Towards understanding and use of mixed-model analysis of agricultural experiments
Bibliographic record
Abstract
Despite the presence of both fixed and random effects in most agricultural experiments, many crop researchers have continued use of the conventional analysis of variance (ANOVA) model or general linear model (GLM) that provides a correct analysis only if all the effects are fixed. Ignoring or mistreating random effects may have inadvertently led to inappropriate analyses and thus to dubious conclusions appearing in the scientific literature. The objective of this paper is to provide a tutorial account of the mixed-model methodology and its applications to the analysis of agricultural experiments. The description and discussion on ANOVA vs. mixed-effect models center on the use of PROC GLM vs. PROC MIXED of the SAS ® System. This paper points out the need for mixed-model analysis, describes and discusses key new features and properties of mixed-model analysis that would facilitate the understanding and use of PROC MIXED. Additionally, it analyzes and interprets three examples: comparison between two samples, and analyses of randomized complete design and split-plot design. Appendices include SAS code and theory underlying mixed-model analysis which will help gain hands-on experiences and ensure correct interpretation of SAS outputs by PROC MIXED. Such a comparative assessment of GLM vs. MIXED procedures will help to underscore the key advantages of PROC MIXED and to convince GLM users to make a true transition towards the increased and appropriate use of PROC MIXED in agricultural experiments.Key words: Analysis of variance, fixed vs. random effects, general linear models, inference spaces, mixed models, randomized complete block design, split-plot design
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.175 | 0.252 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.005 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".