| smoothCluster {SUMMER} | R Documentation |
Cluster-level space-time smoothing models for mortality rates
Description
The function smoothCluster replace the previous function name fitINLA2 (before version 1.0.0).
Usage
smoothCluster(
data,
X = NULL,
family = c("betabinomial", "binomial")[1],
age.groups = c("0", "1-11", "12-23", "24-35", "36-47", "48-59"),
age.n = c(1, 11, 12, 12, 12, 12),
age.time.group = c(1, 2, 3, 3, 3, 3),
age.strata.fixed.group = c(1, 2, 3, 4, 5, 6),
time.model = c("rw1", "rw2", "ar1")[2],
st.time.model = NULL,
Amat,
bias.adj = NULL,
bias.adj.by = NULL,
formula = NULL,
year_label,
type.st = 4,
survey.effect = FALSE,
linear.trend = TRUE,
common.trend = FALSE,
strata.time.effect = FALSE,
hyper = "pc",
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,
pc.st.slope.u = NA,
pc.st.slope.alpha = NA,
overdisp.mean = 0,
overdisp.prec = 0.4,
options = list(config = TRUE),
control.inla = list(strategy = "adaptive", int.strategy = "auto"),
control.fixed = list(),
verbose = FALSE,
geo = NULL,
rw = NULL,
ar = NULL,
st.rw = NULL,
age.rw.group = NULL,
...
)
fitINLA2(
data,
X = NULL,
family = c("betabinomial", "binomial")[1],
age.groups = c("0", "1-11", "12-23", "24-35", "36-47", "48-59"),
age.n = c(1, 11, 12, 12, 12, 12),
age.time.group = c(1, 2, 3, 3, 3, 3),
age.strata.fixed.group = c(1, 2, 3, 4, 5, 6),
time.model = c("rw1", "rw2", "ar1")[2],
st.time.model = NULL,
Amat,
bias.adj = NULL,
bias.adj.by = NULL,
formula = NULL,
year_label,
type.st = 4,
survey.effect = FALSE,
linear.trend = TRUE,
common.trend = FALSE,
strata.time.effect = FALSE,
hyper = "pc",
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,
pc.st.slope.u = NA,
pc.st.slope.alpha = NA,
overdisp.mean = 0,
overdisp.prec = 0.4,
options = list(config = TRUE),
control.inla = list(strategy = "adaptive", int.strategy = "auto"),
control.fixed = list(),
verbose = FALSE,
geo = NULL,
rw = NULL,
ar = NULL,
st.rw = NULL,
age.rw.group = NULL,
...
)
Arguments
data |
count data of person-months with the following columns
|
X |
Covariate matrix. It must contain either a column with name "region", or a column with name "years", or both. The covariates must not have missing values for all regions (if varying in space) and all time periods (if varying in time). The rest of the columns are treated as covariates in the mean model. |
family |
family of the model. This can be either binomial (with logistic normal prior), betabiniomial. |
age.groups |
a character vector of age groups in increasing order. |
age.n |
number of months in each age groups in the same order. |
age.time.group |
vector indicating grouping of the ages groups in the temporal model. For example, if each age group is assigned a different temporal component, then set age.rw.group to c(1:length(age.groups)); if all age groups share the same random walk component, then set age.rw.group to a rep(1, length(age.groups)). The default for 6 age groups is c(1,2,3,3,3,3), which assigns a separate temporal trend to the first two groups and a common random walk for the rest of the age groups. The vector should contain values starting from 1. This argument replaces the previous |
age.strata.fixed.group |
vector indicating grouping of the ages groups for different strata in the intercept. The default is c(1:length(age.groups)), which correspond to each age group within each stratum receives a separate intercept. If several age groups are specified to be the same value in this vector, the stratum specific deviation from the baseline is assumed to be the same for these age groups. For example, if More specific examples: (1) if each age group is assigned a different intercept, then set age.strata.fixed.group to c(1:length(age.groups)) (2) if all age groups share the same intercept, then set age.strata.fixed.group to a rep(1, length(age.groups)). The default for 6 age groups is the former. (3) If each temporal trend is associated with its own intercept, set it to be the same as |
time.model |
Model for the main temporal trend, can be rw1, rw2, ar1, or NULL (for spatial-only smoothing). 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. Default to be the same as time.model unless specified otherwise. The default does not include region-specific random slopes. They can be added to the interaction term by specifying |
Amat |
Adjacency matrix for the regions |
bias.adj |
the ratio of unadjusted mortality rates or age-group-specific hazards to the true rates or hazards. It needs to be a data frame that can be merged to thee outcome, i.e., with the same column names for time periods (for national adjustment), or time periods and region (for subnational adjustment). The column specifying the adjustment ratio should be named "ratio". |
bias.adj.by |
vector of the column names specifying how to merge the bias adjustment to the count data. For example, if bias adjustment factor is provided in bias.adj for each region and time, then bias.adj.by should be 'c("region", "time")'. |
formula |
INLA formula. See vignette for example of using customized formula. |
year_label |
string vector of year names |
type.st |
type for space-time interaction |
survey.effect |
logical indicator whether to include a survey fixed effect. If this is set to TRUE, there needs to be a column named 'survey' in the input data frame. In prediction, this effect term will be set to 0. |
linear.trend |
logical indicator whether a linear trend is added to the temporal main effect. If the temporal main effect is RW2, then it will be forced to FALSE. Default is TRUE. |
common.trend |
logical indicator whether all age groups and strata share the same linear trend in the temporal main effect. |
strata.time.effect |
logical indicator whether to include strata specific temporal trends. |
hyper |
Deprecated. which hyperpriors to use. Only supports 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. |
pc.st.slope.u |
hyperparameter U for the PC prior on precisions for the area-level random slope. If both pc.st.slope.u and pc.st.slope.alpha are not NA, an area-level random slope with iid prior will be added to the model. The parameterization of the random slope is so that Prob(|beta| > pc.st.slope.u) = pc.st.slope.alpha, where time covariate is rescaled to be -0.5 to 0.5, so that the random slope can be interpreted as the total deviation from the main trend from the first year to the last year to be projected, on the logit scale. |
pc.st.slope.alpha |
hyperparameter alpha for the PC prior on precisions for the area-level random slope. See above for the parameterization. |
overdisp.mean |
hyperparameter for the betabinomial likelihood. Mean of the over-dispersion parameter on the logit scale. |
overdisp.prec |
hyperparameter for the betabinomial likelihood. Precision of the over-dispersion parameter on the logit scale. |
options |
list of options to be passed to control.compute() in the inla() function. |
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. Spatial polygon file, legacy parameter from previous versions of the package. |
rw |
Deprecated. Take values 0, 1 or 2, indicating the order of random walk. If rw = 0, the autoregressive process is used instead of the random walk in the main trend. See the description of the argument ar for details. |
ar |
Deprecated. Order of the autoregressive component. If ar is specified to be positive integer, the random walk components will be replaced by AR(p) terms in the interaction part. The main temporal trend remains to be random walk of order rw unless rw = 0. |
st.rw |
Deprecated. Take values 1 or 2, indicating the order of random walk for the interaction term. If not specified, it will take the same order as the argument rw in the main effect. Notice that this argument is only used if ar is set to 0. |
age.rw.group |
Deprecated. Legacy parameter replaced by |
... |
arguments to be passed to the inla() function call. |
Value
INLA model fit using the provided formula, country summary data, and geographic data
Author(s)
Zehang Richard Li
See Also
Examples
## Not run:
library(dplyr)
data(DemoData)
# Create dataset of counts
counts.all <- NULL
for(i in 1:length(DemoData)){
counts <- getCounts(DemoData[[i]][, c("clustid", "time", "age", "died",
"region", "strata")],
variables = 'died', by = c("age", "clustid", "region",
"time", "strata"))
counts <- counts %>% mutate(cluster = clustid, years = time, Y=died)
counts$strata <- gsub(".*\\.","",counts$strata)
counts$survey <- names(DemoData)[i]
counts.all <- rbind(counts.all, counts)
}
# fit cluster-level model on the periods
periods <- levels(DemoData[[1]]$time)
fit <- smoothCluster(data = counts.all,
Amat = DemoMap$Amat,
time.model = "rw2",
st.time.model = "rw1",
strata.time.effect = TRUE,
survey.effect = TRUE,
family = "betabinomial",
year_label = c(periods, "15-19"))
summary(fit)
est <- getSmoothed(fit, nsim = 1000)
plot(est$stratified, plot.CI=TRUE) + ggplot2::facet_wrap(~strata)
# fit cluster-level space-time model with covariate
# notice without projected covariates, we use periods up to 10-14 only
# construct a random covariate matrix for illustration
periods <- levels(DemoData[[1]]$time)
X <- expand.grid(years = periods,
region = unique(counts.all$region))
X$X1 <- rnorm(dim(X)[1])
X$X2 <- rnorm(dim(X)[1])
fit.covariate <- smoothCluster(data = counts.all,
X = X,
Amat = DemoMap$Amat,
time.model = "rw2",
st.time.model = "rw1",
strata.time.effect = TRUE,
survey.effect = TRUE,
family = "betabinomial",
year_label = c(periods))
est <- getSmoothed(fit.covariate, nsim = 1000)
# fit cluster-level model for one time point only
# i.e., space-only model
fit.sp <- smoothCluster(data = subset(counts.all, time == "10-14"),
Amat = DemoMap$Amat,
time.model = NULL,
survey.effect = TRUE,
family = "betabinomial")
summary(fit.sp)
est <- getSmoothed(fit.sp, nsim = 1000)
plot(est$stratified, plot.CI = TRUE) + ggplot2::facet_wrap(~strata)
# fit cluster-level model for one time point and covariate
# construct a random covariate matrix for illustration
X <- data.frame(region = unique(counts.all$region),
X1 = c(1, 2, 2, 1),
X2 = c(1, 1, 1, 2))
fit.sp.covariate <- smoothCluster(data = subset(counts.all, time == "10-14"),
X = X,
Amat = DemoMap$Amat,
time.model = NULL,
survey.effect = TRUE,
family = "betabinomial")
summary(fit.sp.covariate)
est <- getSmoothed(fit.sp.covariate, nsim = 1000)
## End(Not run)