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An alternative measure of model fit involves the use of the log likelihood ratio of the intercept model.
The assessment of model fit involves an inspection of the factor loadings as well as an examination of 'fit statistics.' Each fit statistic provides information about the degree to which the model fits the observed data.
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The individual predictive ability of each mediator was ranked by using the corresponding p-values derived through the logistic model fit involving that sole mediator as input variable.
Then, the model fitting involves the estimation of topic specific visual word distributions and image specific latent topic distributions from the given database using Maximum Likelihood Estimation (MLE).
The deconvolution coupled with multi-exponential model fitting involve iterative optimization, which is both computationally expensive and time consuming.
Analyses of model fit mainly involved overall fit statistics or item fit statistics, and to a lesser extent the evaluation of person fit.
Assessing goodness of fit involves investigating how close values predicted by the model are to the observed values.
For the sound localizer a general linear model was fitted involving one regressor for the melody blocks and one regressor for the silence periods.
The recognition model with the best fit involved an additional projection from entorhinal cortex to prelimbic cortex.
In HP, the IVE of a variable is estimated by averaging the increase in model fit over all combinations involving that variable (see [4] for a more detailed explanation).
From a model fit to the data that involves both spin singlet and triplet PP dynamics, we obtained their effective recombination and spin-lattice relaxation rates.
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