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Smoothing Levels or Trends over Geographic Areas

If this option is used, a proximity matrix, called proximity, to be used in smoothing over the geographic areas, must be stored in dataobj.

Hct.sigma

A scalar which sets $\sigma_t$, the prior standard deviation of $E(Y)$, which indicates how to smooth $E(Y)$ over geographic areas, or NA to not smooth in this way. The parameter $\sigma_ct$ is the expected prior standard deviation of $E(Y)$ for a geographic area (varying over time periods and age groups, and with the standard deviations averaged over geographic areas). (A larger standard deviation represents more prior uncertainty, which allows the data to play a greater role.) Default: 0.3.

Hct.sigma.sd

A scalar; the standard deviation of parameter Ht.sigma (for Gibbs sampling only). Default: 0.1.

Hct.t.deriv

A numeric vector; controls whether smoothing the level or the time trend of $E(Y)$ over geographic areas (both cannot presently be done simultaneously). To smooth the level of $E(Y)$ over geographic areas, set to 1, the identity. To smooth the time trend, set this (as in Hat.t.deriv) to the weight of the partial derivative taken with respect to time in the standard smoothness functional for the prior. The use of the first or higher order partial derivatives are supported. Default is 1.

Hct.time.weight

A scalar or a numeric vector with weights that determine how much smoothing occurs for different time periods when smoothing over geographic areas. If 0 or NA, time periods are weighted equally; if set to a nonzero scalar value, the weight for time period $t$ in smoothing over areas is proportional to $t^$Hct.time.weight; if the argument is a vector of length T, the $t$th element is the weight of time period $t$. Default: 0.