ANOVA–simultaneous component analysis
Analysis of variance – simultaneous component analysis (ASCA or ANOVA–SCA) is a method that partitions variation and enables interpretation of these partitions by SCA, a method that is similar to principal components analysis (PCA). This method is a multivariate or even megavariate extension of analysis of variance (ANOVA). The variation partitioning is similar to ANOVA. Each partition matches all variation induced by an effect or factor, usually a treatment regime or experimental condition. The calculated effect partitions are called effect estimates. Because even the effect estimates are multivariate, interpretation of these effects estimates is not intuitive. By applying SCA on the effect estimates one gets a simple interpretable result.In case of more than one effect this method estima
Wikipage redirect
primaryTopic
ANOVA–simultaneous component analysis
Analysis of variance – simultaneous component analysis (ASCA or ANOVA–SCA) is a method that partitions variation and enables interpretation of these partitions by SCA, a method that is similar to principal components analysis (PCA). This method is a multivariate or even megavariate extension of analysis of variance (ANOVA). The variation partitioning is similar to ANOVA. Each partition matches all variation induced by an effect or factor, usually a treatment regime or experimental condition. The calculated effect partitions are called effect estimates. Because even the effect estimates are multivariate, interpretation of these effects estimates is not intuitive. By applying SCA on the effect estimates one gets a simple interpretable result.In case of more than one effect this method estima
has abstract
Analysis of variance – simulta ...... nt effects are not correlated.
@en
Wikipage page ID
13,371,195
Wikipage revision ID
641,228,736
hypernym
type
comment
Analysis of variance – simulta ...... one effect this method estima
@en
label
ANOVA–simultaneous component analysis
@en