Author Information

Andre Plante

Is the first presenter a student?

No

Type of Submission

Paper/Presentation

Abstract

For fixed-effect models one can always, according to the Gauss-Markov Theorem, uniquely determine independent variables called source identifiers, each corresponding to a source of variation. When linearly combined, source identifiers can generate all possible expected values for the response variable. The co-effect method uses regression of the response variable on source identifiers. Corresponding regression coefficients are, by definition, unbiased estimates of co-effects, and satisfy the same restrictions as those imposed on main effects and interaction effects in standard analysis of variance. with balanced data, co-effect analysis gives results identical to those of the standard method; with unbalanced data, however, results can be significantly different

.

An example is given where predicted genetic interaction can be easily observed using the co-effect method (Р≈10-14) while Yates' weighted-squares-of-means method does not detect any interaction effects (P>O.l)

.

Keywords

Unbalanced data, analysis of variance, interaction, co-effects, genetic experiment

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Apr 26th, 8:30 AM

CO-EFFECT ANALYSIS OF VARIANCE: A NEW METHOD FOR UNBALANCED DATA

For fixed-effect models one can always, according to the Gauss-Markov Theorem, uniquely determine independent variables called source identifiers, each corresponding to a source of variation. When linearly combined, source identifiers can generate all possible expected values for the response variable. The co-effect method uses regression of the response variable on source identifiers. Corresponding regression coefficients are, by definition, unbiased estimates of co-effects, and satisfy the same restrictions as those imposed on main effects and interaction effects in standard analysis of variance. with balanced data, co-effect analysis gives results identical to those of the standard method; with unbalanced data, however, results can be significantly different

.

An example is given where predicted genetic interaction can be easily observed using the co-effect method (Р≈10-14) while Yates' weighted-squares-of-means method does not detect any interaction effects (P>O.l)

.