Type C: A family of regional models for the regions shown in Figure 1 consisting of the same model forms used for Type A but with separate parameters estimated for each region.
The spatial SUR (SSUR) model allows for spatially correlated error terms within each equation and across equations, with separate parameter estimates for each time period.
Summary volumes for each block were calculated using SPM2 (Wellcome Department of Imaging Neuroscience, London, UK; http://www.fil.ion.ucl.ac.uk/spm) by fitting a GLM with separate parameter estimates for each block.
Each sample was simulated with separate parameter settings estimated from one of the four real human ENCODE samples (e.g. the simulation parameters for A Rep 1 were estimated from HUVEC Rep 1).
Type C regional models were obtained by fitting anamorphic, common-asymptote, and a general polymorphic form of each growth function, with separate parameters for each region.
The model parameters for Equations 4 and 5 were estimated so as to minimise the sum of squares of differences between predicted and measured values, with separate parameters being obtained for each block × species/cross combination.
The same model is shown with separate parameters for the HCI and no access to piped water inside the home to indicate the direction of the effects of the parameters in the model.
Testing a common VAR model for all patients against a VAR model with separate parameters for each subgroup, we found a significant difference in the structure of temporal relationships between the two subgroups (likelihood ratio test: T = 61.04, p = 0.006).
To investigate the pattern and rate of limb reduction within Lerista, we reconstructed numbers of digits for internal nodes assuming a Markov model of character evolution with separate parameters describing instantaneous rates of digit gain and loss.
Firstly, in order to determine whether there is a significant overall difference in the temporal relationships between the two subgroups, a common VAR model for all subjects is tested against a VAR model with separate parameters for each group using a likelihood ratio test.
