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Table 6 shows the predictive ability for grain yield across environments measured as a Pearson correlation between the observed value (y) and the predicted value averaged over all 50 validation runs for each of the different combinations of number of individuals and number of markers for two maize bi-parental populations.
To find the difference between the observed value (diameter, volume and density) and the actual value, a one-sample t-test was used.
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Residual values obtained between the observed values by experiments and predicted values by the model are very low, and this result showed that the experimental results were well in consistence with the calculation results via the model.
Furthermore, it computes a weighted value in accordance with the distance between the observed values.
IGn is obtained by numerically solving Eqn (1) with τ as an unknown parameter to be estimated and BG given by linearly interpolating between the observed values.
In this way, each parameter is changed systematically and the MAPE values between the field observed values and the simulated values are compared, and finally, those parameters with least MAPE value are considered as calibrated values.
The agreement between predicted and observed value was tested by Cohen Kappa's coefficient.
The high correlation between the predicted and observed values indicated the validity of the model.
The standard deviation of the differences between the estimated and observed values for all the points is computed.
We observe high prediction accuracy, measured as the Pearson correlation coefficient between the predicted and observed values, standardized with the square root of the heritability (h = 0.78), amounting to 0.6 for the performance across environments.
Then, we retained 500 (0.5%) datasets with the smallest Euclidean distance between the simulated and observed values of standardised incidence rates.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com