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For the parent model, we fit the main field to spherical harmonic degree 20 and the secular variation to degree 12.
Including the variables described above in the model, we fit the following regression models to the data: Log e innoshare i + 1 = α + β 1 Innomode i + β 2 New ‐ to ‐ market i + β 3 Controls i + ε i (1).
To this model we fit two well-established conodont zonation schemes, thus allowing time estimates for conodont ranges for the Givetian, indicating a range from ~ 1.8 myr to ~ 100 kyr before extinction of an individual conodont species.
Finally, due to uncertainties in the Dst index baseline, which drives the POMME external field model, we fit an additional daily correction to the external field model represented as an external dipole oriented in the internal main field dipole direction, as well as a smaller-induced counterpart.
We attribute this to the phantasms acting like real signals to bring the total response to signal+phantasm above threshold, and this is the model we fit to the data in the continuous curves of Fig. 2.
The negative binomial model we fit requires at least two biological replicates.
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To select the best fitting model, we fitted parametric models with exponential, log-logistic, log normal and gamma distribution separately.
To confirm convergence of the best fitting regression model, we fitted these with two different sets of initial values and produced a Gelman-Rubin plot to check convergence.
In the first model we fitted a Weibull regression model to survival times which were interpolated from the interval counts.
The simplest model we fitted is given by Eq. 1: ACD_{plot} =a cdot TCH^{b} + epsilon, (1).
Using the dominant Mendelian model, we fitted several polygenic mixed models with a parameter for varying number of loci.
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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