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
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.