Index
A
Adjusted R
2
, see R-square (R
2 ), adjusted
AEM, see Asymmetric eigenvector maps (AEM)
Agglomerative, see Clustering, agglomerative
Akaike information criterion (AIC), 228, 335,
339, 340
Alpha diversity, see Diversity, alpha
Analysis of variance (ANOVA), 68, 107–111,
216, 221, 237–244, 249–251, 256,
361–367
Arch effect, 182, 183
Association, 3, 35–57, 59, 60, 63, 70, 104, 107,
117, 123, 125, 126, 135, 152, 153,
187, 188, 193, 385
Association matrix, see Matrix, association
Asymmetrical, 36–40, 50, 55, 172, 175, 203, 204,
250, 268–275, 283, 297, 348–355
Asymmetric eigenvector maps (AEM), 348–355
Autocorrelation, 301, 303–307, 358, 360, 365
B
Backward selection, see Selection, backward
Baselga, 389, 392–395, 404
Beta diversity, see Diversity, beta
Binary matrix, see Matrix, binary
Binary variable, see Variable, binary
Biplot, 156, 157, 159–165, 167–170, 176–180,
187–191, 194, 210, 212, 217, 260,
274, 276, 277, 295, 296, 328, 403
Bonferroni, see Correction, Bonferroni
Bootstrap, 79, 80, 126, 256, 261, 382
Box-Cox, see Transformation, Box-Cox
Bray-Curtis, see Dissimilarity, Bray-Curtis
Broad scale, see Scale, broad
C
CA, see Correspondence analysis (CA)
Cailliez, see Correction, Cailliez
Calibration, 232, 271
Calinski-Harabasz, 98, 113
Canonical correlation analysis (CCorA), 203,
204, 274–277, 297
Canonical correspondence analysis (CCA), 203,
204, 216, 226, 228, 256–263, 272,
275, 277, 283, 297, 310, 355, 356, 360
Categorical variable, see Variable, categorical
Centring, 21, 175
Chain of primary connections, 62
Chi-square distance, see Distance, chi-square
Chi-square transformation, see Transformation,
chi-square
Chord distance, see Distance, chord
Chord transformation, see Transformation, chord
Clustering
agglomerative, 61–67, 97
c-means, 141–145
complete linkage, 64, 65, 75, 78
constrained, 59–61, 126–135
flexible, 69, 70
fuzzy, 61, 141–150
hierarchical, 61–65, 70–97, 100, 184, 410
minimum variance, 68, 69, 138
monothetic, 60, 135–138
noise, 146–150
non-hierarchical, 96–106
polythetic, 60
single linkage, 62–65
unconstrained, 60, 70
UPGMA, 65, 66
UPGMC, 66, 67
© Springer International Publishing AG, part of Springer Nature 2018
D. Borcard et al., Numerical Ecology with R, Use R!,
https://doi.org/10.1007/978-3-319-71404-2
427
A
Adjusted R
2
, see R-square (R
2 ), adjusted
AEM, see Asymmetric eigenvector maps (AEM)
Agglomerative, see Clustering, agglomerative
Akaike information criterion (AIC), 228, 335,
339, 340
Alpha diversity, see Diversity, alpha
Analysis of variance (ANOVA), 68, 107–111,
216, 221, 237–244, 249–251, 256,
361–367
Arch effect, 182, 183
Association, 3, 35–57, 59, 60, 63, 70, 104, 107,
117, 123, 125, 126, 135, 152, 153,
187, 188, 193, 385
Association matrix, see Matrix, association
Asymmetrical, 36–40, 50, 55, 172, 175, 203, 204,
250, 268–275, 283, 297, 348–355
Asymmetric eigenvector maps (AEM), 348–355
Autocorrelation, 301, 303–307, 358, 360, 365
B
Backward selection, see Selection, backward
Baselga, 389, 392–395, 404
Beta diversity, see Diversity, beta
Binary matrix, see Matrix, binary
Binary variable, see Variable, binary
Biplot, 156, 157, 159–165, 167–170, 176–180,
187–191, 194, 210, 212, 217, 260,
274, 276, 277, 295, 296, 328, 403
Bonferroni, see Correction, Bonferroni
Bootstrap, 79, 80, 126, 256, 261, 382
Box-Cox, see Transformation, Box-Cox
Bray-Curtis, see Dissimilarity, Bray-Curtis
Broad scale, see Scale, broad
C
CA, see Correspondence analysis (CA)
Cailliez, see Correction, Cailliez
Calibration, 232, 271
Calinski-Harabasz, 98, 113
Canonical correlation analysis (CCorA), 203,
204, 274–277, 297
Canonical correspondence analysis (CCA), 203,
204, 216, 226, 228, 256–263, 272,
275, 277, 283, 297, 310, 355, 356, 360
Categorical variable, see Variable, categorical
Centring, 21, 175
Chain of primary connections, 62
Chi-square distance, see Distance, chi-square
Chi-square transformation, see Transformation,
chi-square
Chord distance, see Distance, chord
Chord transformation, see Transformation, chord
Clustering
agglomerative, 61–67, 97
c-means, 141–145
complete linkage, 64, 65, 75, 78
constrained, 59–61, 126–135
flexible, 69, 70
fuzzy, 61, 141–150
hierarchical, 61–65, 70–97, 100, 184, 410
minimum variance, 68, 69, 138
monothetic, 60, 135–138
noise, 146–150
non-hierarchical, 96–106
polythetic, 60
single linkage, 62–65
unconstrained, 60, 70
UPGMA, 65, 66
UPGMC, 66, 67
© Springer International Publishing AG, part of Springer Nature 2018
D. Borcard et al., Numerical Ecology with R, Use R!,
https://doi.org/10.1007/978-3-319-71404-2
427