# Getting Started with plsRglm

#### Frederic Bertrand

#### Myriam Maumy-Bertrand

#### March 2026

`plsRglm` provides partial least squares regression for linear and generalized linear models, repeated k-fold cross-validation, bootstrap utilities, and support for incomplete predictor matrices. This vignette is the practical starting point for the current package API. The companion vignette `vignette("plsRglm", package = "plsRglm")` keeps the longer historical case studies and algorithmic notes.

# Core Fitting Workflows

`plsR()` is the dedicated interface for ordinary PLS regression. `plsRglm()` extends the same ideas to generalized linear and ordinal models, and can also fit `modele = "pls"` through the shared interface.

## Linear PLS with matrix and formula interfaces

    data(Cornell)
    XCornell <- Cornell[, 1:7]
    yCornell <- Cornell$Y

    pls_fit_matrix <- plsR(yCornell, XCornell, nt = 3, verbose = FALSE)
    pls_fit_formula <- plsR(Y ~ ., data = Cornell, nt = 3, pvals.expli = TRUE, verbose = FALSE)

    pls_fit_formula$InfCrit
    #>                AIC      RSS_Y      R2_Y R2_residY RSS_residY    AIC.std
    #> Nb_Comp_0 82.01205 467.796667        NA        NA 11.0000000  37.010388
    #> Nb_Comp_1 53.15173  35.742486 0.9235940 0.9235940  0.8404663   8.150064
    #> Nb_Comp_2 41.08283  11.066606 0.9763431 0.9763431  0.2602256  -3.918831
    #> Nb_Comp_3 32.06411   4.418081 0.9905556 0.9905556  0.1038889 -12.937550
    #>            DoF.dof sigmahat.dof    AIC.dof    BIC.dof GMDL.dof DoF.naive
    #> Nb_Comp_0 1.000000    6.5212706 46.0708838 47.7893514 27.59461         1
    #> Nb_Comp_1 2.740749    1.8665281  4.5699686  4.9558156 21.34020         2
    #> Nb_Comp_2 5.085967    1.1825195  2.1075461  2.3949331 27.40202         3
    #> Nb_Comp_3 5.121086    0.7488308  0.8467795  0.9628191 24.40842         4
    #>           sigmahat.naive  AIC.naive  BIC.naive GMDL.naive
    #> Nb_Comp_0      6.5212706 46.0708838 47.7893514   27.59461
    #> Nb_Comp_1      1.8905683  4.1699567  4.4588195   18.37545
    #> Nb_Comp_2      1.1088836  1.5370286  1.6860917   17.71117
    #> Nb_Comp_3      0.7431421  0.7363469  0.8256118   19.01033
    coef(pls_fit_formula)
    #> Coefficients of the components
    #> Coeff_Comp_Reg1 Coeff_Comp_Reg2 Coeff_Comp_Reg3
    #>       0.4820365       0.2731127       0.1030689
    #> Coefficients of the predictors (original scale)
    #>                 [,1]
    #> Intercept  92.675989
    #> X1         -9.828318
    #> X2         -6.960181
    #> X3        -16.666239
    #> X4         -8.421802
    #> X5         -4.388934
    #> X6         10.161304
    #> X7        -34.528959

The fitted model stores the extracted components (`tt`), the loadings (`pp`), the coefficients on the original predictors (`Coeffs`), and information-criterion summaries (`InfCrit`).

## Generalized PLS models

    data(aze_compl)
    logit_fit <- plsRglm(y ~ ., data = aze_compl, nt = 3, modele = "pls-glm-logistic", verbose = FALSE)

    logit_fit$InfCrit
    #>                AIC      BIC Missclassed Chi2_Pearson_Y    RSS_Y      R2_Y
    #> Nb_Comp_0 145.8283 148.4727          49      104.00000 25.91346        NA
    #> Nb_Comp_1 118.1398 123.4285          28      100.53823 19.32272 0.2543365
    #> Nb_Comp_2 109.9553 117.8885          26       99.17955 17.33735 0.3309519
    #> Nb_Comp_3 105.1591 115.7366          22      123.37836 15.58198 0.3986915
    #>            R2_residY RSS_residY
    #> Nb_Comp_0         NA   25.91346
    #> Nb_Comp_1  -6.004879  181.52066
    #> Nb_Comp_2  -9.617595  275.13865
    #> Nb_Comp_3 -12.332217  345.48389
    head(predict(logit_fit, type = "response"))
    #>           1           2           3           4           5           6
    #> 0.631858261 0.166440274 0.006791838 0.414180215 0.022736369 0.030654567

    family_fit <- plsRglm(
      Y ~ .,
      data = Cornell,
      nt = 2,
      modele = "pls-glm-family",
      family = gaussian(link = "log"),
      verbose = FALSE
    )

    family_fit$family$family
    #> [1] "gaussian"
    family_fit$family$link
    #> [1] "log"

`plsRglm()` supports predefined model shortcuts together with a custom-family entry point:

    plsRglm(Y ~ ., data = Cornell, nt = 3, modele = "pls")
    plsRglm(Y ~ ., data = Cornell, nt = 3, modele = "pls-glm-gaussian")
    plsRglm(Y ~ ., data = Cornell, nt = 3, modele = "pls-glm-inverse.gaussian")
    plsRglm(y ~ ., data = aze_compl, nt = 3, modele = "pls-glm-logistic")
    data(pine)
    plsRglm(round(x11) ~ ., data = pine, nt = 3, modele = "pls-glm-poisson")
    plsRglm(x11 ~ ., data = pine, nt = 3, modele = "pls-glm-Gamma")
    plsRglm(Quality ~ ., data = bordeaux, nt = 2, modele = "pls-glm-polr")
    plsRglm(
      Y ~ .,
      data = Cornell,
      nt = 3,
      modele = "pls-glm-family",
      family = gaussian(link = "log")
    )

Ordinal responses are handled through `modele = "pls-glm-polr"`. As with `MASS::polr()`, the response should be an ordered factor:

    data(bordeaux)
    bordeaux$Quality <- factor(bordeaux$Quality, ordered = TRUE)
    polr_fit <- plsRglm(Quality ~ ., data = bordeaux, nt = 2, modele = "pls-glm-polr", verbose = FALSE)

    head(predict(polr_fit, type = "class"))
    #> [1] 3 3 3 3 1 1
    #> Levels: 1 2 3

# Cross-Validation and Model Choice

Use `cv.plsR()` for ordinary PLS regression and `cv.plsRglm()` for generalized models. Both provide repeated k-fold cross-validation and integrate with `summary()` and `cvtable()`.

    cv_pls <- cv.plsR(Y ~ ., data = Cornell, nt = 3, K = 4, NK = 2, verbose = FALSE)
    cv_pls_summary <- cvtable(summary(cv_pls))
    #> ____************************************************____
    #> ____Component____ 1 ____
    #> ____Component____ 2 ____
    #> ____Component____ 3 ____
    #> ____Predicting X without NA neither in X nor in Y____
    #> ****________________________________________________****
    #>
    #>
    #> NK: 1,  2
    #>
    #> CV Q2 criterion:
    #> 0 1
    #> 0 2
    #>
    #> CV Press criterion:
    #> 1 2 3
    #> 1 0 1

    cv_pls_summary
    #> $CVQ2
    #>
    #> 0 1
    #> 0 2
    #>
    #> $CVPress
    #>
    #> 1 2 3
    #> 1 0 1
    #>
    #> attr(,"class")
    #> [1] "table.summary.cv.plsRmodel"
    plot(cv_pls_summary)

    cv_logit <- cv.plsRglm(
      y ~ .,
      data = aze_compl,
      nt = 3,
      K = 4,
      NK = 2,
      modele = "pls-glm-logistic",
      verbose = FALSE
    )
    cv_logit_summary <- cvtable(summary(cv_logit, MClassed = TRUE))
    #> ____************************************************____
    #>
    #> Family: binomial
    #> Link function: logit
    #>
    #> ____Component____ 1 ____
    #> ____Component____ 2 ____
    #> ____Component____ 3 ____
    #> ____Predicting X without NA neither in X or Y____
    #> ****________________________________________________****
    #>
    #>
    #> NK: 1,  2
    #>
    #> CV MissClassed criterion:
    #> 1
    #> 0
    #>
    #> CV Q2Chi2 criterion:
    #> 0
    #> 0
    #>
    #> CV PreChi2 criterion:
    #> 1
    #> 0

    cv_logit_summary
    #> $CVMC
    #>
    #> 1
    #> 0
    #>
    #> $CVQ2Chi2
    #>
    #> 0
    #> 0
    #>
    #> $CVPreChi2
    #>
    #> 1
    #> 0
    #>
    #> attr(,"class")
    #> [1] "table.summary.cv.plsRglmmodel"
    plot(cv_logit_summary)

For generalized models, `summary(..., MClassed = TRUE)` exposes miss-classification information when it is relevant.

# Prediction and Missing Data

Incomplete predictor matrices are a core package feature, both during fitting and during prediction.

    data(pine)
    data(pine_sup)
    data(pineNAX21)

    pred_fit <- plsRglm(
      x11 ~ .,
      data = pine,
      nt = 3,
      modele = "pls-glm-family",
      family = gaussian(),
      verbose = FALSE
    )

    pine_sup_small <- pine_sup[1:3, 1:10]
    pine_sup_small[1, 1] <- NA

    predict(pred_fit, newdata = pine_sup_small, type = "response", methodNA = "missingdata")
    #> Prediction as if missing values in every row.
    #> [1] 1.069831 1.044208 1.099871
    predict(pred_fit, newdata = pine_sup_small, type = "scores", methodNA = "missingdata")
    #> Prediction as if missing values in every row.
    #>          Comp_1     Comp_2      Comp_3
    #> [1,] -1.6372897  1.6379888 -0.70670174
    #> [2,]  2.7246680 -0.9524725  0.08931408
    #> [3,]  0.2109157  0.1437880  0.60713672

    missing_train_fit <- plsR(x11 ~ ., data = pineNAX21, nt = 3, verbose = FALSE)
    missing_train_fit$na.miss.X
    #> [1] TRUE

When `newdata` contains incomplete rows, `methodNA = "missingdata"` treats all prediction rows with the missing-data scoring rule, while `methodNA = "adaptative"` switches between complete-row and incomplete-row formulas automatically.

# Bootstrap Utilities

`bootpls()` and `bootplsglm()` wrap the `boot` package for PLS and PLS-GLM models. The default resampling schemes differ:

- `bootpls()` defaults to `(y, X)` resampling with `typeboot = "plsmodel"`.
- `bootplsglm()` defaults to `(y, T)` resampling with `typeboot = "fmodel_np"`.

For a lightweight vignette render, the examples below use a small number of resamples and request non-BCa confidence intervals.

    boot_pls <- bootpls(pls_fit_formula, R = 20, verbose = FALSE)
    dim(boot_pls$t)
    #> [1] 20  8
    confints.bootpls(boot_pls, indices = 2:4, typeBCa = FALSE)
    #>    Normal.Lower Normal.Upper Basic.Lower Basic.Upper Percentile.Lower
    #> X1   -0.2475717  -0.03327075  -0.2245013 -0.02750589       -0.2506774
    #> X2   -0.3536188  -0.13085016  -0.3745613 -0.16541114       -0.2519763
    #> X3   -0.2439306  -0.03274124  -0.2214286 -0.02580527       -0.2493054
    #>    Percentile.Upper
    #> X1      -0.05368204
    #> X2      -0.04282623
    #> X3      -0.05368204
    #> attr(,"typeBCa")
    #> [1] FALSE

    boot_logit <- bootplsglm(logit_fit, R = 20, verbose = FALSE)
    dim(boot_logit$t)
    #> [1] 20 33
    confints.bootpls(boot_logit, indices = 1:4, typeBCa = FALSE)
    #>         Normal.Lower Normal.Upper Basic.Lower Basic.Upper Percentile.Lower
    #> D2S138   -0.06589283 -0.008551969 -0.06363197 -0.01614121      -0.06403926
    #> D18S61    0.05549884  0.211389097  0.05344981  0.21637898       0.06378505
    #> D16S422  -0.05412552 -0.010392387 -0.05157091 -0.01447845      -0.05425966
    #> D17S794   0.02916771  0.100612731  0.02988348  0.10029296       0.03541179
    #>         Percentile.Upper
    #> D2S138        -0.0165485
    #> D18S61         0.2267142
    #> D16S422       -0.0171672
    #> D17S794        0.1058213
    #> attr(,"typeBCa")
    #> [1] FALSE

The plotting helpers `boxplots.bootpls()` and `plots.confints.bootpls()` can be applied directly to these bootstrap objects when a graphical summary is helpful.

# Further Reading

- Use `vignette("plsRglm", package = "plsRglm")` for the historical applications and algorithmic note.
- Use the function help pages for lower-level weighted constructors such as `PLS_lm_wvc()` and `PLS_glm_wvc()`.
- Use `?cv.plsRglm`, `?bootplsglm`, and `?predict.plsRglmmodel` for the full argument reference.

    sessionInfo()
    #> R version 4.5.2 (2025-10-31)
    #> Platform: aarch64-apple-darwin20
    #> Running under: macOS Sonoma 14.7.1
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    #> BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
    #> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
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    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #> [1] plsRglm_1.7.1
    #>
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    #>  [1] sass_0.4.10           lattice_0.22-7        digest_0.6.39
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