= ”names”) (SCA loadings in 2 dimensions).
Figure 11.5 Output from use of plot(can.statis$statis) (STATIS summary plot).
Figure 11.6 Output from use of scoreplot() (ASCA scores in 2 dimensions).
Figure 11.7 Output from use of scoreplot() (ASCA scores in 1 dimension).
Figure 11.8 Output from use of loadingplot() (ASCA scores in 2 dimensions).
Figure 11.9 Output from use of scoreplot() (block-scores).
Figure 11.10 Output from use of loadingplot() (block-loadings).
Figure 11.11 Output from use of scoreplot() andloadingweightplot() on an object from sMB-PLS.
Figure 11.12 Output from use of maage().
Figure 11.13 Output from use of maageSeq().
Figure 11.14 Output from use of loadingplot() on an sopls object.
Figure 11.15 Output from use of scoreplot() on an sopls object.
Figure 11.16 Output from use of scoreplot() on a pcp object.
Figure 11.17 Output from use of plot() on a cvanova object.
Figure 11.18 Output from use of scoreplot() on a popls object.
Figure 11.19 Output from use of loadingplot() on a popls object.
Figure 11.20 Output from use of loadingplot() on a rosa object.
Figure 11.21 Output from use of scoreplot() on a rosa object.
Figure 11.22 Output from use of image() on a rosa object.
Figure 11.23 Output from use of image() withparameter ”residual” on a rosa object.
Figure 11.24 Output from use of scoreplot() on an mbrda object.
Figure 11.25 Output from use of plot() on an lpls object.Correlation loadings from blocks are coloured andoverlaid each other to visualise relations across blocks.
List of Tables
Table 1.2 Abbreviations of the different methods.
Table 2.2 Different methods for fusing two data blocks, indicating the properties in terms of explained variation within and between the blocks. Thelast two columns refer to whether the methods favour explaining within- or between-block variation. For more explanation, see text.
Table 2.3 The matrices of which the weights w are eigenvectorsin its original form and using the SVDs of X and Y.
Table 4.1 Overview of the data sets used in the genomics example.
Table 5.1 Overview of methods. Legend: U=unsupervised,S=supervised, C=complex, HOM=homogeneous data,HET=heterogeneous data, SEQ=sequential, SIM=simultaneous, MOD=model-based, ALG= algorithm-based, C=common,CD=common/distinct, CLD=common/local/distinct, LS=least squares, ML=maximum likelihood, ED=eigendecomposition,MC=maximising correlations/covariances. Forabbreviations of the methods, see Section 1.11.
Table 5.2 Different types of SCA, where Dm is a diagonal matrixand Φ is a positive definite matrix (see Section 2.8). The correlations and variances pertain to the block-scores (see text).
Table 5.3 Proportions of explained variance per component (C1, C2,…)and total in each of the blocks for the two different methods. Legend: conc is the abbreviation of concatenated; yellow is distinct for TIV; red is distinct for LAIV; green is common (see text).
Table 5.4 Properties of methods for common and distinctcomponents. The matrix D indicates a diagonalmatrix with all positive elements on its diagonal.
Table 6.1 Overview of methods. Legend: U=unsupervised,S=supervised, C=complex, HOM=homogeneous data,HET=heterogeneous data, SEQ=sequential, SIM=simultaneous, MOD=model-based, ALG= algorithm-based, C=common,CD=common/distinct, CLD=common/local/distinct, LS=least squares, ML=maximum likelihood, ED=eigendecomposition, MC=maximising correlations/covariances. Forabbreviations of the methods, see Section 1.11.
Table 7.1 Overview of methods. Legend: U=unsupervised,S=supervised, C=complex, HOM=homogeneous data,HET=heterogeneous data, SEQ=sequential, SIM=simultaneous, MOD=model-based, ALG= algorithm-based, C=common,CD=common/distinct, CLD=common/local/distinct, LS=least squares, ML=maximum likelihood, ED=eigendecomposition, MC=maximising correlations/covariances. Forabbreviations of the methods, see Section 1.11.
Table 8.1 Overview of methods. Legend: U=unsupervised, S=supervised, C=complex, HOM=homogeneous data, HET=heterogeneousdata, SEQ=sequential, SIM=simultaneous, MOD=model-based,ALG= algorithm-based, C=common, CD=common/distinct,CLD=common/local/distinct, LS=least squares, ML=maximum likelihood, ED=eigendecomposition, MC=maximising correla-tions/covariances. The green colour indicates that this methodis discussed extensively in this chapter. The abbreviations forthe methods represent the different sections and follow thesame order. For abbreviations of the methods, see Section 1.11.
Table 8.2 Tabulation of consumer characteristics. A selection of two consumer attributes/characteristics, gender, and lunch habitsis given. The numbers represent percentages in each of the categories for each of the segments (subgroups). The sumsin each column for each consumer characteristic variable isequal to 100. The lunch variable reflects the frequency of usewith 1 representing the highest frequency and 5 ‘no answer’.Source: (Helgesen et al., 1997). Repro-duced with permission from Elsevier.
Table 8.3 Consumer liking of cheese. Design of the conjointexperiment based on six design factors. Source: (Almli et al., 2011). Reproduced with permission from Elsevier.
Table 9.1 Overview of methods. Legend: U=unsupervised,S=supervised, C=complex, HOM=homogeneous data,HET=heterogeneous data, SEQ=sequential, SIM=simultaneous, MOD=model-based, ALG= algorithm-based, C=common, CD=common/distinct, CLD=common/local/distinct, LS=least squares, ML=maximum likelihood, ED=eigendecomposition, MC=maximising correlations/covariances. For abbreviations of the methods, see Section 1.11.
Table 10.1 Overview of methods. Legend: U=unsupervised, S=supervised, C=complex, HOM=homogeneous data, HET=heterogeneous data, SEQ=sequential, SIM=simultaneous, MOD=model-based, ALG= algorithm-based, C=common, CD=common/distinct,
