Canonical correspondence analysis
In multivariate analysis, canonical correspondence analysis (CCA) is an ordination technique that determines axes from the response data as a linear combination of measured predictors. CCA is commonly used in ecology in order to extract gradients that drive the composition of ecological communities. CCA extends with regression, in order to incorporate predictor variables.
- Comment
- enIn multivariate analysis, canonical correspondence analysis (CCA) is an ordination technique that determines axes from the response data as a linear combination of measured predictors. CCA is commonly used in ecology in order to extract gradients that drive the composition of ecological communities. CCA extends with regression, in order to incorporate predictor variables.
- Has abstract
- enIn multivariate analysis, canonical correspondence analysis (CCA) is an ordination technique that determines axes from the response data as a linear combination of measured predictors. CCA is commonly used in ecology in order to extract gradients that drive the composition of ecological communities. CCA extends with regression, in order to incorporate predictor variables.
- Hypernym
- Multivariate
- Is primary topic of
- Canonical correspondence analysis
- Label
- enCanonical correspondence analysis
- Link from a Wikipage to another Wikipage
- Canonical correlation
- Category:Dimension reduction
- Correspondence Analysis (CA)
- Ecology
- Ordination (statistics)
- SameAs
- eNsQ
- m.0ynyrv8
- Q16851643
- Subject
- Category:Dimension reduction
- WasDerivedFrom
- Canonical correspondence analysis?oldid=1100880740&ns=0
- WikiPageLength
- 2889
- Wikipage page ID
- 40892755
- Wikipage revision ID
- 1100880740
- WikiPageUsesTemplate
- Template:Reflist
- Template:Statistics-stub