Plot component scores from an analysis of multiple data blocks

Bivariate scatterplot (or plots) of the score values from an analysis of data blocks containing shape information

scoresPlot(
  result,
  comp = c(1, 2),
  pcol = NULL,
  plabels = NULL,
  consensus.only = FALSE
)

Arguments

result	result produced by the analyseBlocks function.
comp	the components selected to be shown along the scatter plot axes. Default is component one and component two. The first selected component will be shown along the horizontal axis and the second selected component will be shown along the vertical axis. The selected components must be within the range of components calculated by `analyseBlocks`.
pcol	optional colour value (integer, hex code, colour name) or vector of colour values to be applied to the points in the scatterplot. If no value is specified then points will be coloured in a gradient from black to green according to their sequence in the data blocks. If a single integer is supplied (e.g. `pcol = 1` or `pcol = "red"` or `pcol = "#ffffff"`) then all points will have the same colour. If a vector of length `n` is supplied (e.g. `pcol = 1:10`) then each point will be coloured by a value corresponding to its position in the vector.
plabels	optional user-supplied vector of labels for labelling points in scores plot.
consensus.only	a logical value (default value `FALSE`), relevant only if analyses were performed using Regularized Consensus Principal Component Analysis (i.e. `analyseBlocks`, `option = 'rcpca'`). If `TRUE`, only plot the global scores and the consensus space.

Value

a two-dimensional scatterplot

Details

scoresPlot helps to visualise the result from the analyseBlocks function and gives a different result depending on whether 1) option = "rcpca" or 2) option = "pca") was used for analyseBlocks. 1) If option = "rpcca" was used for analyseBlocks then a scatterplot will be produced for the selected components from each individual block and for the consensus space. Axes will display the average variance explained by the components of the individual blocks and the consensus. Average variance explained by a selected component is presented as a proportion of the total variance. For detail about average variance explained see Tenenhaus and Guillemot (2017) and Tenenhaus et al. (2017). 2) If option = "pca" was selected for analyseBlocks then a scatterplot will be produced for the selected components from the analysis of the superblock. Axes will display the variance explained by the components of the superblock. Variance explained by a selected component is presented as a proportion of the total variance explained by all components.

References

Tenenhaus A, Guillemot V. 2017. RGCCA: Regularized and Sparse Generalized Canonical Correlation Analysis for multiblock data 2.1.2. https://CRAN.R-project.org/package=RGCCA.

Tenenhaus M, Tenenhaus A, Groenen PJF. 2017. Regularized Generalized Canonical Correlation Analysis: A framework for sequential multiblock component methods. Psychometrika 82: 737-777 https://doi.org/10.1007/s11336-017-9573-x

Examples

block1 <- dodecBlock()
block2 <- dodecBlock()
blocklist <- combineBlocks(blocks = c(block1, block2))
result1 <- analyseBlocks(blocklist)
result2 <- analyseBlocks(blocklist, option = "pca", ncomp = 10)
scoresPlot(result1)
dev.off()
#> null device 
#>           1 
scoresPlot(result2,
  comp = c(2, 3),
  pcol = colorRampPalette(c(rgb(1, 0.7, 0, 1), rgb(0, 0, 1, 1)),
  alpha = TRUE)(result2$n[1]))