Yield-Component Path Analysis Calculator
Yield-component analysis with correlation matrix and Wright-Dewey-Lu path coefficients, online and free.
When to use it
Use when you have replicated breeding or agronomy data and want to decompose the correlations of yield with its components (panicles, grains per panicle, 1000-grain weight, etc.) into direct and indirect effects. Standard analysis in Singh and Chaudhary (1979) and the rice / wheat breeding literature.
When NOT to use it
Do not use when components are not pre-specified by biology; this is not exploratory. Do not use when component variables are highly collinear (path coefficients become unstable; check the determinant of the correlation matrix). For a path on genotypic, phenotypic and environmental correlations from a multi-trait analysis, use the path-coefficient tool instead.
What you get
Pearson correlation matrix among yield and components; standardised regression (path) coefficients = direct effects; indirect-effect breakdown of each component's correlation with yield (sum of direct + indirect via every other component); R-squared from the path model; residual effect (sqrt of unexplained variance).
Worked example
Wheat trial with grain yield (t/ha) and three components: tillers, grains per spike, 1000-grain weight (g).
Yield: 25.4, 27.1, 26.8, 24.9, 31.2, 29.8, 30.1, 28.5 Tillers: 12, 15, 14, 11, 18, 16, 17, 14 GrainsPerSpike: 32, 36, 34, 30, 40, 38, 39, 35 ThousandGrainWt: 28, 30, 29, 27, 33, 32, 31, 30
Expected output: All three components show large positive direct effects on yield; R squared close to 1 confirms the components account for nearly all yield variation.
Source: StatVeda built-in example, paired with the analyse page sample for toolId 'yieldcomp'.
How to interpret the output
Direct effect of component X on yield is the standardised partial regression coefficient holding other components constant. Indirect effect via Y is the path coefficient on Y times the correlation of X with Y. Their sum equals the total Pearson correlation of X with yield. A small total correlation can mask offsetting positive and negative indirect effects, which is the central insight of path analysis (Wright 1921).
Common pitfalls
- Selecting components after the fact based on which gave a large direct effect.
- Ignoring multicollinearity warnings in the correlation matrix determinant.
- Reporting only direct effects; indirect effects often dominate the picture.
Try it in StatVeda
The Yield-Component Analysis (correlation + path coefficients) engine runs entirely in the browser. No signup, no install, no data sent to a server. Paste your data, hit Run, copy the output.
Open Yield-Component Analysis (correlation + path coefficients)References
- Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557 to 585.
- Dewey, D. R. and Lu, K. H. (1959). A correlation and path-coefficient analysis of components of crested wheatgrass seed production. Agronomy Journal, 51(9), 515 to 518.
- Singh, R. K. and Chaudhary, B. D. (1979). Biometrical Methods in Quantitative Genetic Analysis. Kalyani Publishers, New Delhi. Chapter 5.