SUMMER (version 2.0.0)

smoothDirect: Smoothed direct estimates for mortality rates

Description

The function smoothDirect replaces the previous function name fitINLA (before version 1.0.0).

Usage

smoothDirect(
  data,
  Amat,
  formula = NULL,
  time.model = c("rw1", "rw2", "ar1")[2],
  st.time.model = NULL,
  year.label,
  year_label = deprecated(),
  year.range = c(1980, 2014),
  year_range = deprecated(),
  is.yearly = TRUE,
  m = 5,
  type.st = 1,
  survey.effect = FALSE,
  hyper = c("pc", "gamma")[1],
  pc.u = 1,
  pc.alpha = 0.01,
  pc.u.phi = 0.5,
  pc.alpha.phi = 2/3,
  pc.u.cor = 0.7,
  pc.alpha.cor = 0.9,
  pc.st.u = NA,
  pc.st.alpha = NA,
  control.compute = list(dic = TRUE, mlik = TRUE, cpo = TRUE, openmp.strategy =
    "default", config = TRUE),
  control.inla = list(strategy = "adaptive", int.strategy = "auto"),
  control.fixed = list(),
  verbose = FALSE,
  geo = NULL,
  rw = NULL,
  ar = NULL,
  options = NULL
)

Value

List of fitted object

Arguments

data

Combined dataset

Amat

Adjacency matrix for the regions

formula

INLA formula. See vignette for example of using customized formula.

time.model

Model for the main temporal trend, can be rw1, rw2, or ar1. ar1 is not implemented for yearly model with period data input. Default to be rw2. For ar1 main effect, a linear slope is also added with time scaled to be between -0.5 to 0.5, i.e., the slope coefficient represents the total change between the first year and the last year in the projection period on the logit scale.

st.time.model

Temporal component model for the interaction term, can be rw1, rw2, or ar1. ar1 is not implemented for yearly model with period data input. Default to be the same as time.model unless specified otherwise. For ar1 interaction model, region-specific random slopes are currently not implemented.

year.label

string vector of year names

year_label

replaced by year.label

year.range

Entire range of the years (inclusive) defined in year.label.

year_range

replaced by year.range

is.yearly

Logical indicator for fitting yearly or period model.

m

Number of years in each period.

type.st

type for space-time interaction

survey.effect

logical indicator whether to include a survey iid random effect. If this is set to TRUE, there needs to be a column named 'survey' in the input data frame. In prediction, this random effect term will be set to 0. Notice this survey effect is implemented according to the Merter et al. (2015) model, and differently compared to the smoothCluster() function.

hyper

which hyperpriors to use. Default to be using the PC prior ("pc").

pc.u

hyperparameter U for the PC prior on precisions.

pc.alpha

hyperparameter alpha for the PC prior on precisions.

pc.u.phi

hyperparameter U for the PC prior on the mixture probability phi in BYM2 model.

pc.alpha.phi

hyperparameter alpha for the PC prior on the mixture probability phi in BYM2 model.

pc.u.cor

hyperparameter U for the PC prior on the autocorrelation parameter in the AR prior, i.e. Prob(cor > pc.u.cor) = pc.alpha.cor.

pc.alpha.cor

hyperparameter alpha for the PC prior on the autocorrelation parameter in the AR prior.

pc.st.u

hyperparameter U for the PC prior on precisions for the interaction term.

pc.st.alpha

hyperparameter alpha for the PC prior on precisions for the interaction term.

control.compute

list of options to be passed to control.compute() in the inla() function. The default argument saves the internal objects created by INLA for posterior sampling later. If the fitted object is too large in size and there is no need to perform joint posterior sampling from the model (only used in benchmarking), this argument can be set to control.compute = list(config = FALSE) to reduce the size of the fitted object.

control.inla

list of options to be passed to control.inla() in the inla() function. Default to the "adaptive" integration strategy.

control.fixed

list of options to be passed to control.fixed() in the inla() function.

verbose

logical indicator to print out detailed inla() intermediate steps.

geo

Deprecated.

rw

Deprecated.

ar

Deprecated.

options

Deprecated.

Author

Zehang Richard Li

References

Li, Z., Hsiao, Y., Godwin, J., Martin, B. D., Wakefield, J., Clark, S. J., & with support from the United Nations Inter-agency Group for Child Mortality Estimation and its technical advisory group. (2019). Changes in the spatial distribution of the under-five mortality rate: Small-area analysis of 122 DHS surveys in 262 subregions of 35 countries in Africa. PloS one, 14(1), e0210645.

Mercer, L. D., Wakefield, J., Pantazis, A., Lutambi, A. M., Masanja, H., & Clark, S. (2015). Space-time smoothing of complex survey data: small area estimation for child mortality. The annals of applied statistics, 9(4), 1889.

See Also

getDirect

Examples

if (FALSE) {
  years <- levels(DemoData[[1]]$time)
  # obtain direct estimates
  data_multi <- getDirectList(births = DemoData, years = years,
  regionVar = "region",  timeVar = "time", clusterVar = "~clustid+id",
  ageVar = "age", weightsVar = "weights", geo.recode = NULL)
  data <- aggregateSurvey(data_multi)
  
  #  national model
  years.all <- c(years, "15-19")
  fit1 <- smoothDirect(data = data, Amat = NULL, 
  year.label = years.all, year.range = c(1985, 2019), 
  time.model = 'rw2', m = 5, control.compute = list(config =TRUE))
  out1 <- getSmoothed(fit1)
  plot(out1)
  
  #  subnational model
  fit2 <- smoothDirect(data = data, Amat = DemoMap$Amat, 
  year.label = years.all, year.range = c(1985, 2019), 
  time.model = 'rw2', m = 5, type.st = 4)
  out2 <- getSmoothed(fit2)
  plot(out2)
  
  #  subnational space-only model for one period
  fit3 <- smoothDirect(data = subset(data, years == "10-14"), 
           time.model = NULL, Amat = DemoMap$Amat)
  out3 <- getSmoothed(fit3)
  plot(out3, plot.CI = TRUE)
}