Description Usage Arguments Details Value Examples
View source: R/tDistanceModel.R
Create a DistanceModel object describing the network structure of the population under study. The TDistanceModel function accepts lagged
1 2 3 4 5 6 7 | TDistanceModel(
distanceList,
laggedDistanceList,
scaleMode = c("none", "rowscale", "invsqrt"),
priorAlpha = 1,
priorBeta = 1
)
|
distanceList |
a list of square, symmetric distance matrices. |
laggedDistanceList |
a list of lists, of dimension T by k, where T is the number of time points and k is the number of time points being carried forward. |
scaleMode |
an optional argument specifying the type of preprocessing needed. |
priorAlpha |
the first shape parameter for the beta distributed autocorrelation terms |
priorBeta |
the second shape parameter for the beta distributed autocorrelation terms |
In stochastic spatial SEIR models as specified in Brown et al. 2015, populations are divided into homogeneous groups, or locations, with heterogeneous mixing between groups. This is accomplished using a distance matrix parameterization, in which some number of square, symmetric distance matrices are constructed, each of which receives a spatial autocorrelation parameter.
Care must be taken to specify reasonable prior parameters for such terms, as well as in the construction and scaling of the distance matrices; it is certainly possible to construct an overspecified model, and to correspondingly bias inference about other important exposure process terms.
an object of type TDistanceModel
1 | distanceModel <- DistanceModel(list(1-diag(4)))
|
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