Detailed description of the models, fitting procedures, numerical methods, and statistical analysis of the model fits is presented in the Supporting Material.
To this purpose a general model fitting procedure has been developed.
Then, a statistical formulation of general nonlinear structural equation analysis is introduced, and a general model fitting procedure applicable under weak assumptions on latent variable distributions is developed.
Results based on extensive testing and evaluation, show that the new procedure performs consistently well in fitting a set of training data as well as predicting a set of validation data, and outperforms a calibration procedure and an empirical model fitting procedure.
We tested the lag time between temperature, rainfall, and HFMD by including lag terms 1 2 and 3 4 weeks using a backward stepwise model fitting procedure.
We developed a maximum likelihood model fitting procedure to a collection of HIV-1 alignments sampled from different viral genes, and inferred two empirical substitution models, suitable for describing between-and within-host evolution.
As noted above, an important application of the Bayesian model fitting procedure we use is to produce better estimates of the parameters underlying LF transmission.
Our model fitting procedure is iterative in order to minimize confounding between the biological variation of interest and the other sources of variation.
Although the model fitting procedure we use is somewhat robust to these outliers, they can still affect the posterior distribution on the ordering, which manifests as uncertainty.
Of the other variables in our model (table 3), six occurred in three to seven of the best models, while cigarettes a day was not selected by the stepwise model fitting procedure.
