plsRbeta-package

Source: R/plsRbeta-package.R

Provides Partial least squares Regression for (weighted) beta regression models (Bertrand 2013, <http://journal-sfds.fr/article/view/215>) and k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.

References

Partial least squares Regression for (weighted) beta regression models (Bertrand 2013, <http://journal-sfds.fr/article/view/215>), https://github.com/fbertran/plsRbeta/ et https://fbertran.github.io/plsRbeta/

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____
#> ****________________________________________________****
#> 
modpls$pp

#>              Comp_ 1     Comp_ 2     Comp_ 3
#> gravity   0.37895923 -0.42864981  0.50983922
#> pressure  0.61533000 -0.41618828 -0.01737302
#> temp10   -0.50627633  0.47379983 -0.47750566
#> temp      0.30248369  0.60751756  0.28239621
#> batch1    0.50274128 -0.30221156 -0.25801764
#> batch2   -0.14241033 -0.13859422  0.80068659
#> batch3   -0.04388172 -0.17303214  0.48564161
#> batch4    0.11299471 -0.08302689  0.04755182
#> batch5    0.23341035  0.08396326 -0.51238456
#> batch6    0.07974302  0.07209943 -0.30710455
#> batch7   -0.37365392 -0.02133356  0.81852001
#> batch8   -0.12891598  0.16967195 -0.06904725
#> batch9   -0.02230288  0.19425476 -0.57189134
#> batch10  -0.25409429  0.28587553 -0.61277072
modpls$Coeffs

#>                    [,1]
#> Intercept -4.1210566077
#> gravity    0.0157208676
#> pressure   0.0305159627
#> temp10    -0.0074167766
#> temp       0.0108057945
#> batch1     0.0910284843
#> batch2     0.1398537354
#> batch3     0.2287070465
#> batch4    -0.0008124326
#> batch5     0.1018679027
#> batch6     0.1147971957
#> batch7    -0.1005469609
#> batch8    -0.0447907428
#> batch9    -0.0706292318
#> batch10   -0.1984703429
modpls$Std.Coeffs

#>                    [,1]
#> Intercept -1.5526788976
#> gravity    0.0885938394
#> pressure   0.0799466278
#> temp10    -0.2784359925
#> temp       0.7537685874
#> batch1     0.0305865495
#> batch2     0.0414169259
#> batch3     0.0677303525
#> batch4    -0.0002729861
#> batch5     0.0301676274
#> batch6     0.0339965674
#> batch7    -0.0337848600
#> batch8    -0.0132645358
#> batch9    -0.0173701781
#> batch10   -0.0587759166
modpls$InfCrit

#>                  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
modpls$PredictY[1,]

#>    gravity   pressure     temp10       temp     batch1     batch2     batch3 
#>  2.0495333  1.6866554 -1.3718198 -1.8219769  2.6040833 -0.3165683 -0.3165683 
#>     batch4     batch5     batch6     batch7     batch8     batch9    batch10 
#> -0.3720119 -0.3165683 -0.3165683 -0.3720119 -0.3165683 -0.2541325 -0.3165683 
rm("modpls")

data("GasolineYield",package="betareg")
yGasolineYield <- GasolineYield$yield
XGasolineYield <- GasolineYield[,2:5]
modpls <- plsRbeta(yGasolineYield,XGasolineYield,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 nor in Y____
#> ****________________________________________________****
#> 
modpls$pp

#>             Comp_ 1    Comp_ 2    Comp_ 3
#> gravity   0.4590380 -0.4538663 -2.5188256
#> pressure  0.6395524 -0.4733525  0.6488823
#> temp10   -0.5435643  0.5292108 -1.3295905
#> temp      0.5682795  0.5473174 -0.2156423
modpls$Coeffs

#>                   [,1]
#> Intercept -3.324462301
#> gravity    0.001577508
#> pressure   0.072027686
#> temp10    -0.008398771
#> temp       0.010365973
modpls$Std.Coeffs

#>                   [,1]
#> Intercept -1.547207760
#> gravity    0.008889933
#> pressure   0.188700277
#> temp10    -0.315301400
#> temp       0.723088387
modpls$InfCrit

#>                  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 -112.87383 -108.47662       30.57369 0.05211039   0.8498691 0.8537900
#> Nb_Comp_2 -136.43184 -130.56889       30.97370 0.02290022   0.9256771 0.9357471
#> Nb_Comp_3 -139.08440 -131.75572       31.08224 0.02022386   0.9385887 0.9432564
modpls$PredictY[1,]

#>   gravity  pressure    temp10      temp 
#>  2.049533  1.686655 -1.371820 -1.821977 
rm("modpls")