The following methods for complex data structures are available in
the *multiblock* package (function names in parentheses):

- L-PLS - Partial Least Squares in L configuration
(
*lpls*) - SO-PLS-PM - Sequential and Orthogonalised PLS Path Modeling
(
*sopls_pm*)

To showcase L-PLS we will use simulated data specifically made for
L-shaped data. Regression using L-PLS can be either outwards from
*X1* to *X2* and *X3* or inwards from *X2*
and *X3* to *X1*. In the former case, prediction can
either be of *X2* or *X3* given *X1*.
Cross-validation is performed either on the rows of *X1* or the
columns of *X1*.

```
______N
| |
| |
| X3 |
| |
K|_______|
______N ________J
| | | |
| | | |
| X1 | | X2 |
| | | |
I|_______| I|_________|
```

We simulate two latent components in L shape with blocks having
dimensions (30x20), (20x5) and (6x20) for blocks *X1*,
*X2* and *X3*, respectively.

The first L-PLS will be outwards. Predictions have to be accompanied by a direction.

The second L-PLS will be inwards.

Cross-validation comes with choices of directions when applying this to L-PLS since we have both sample and variable links. The cross-validation routines compute RMSECV values and perform cross-validated predictions.

```
# LOO cross-validation horizontally
lp.cv1 <- lplsCV(lp.exo, segments1 = as.list(1:dim(X1)[1]), trace = FALSE)
# LOO cross-validation vertically
lp.cv2 <- lplsCV(lp.exo, segments2 = as.list(1:dim(X1)[2]), trace = FALSE)
# Three-fold CV, horizontal
lp.cv3 <- lplsCV(lp.exo, segments1 = as.list(1:10, 11:20, 21:30), trace = FALSE)
# Three-fold CV, horizontal, inwards model
lp.cv4 <- lplsCV(lp.endo, segments1 = as.list(1:10, 11:20, 21:30), trace = FALSE)
```

The following example uses the *potato* data and the
*wine* data to showcase some of the functions available for
SO-PLS-PM analyses.

A model with four blocks having 5 components per input block is
fitted. We set *computeAdditional* to *TRUE* to turn on
computation of additional explained variance per added block in the
model.

```
# Load potato data
data(potato)
# Single path
pot.pm <- sopls_pm(potato[1:3], potato[['Sensory']], c(5,5,5), computeAdditional=TRUE)
# Report of explained variances and optimal number of components .
# Bootstrapping can be enabled to assess stability.
# (LOO cross-validation is default)
pot.pm
#> direct indirect total additional1 additional2 overall
#> 0 (0) 52.44 52.44 (3) 4.09 (3) 14.01 (2) 70.55
```

A model containing five blocks is fitted. Explained variances for all sub-paths are estimated.

```
# Load wine data
data(wine)
# All path in the forward direction
pot.pm.multiple <- sopls_pm_multiple(wine, ncomp = c(4,2,9,8))
# Report of direct, indirect and total explained variance per sub-path.
# Bootstrapping can be enabled to assess stability.
pot.pm.multiple
#> $`Smell at rest->View`
#> direct indirect total
#> 32.68 (1) 0 32.68 (1)
#>
#> $`Smell at rest->Smell after shaking`
#> direct indirect total
#> 0 (0) 40.03 40.03 (4)
#>
#> $`Smell at rest->Tasting`
#> direct indirect total
#> 0 (0) 11.52 11.52 (2)
#>
#> $`Smell at rest->Global quality`
#> direct indirect total
#> 0 (0) 25.25 25.25 (3)
#>
#> $`View->Smell after shaking`
#> direct indirect total
#> 30.97 (2) 0 30.97 (2)
#>
#> $`View->Tasting`
#> direct indirect total
#> 0 (0) 41.09 41.09 (2)
#>
#> $`View->Global quality`
#> direct indirect total
#> 0 (0) 30.87 30.87 (2)
#>
#> $`Smell after shaking->Tasting`
#> direct indirect total
#> 56.67 (3) 0 56.67 (3)
#>
#> $`Smell after shaking->Global quality`
#> direct indirect total
#> 0 (0) 70.15 70.15 (2)
#>
#> $`Tasting->Global quality`
#> direct indirect total
#> 78.12 (2) 0 78.12 (2)
```