```
# Start the multiblock R package
library(multiblock)
```

The following single- and two-block methods are available in the *multiblock* package (function names in parentheses):

- PCA - Principal Component Analysis (
*pca*) - PCR - Principal Component Regression (
*pcr*) - PLSR - Partial Least Squares Regression (
*plsr*) - CCA - Canonical Correlation Analysis (
*cca*) - IFA - Interbattery Factor Analysis (
*ifa*) - GSVD - Generalized SVD (
*gsvd*)

The following sections will describe how to format your data for analysis and invoke all methods from the list above.

We use a selection of extracts from the potato data included in the package for the basic data analyses. The data set is stored as a named list of nine matrices with chemical, rheological, spectral and sensory measurements with measurements from 26 raw and cooked potatoes.

```
data(potato)
<- potato$Chemical
X <- potato$Sensory[,1,drop=FALSE] y
```

Since the basic methods cover both single block analysis, supervised and unsupervised analysis, the interfaces for the basic methods vary a bit. Supervised methods use the formula interface and the remaining methods take input as a single matrix or list of matrices. See vignettes for supervised and unsupervised analysis for details.

```
# Single block
<- pca(X, ncomp = 2)
pot.pca
# Two blocks, supervised
<- pcr(y ~ X, ncomp = 2)
pot.pcr <- plsr(y ~ X, ncomp = 2)
pot.pls
# Two blocks, unsupervised
<- cca(potato[1:2])
pot.cca <- ifa(potato[1:2])
pot.ifa
# Variable linked decomposition
<- gsvd(lapply(potato[3:4], t)) pot.gsvd
```

Output from all methods include matrices called *loadings*, *scores*, *blockLoadings* and *blockScores*, or a suitable subset of these according the method used. An *info* list describes which types of (block) loadings/scores are in the output. There may be various extra elements in addition to the common elements, e.g. coefficients, weights etc. The *names()* and *summary()* functions below show all elements of the object and a summary based on the *info* list, respectively.

```
# PCA returns loadings and scores:
names(pot.pca)
#> [1] "loadings" "scores" "Xmeans" "explvar" "PCA" "info" "call"
summary(pot.pca)
#> Principal Component Analysis
#> ============================
#>
#> $scores: Scores (26x2)
#> $loadings: Loadings (14x2)
# GSVD returns block scores and common loadings:
names(pot.gsvd)
#> [1] "loadings" "blockScores" "GSVD" "info" "call"
summary(pot.gsvd)
#> Generalized Singular Value Decomposition
#> ========================================
#>
#> $loadings: Loadings (26x26)
#> $blockScores: Block scores:
#> - NIRraw (1050x1050), NIRcooked (1050x1050)
```

Functions for accessing scores and loadings are based on functions from the *pls* package, but extended with a *block* parameter to allow extraction of common/global scores/loadings and their block counterparts. The default value for *block* is 0, corresponding to the common/global block. Block scores/loadings can be accessed by setting *block* to a number or name.

```
# Global scores plotted with object labels
scoreplot(pot.pca, labels = "names")
```

```
# Block loadings for Chemical block with variable labels in scatter format
loadingplot(pot.cca, block = "Chemical", labels = "names")
```

```
# Non-existing elements are swapped with existing ones with a warning.
<- scores(pot.cca)
sc #> Warning in scores.multiblock(pot.cca): No global/consensus scores. Returning
#> block 1 scores.
```