2 years that share at least two cultivars (because standards were present in most years for both mummy blight and fruit rot data sets, typically many cultivars were shared for randomly selected pairs of years). We use a principal components analysis
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Mark K. Ehlenfeldt, James J. Polashock, Allan W. Stretch and Matthew Kramer
Amy F. Iezzoni and Marvin P. Pritts
S. Pérez, S. Montes and C. Mejía
weight ratio; GH, growth habit; GP, germination period; HS, harvest season; La, leaf area; NW, nodes with zero buds: Nl b, nodes with one bud; N2b. nodes with two buds; N3b. nodes with three buds; P, productivity; PCA, principal component analysis: Prec
Esther Giraldo, Margarita López-Corrales and José Ignacio Hormaza
selected variable categories was also reduced by the Pearson correlation coefficient and by principal components analysis (PCA) ( Iezzoni and Pritts, 1991 ). These analyses were performed on the correlation matrix obtained with the frequencies, turning the
Yun-Peng Zhong, Zhi Li, Dan-Feng Bai, Xiu-Juan Qi, Jin-Yong Chen, Cui-Guo Wei, Miao-Miao Lin and Jin-Bao Fang
between a particular growth variable and drought tolerance was expressed by Eq. [ 1 ], whereas a negative correlation was expressed by Eq. [ 2 ]. Principal component analysis was used to extract the common factors associated with the cumulative variance
Jinwook Lee and Kenneth W. Mudge
ginsenoside Rg1 was positively and negatively correlated with root ginsenoside Rg1 and Re, respectively. Principal component analysis models were performed to provide the overall responses of all the ginsenosides to the given ginseng populations, depending on
Marjorie Reyes-Díaz, Claudio Inostroza-Blancheteau, Rayen Millaleo, Edgardo Cruces, Cristián Wulff-Zottele, Miren Alberdi and María de la Luz Mora
or/and biochemical profiles best characterized the leaf phenotypes of the cultivars with contrasting Al tolerance, we applied principal component analysis (PCA) ( Roessner et al., 2001 ; Taylor et al., 2002 ). Principal component analysis is a method
Winston Elibox and Pathmanathan Umaharan
the association between the parameters. Where a correlation was strong, linear regression analysis was performed to determine the nature of the relationship. The quantitative data for the 13 parameters were subjected to principal component analysis
Mohsen Hesami and Mostafa Rahmati-Joneidabad
religiosa L. According to Table 4 , PCA showed that the first two factor components explained 84.51% of the variation. Table 4. Principal component analysis of morphological traits. The first factor component had the positive relationship with leaf, tree
Timothy K. Broschat
being severe and 5 being completely free of deficiency symptoms for that element. Because plant size variables, such as height, width, and stem caliper, are typically highly intercorrelated, principal component analysis was performed on the data to
