smoothCluster  R Documentation 
The function smoothCluster
replace the previous function name fitINLA2
(before version 1.0.0).
smoothCluster( data, X = NULL, family = c("betabinomial", "binomial")[1], age.groups = c("0", "111", "1223", "2435", "3647", "4859"), age.n = c(1, 11, 12, 12, 12, 12), age.rw.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, ... ) fitINLA2( data, X = NULL, family = c("betabinomial", "binomial")[1], age.groups = c("0", "111", "1223", "2435", "3647", "4859"), age.n = c(1, 11, 12, 12, 12, 12), age.rw.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, ... )
data 
count data of personmonths 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.rw.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. 
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 spatialonly 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 regionspecific 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 agegroupspecific 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 spacetime 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 arealevel random slope. If both pc.st.slope.u and pc.st.slope.alpha are not NA, an arealevel 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 arealevel random slope. See above for the parameterization. 
overdisp.mean 
hyperparameter for the betabinomial likelihood. Mean of the overdispersion parameter on the logit scale. 
overdisp.prec 
hyperparameter for the betabinomial likelihood. Precision of the overdispersion 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. 
... 
arguments to be passed to the inla() function call. 
INLA model fit using the provided formula, country summary data, and geographic data
Zehang Richard Li
getDirect
## 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$survey < names(DemoData)[i] counts.all < rbind(counts.all, counts) } # fit clusterlevel model on the periods periods < levels(DemoData[[1]]$time) fit < smoothCluster(data = counts.all, Amat = DemoMap$Amat, time.model = "rw2", st.time.model = "rw1", strata.time.effect = TRUE, survey.effect = TRUE, family = "betabinomial", year_label = c(periods, "1519")) summary(fit) est < getSmoothed(fit, nsim = 1000) plot(est$stratified, plot.CI=TRUE) + ggplot2::facet_wrap(~strata) # fit clusterlevel spacetime model with covariate # notice without projected covariates, we use periods up to 1014 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$Amat, time.model = "rw2", st.time.model = "rw1", strata.time.effect = TRUE, survey.effect = TRUE, family = "betabinomial", year_label = c(periods)) est < getSmoothed(fit.covariate, nsim = 1000) # fit clusterlevel model for one time point only # i.e., spaceonly model fit.sp < smoothCluster(data = subset(counts.all, time == "1014"), Amat = DemoMap$Amat, time.model = NULL, survey.effect = TRUE, family = "betabinomial") summary(fit.sp) est < getSmoothed(fit.sp, nsim = 1000) plot(est$stratified, plot.CI = TRUE) + ggplot2::facet_wrap(~strata) # fit clusterlevel model for one time point and covariate # construct a random covariate matrix for illustration X < data.frame(region = unique(counts.all$region), X1 = c(1, 2, 2, 1), X2 = c(1, 1, 1, 2)) fit.sp.covariate < smoothCluster(data = subset(counts.all, time == "1014"), 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)
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