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First, in our multilevel modelling we fitted a random coefficient model where we allowed the coefficients for employment status to vary over the years (models allowing all variables (age, socio-economic characteristics and employment status) to vary in the multilevel model unfortunately did not converge).
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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.
To test the natural relevance of this model, we fitted such a mixture of Gaussians to each of a sample of pixels from hyperspectral images of natural scenes.
For each model, we fitted year of birth and sex as fixed effects.
For the eight-compartment model, we fitted only the two most sensitive parameters (out of the 29) for computational reasons.
The logistical regression model we fitted in this region can be further applied to predict genome-wide GATA1 occupancy.
To improve the statistical properties of the regression models, we fitted all models to the natural logarithm of the reported risk estimates (Berlin et al. 1993).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com