bplsr

The bplsr package implements the Bayesian partial least squares regression model. It is a Bayesian factor model which emulates the partial least squares (PLS) method. See Urbas et al. (2024) for details.

Installation

Installing from CRAN:

install.packages('bplsr')

Installing directly from GitHub:

# install.packages("devtools")
devtools::install_github("SzymonUrbas/bplsr")

Example

The following example illustrates how to carry out multivariate regression using BPLS on mid-infrared spectral data of milk samples:

library(bplsr)


X = milk_MIR$xMIR
Y = milk_MIR$yTraits[, c('Casein_content','Fat_content')]

set.seed(1)
# fit model to 75% of data and predict on remaining 25%
idx = sample(seq(nrow(X)),floor(nrow(X)*0.75),replace = FALSE)

Xtrain = X[idx,];Ytrain = Y[idx,]
Xtest = X[-idx,];Ytest = Y[-idx,]

# fit the model (MCMC takes time)
bplsr_Fit = bplsr(Xtrain,Ytrain)

# generate predictions
bplsr_pred = bplsr.predict(model = bplsr_Fit, newdata = Xtest)

# point predictions
head(bplsr_pred$Ytest)
#>    Casein_content Fat_content
#> 5        3.142232    3.908393
#> 6        2.557213    4.109032
#> 7        2.739213    4.959536
#> 8        2.949198    5.058209
#> 9        2.773001    4.276898
#> 10       2.703331    4.500888

# lower and upper limits of prediction interval
head(bplsr_pred$Ytest_PI)
#> , , 2.5%
#> 
#>      Casein_content Fat_content
#> [1,]       2.824016    3.156057
#> [2,]       2.242527    3.343650
#> [3,]       2.421603    4.202634
#> [4,]       2.634399    4.301017
#> [5,]       2.449790    3.513700
#> [6,]       2.380676    3.732963
#> 
#> , , 97.5%
#> 
#>      Casein_content Fat_content
#> [1,]       3.468311    4.665851
#> [2,]       2.883415    4.877653
#> [3,]       3.064176    5.742868
#> [4,]       3.270263    5.837006
#> [5,]       3.093597    5.043436
#> [6,]       3.023982    5.269628

# plot of predictive posterior distribution for single test sample
hist(bplsr_pred$Ytest_dist[1,'Casein_content',], freq = F,
     main = 'Posterior predictive distribution', xlab = 'Casein_content')

References

Urbas, S., Lovera, P., Daly, R., O’Riordan, A., Berry, D., and Gormley, I. C. (2024). “Predicting milk traits from spectral data using Bayesian probabilistic partial least squares regression.” The Annals of Applied Statistics, 18(4): 3486-3506 doi:10.1214/24-AOAS1947