-test.apps.ocp.math.cnrs.fr/index.php/J-SFdS/article/view/215
Author
Frédéric Bertrand
frederic.bertrand@lecnam.net
https://fbertran.github.io/homepage/
Examples
data("GasolineYield",package="betareg")
modpls <- plsRbeta(yield~.,data=GasolineYield,nt=3,modele="pls-beta")
#> ____************************************************____
#>
#> Model: pls-beta
#>
#> Link: logit
#>
#> Link.phi:
#>
#> Type: ML
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
print(modpls)
#> Number of required components:
#> [1] 3
#> Number of successfully computed components:
#> [1] 3
#> Coefficients:
#> [,1]
#> Intercept -4.1210566075
#> gravity 0.0157208676
#> pressure 0.0305159627
#> temp10 -0.0074167766
#> temp 0.0108057945
#> batch1 0.0910284844
#> batch2 0.1398537354
#> batch3 0.2287070464
#> batch4 -0.0008124326
#> batch5 0.1018679027
#> batch6 0.1147971957
#> batch7 -0.1005469609
#> batch8 -0.0447907428
#> batch9 -0.0706292318
#> batch10 -0.1984703429
#> Information criteria and Fit statistics:
#> AIC BIC Chi2_Pearson_Y RSS_Y pseudo_R2_Y R2_Y
#> Nb_Comp_0 -52.77074 -49.83927 30.72004 0.35640772 NA NA
#> Nb_Comp_1 -87.96104 -83.56383 31.31448 0.11172576 0.6879757 0.6865226
#> Nb_Comp_2 -114.10269 -108.23975 33.06807 0.04650238 0.8671800 0.8695248
#> Nb_Comp_3 -152.71170 -145.38302 30.69727 0.01138837 0.9526757 0.9680468