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Detect outliers for selected traits and trials

Outlier detection in trial mean data is performed using a Bonferroni-Holm test to judge residuals standardized by the re-scaled Median Absolute Deviation (MAD). The “Outlier Threshold” refers to the threshold level used by the Bonferroni-Holm test in the detection of outliers. A lower threshold level report fewer outliers. As a default a commonly used threshold level of 0.05 is used. Outliers can be saved, then you will have the option of excluding these measurements while performing Analysis and Download functions.

Bernal-Vasquez AM, Utz HF, Piepho HP (2016). Outlier detection methods for generalized lattices: a case study on the transition from ANOVA to REML. Theor Appl Genet, 129:787-804. doi: 10.1007/s00122-016- 2666-6
Select a set of lines, trials, and traits.

In 2009 the Toronto International Data Release Workshop agreed on a policy statement about prepublication data sharing. Accordingly, the data producers are making many of the datasets in T3 available prior to publication of a global analysis. Guidelines for appropriate sharing of these data are given in the excerpt from the Toronto Statement.

I agree to the Data Usage Policy as specified in Toronto Statement.