R/getDiag.R
In martinbryan/SUMMER: Small-Area-Estimation Unit/Area Models and Methods for Estimation in R
Defines functions
getDiag
Documented in
getDiag
#' Extract posterior summaries of random effects
#'
#' @param inla_mod output from \code{\link{smoothDirect}} or \code{\link{smoothCluster}}
#' @param field which random effects to plot. It can be one of the following: space, time, and spacetime.
#' @param CI Desired level of credible intervals
#' @param draws Posterior samples drawn from the fitted model. This argument allows the previously sampled draws (by setting save.draws to be TRUE) be used in new aggregation tasks.
#' @param nsim number of simulations, only applicable for the cluster-level model space-time interaction terms when random slopes are included.
#' @param ... Unused arguments, for users with fitted object from the package before v1.0.0, arguments including Amat, year_label, and year_range can still be specified manually.
#'
#' @return List of diagnostic plots
#' @author Zehang Richard Li
#' @examples
#' \dontrun{
#' data(DemoMap)
#' years <- levels(DemoData[[1]]$time)
#'
#' # obtain direct estimates
#' data <- getDirectList(births = DemoData,
#' years = years,
#' regionVar = "region", timeVar = "time",
#' clusterVar = "~clustid+id",
#' ageVar = "age", weightsVar = "weights",
#' geo.recode = NULL)
#' # obtain direct estimates
#' data_multi <- getDirectList(births = DemoData, years = years,
#' regionVar = "region", timeVar = "time", clusterVar = "~clustid+id",
#' ageVar = "age", weightsVar = "weights", geo.recode = NULL)
#' data <- aggregateSurvey(data_multi)
#'
#' # national model
#' years.all <- c(years, "15-19")
#' fit1 <- smoothDirect(data = data, geo = DemoMap$geo, Amat = DemoMap$Amat,
#' year_label = years.all, year_range = c(1985, 2019),
#' rw = 2, is.yearly=FALSE, m = 5)
#' random.time <- getDiag(fit1, field = "time")
#' random.space <- getDiag(fit1, field = "space")
#' random.spacetime <- getDiag(fit1, field = "spacetime")
#' }
#'
#' @export
getDiag <- function(inla_mod, field = c("space", "time", "spacetime")[1], CI = 0.95, draws = NULL, nsim = 1000, ...){
lower <- (1 - CI) / 2
upper <- 1 - lower
if(!is.null(inla_mod$year_range)){
year_range <- inla_mod$year_range
}else{
warning("The fitted object was from an old version of SUMMER, please specify 'year_range' argument when calling getDiag()")
}
if(!is.null(inla_mod$year_label)){
year_label <- inla_mod$year_label
}else{
warning("The fitted object was from an old version of SUMMER, please specify 'year_label' argument when calling getDiag()")
}
if(!is.null(inla_mod$has.Amat)){
Amat <- inla_mod$Amat
}else{
warning("The fitted object was from an old version of SUMMER, please specify 'Amat' argument when calling getDiag()")
}
getquants <- function(mlist, lower, upper){
quants <- data.frame(matrix(NA, length(mlist), 3))
for(i in 1:length(mlist)){
quants[i, ] <- INLA::inla.qmarginal(c(lower, 0.5, upper), mlist[[i]])
}
colnames(quants) <- c("lower", "median", "upper")
return(quants)
}
is.yearly = inla_mod$is.yearly
has.slope <- sum(grepl("slope", rownames(inla_mod$fit$summary.fixed)))
if(field == "time" && has.slope == 0){
struct <- inla_mod$fit$marginals.random$time.struct
unstruct <- inla_mod$fit$marginals.random$time.unstruct
if(is.yearly){
label <- label.unstruct <- c(year_range[1] : year_range[2], year_label)
}else{
label <- label.unstruct <- year_label
}
if(!is.null(inla_mod$age.rw.group)){
group <- rep(inla_mod$age.groups, each = length(label))
label <- rep(label, length(inla_mod$age.rw.group))
}
expand <- 1
if(!is.null(inla_mod$age.rw.group)) expand <- max(inla_mod$age.rw.group) / length(inla_mod$age.rw.group)
if(length(struct) != length(label) * expand) stop("The input year_range or year_label does not match the fitted model. Please double check.")
temp <- getquants(struct, lower = lower, upper = upper)
quants <- NULL
if(!is.null(inla_mod$age.rw.group)){
for(i in 1:length(inla_mod$age.groups)){
where <- (inla_mod$age.rw.group[i] - 1) * length(year_label) + c(1:length(year_label))
quants <- rbind(quants, temp[where, ])
}
}else{
quants <- getquants(struct, lower = lower, upper = upper)
}
n <- dim(quants)[1]
m <- length(unstruct)
quants <- rbind(quants, getquants(unstruct, lower = lower, upper = upper))
quants$years <- c(label, label.unstruct)
if(!is.null(inla_mod$age.rw.group)){
quants$group <- c(group, rep("IID", m))
}
quants$years.num <- suppressWarnings(as.numeric(as.character(quants$years)))
quants$label <- c(rep("Structured", n), rep("IID", m))
quants$is.yearly <- !(quants$years %in% year_label)
}else if(field == "time" && has.slope > 0){
fixed <- rownames(inla_mod$fit$summary.fixed)
fixed <- fixed[grepl("slope", fixed)]
fixed <- c(fixed, "time.struct")
select <- list()
for(i in 1:length(fixed)){
select[[i]] <- 0
names(select)[i] <- fixed[i]
}
if(is.null(draws)){
message("AR1 model diagnostics are still experimental, starting posterior sampling...")
sampAll <- INLA::inla.posterior.sample(n = 1e3, result = inla_mod$fit, intern = TRUE, selection = select, verbose = FALSE)
}else{
sampAll <- draws
}
#inla_mod$fit$marginals.random$time.struct
re <- grep("time.struct", rownames(sampAll[[1]]$latent))
fe <- grep("time.slope.group", rownames(sampAll[[1]]$latent))
fe0 <- grep("time.slope:1", rownames(sampAll[[1]]$latent))
if(length(fe0) > 0){
fe <- rep(fe0, length(inla_mod$age.rw.group))
}
struct.all <- matrix(0, length(re), length(sampAll))
T <- length(re) / length(fe)
xx <- ((1:T) - (T + 1)/2) / (T + 1)
for(j in 1:length(sampAll)){
for(k in 1:length(inla_mod$age.rw.group)){
group.index <- inla_mod$age.rw.group[k]
where <- ((group.index-1) * T + 1) :(group.index * T )
struct.all[where, j] <- sampAll[[j]]$latent[re[where], 1] + sampAll[[j]]$latent[fe[group.index], 1] * xx
}
}
temp <- data.frame(t(apply(struct.all, 1, stats::quantile, c(lower, 0.5, upper))))
colnames(temp) <- c("lower", "median", "upper")
rownames(temp) <- NULL
unstruct <- inla_mod$fit$marginals.random$time.unstruct
if(is.yearly){
label <- label.unstruct <- c(year_range[1] : year_range[2], year_label)
}else{
label <- label.unstruct <- year_label
}
if(!is.null(inla_mod$age.rw.group)){
group <- rep(inla_mod$age.groups, each = length(label))
# Now in this version, do not repeat the same effects
label <- rep(label, length(inla_mod$age.rw.group))
}
expand <- 1
if(!is.null(inla_mod$age.rw.group)) expand <- max(inla_mod$age.rw.group) / length(inla_mod$age.rw.group)
if(nrow(temp) != length(label) * expand) stop("The input year_range or year_label does not match the fitted model. Please double check.")
quants <- NULL
if(!is.null(inla_mod$age.rw.group)){
for(i in 1:length(inla_mod$age.groups)){
where <- (inla_mod$age.rw.group[i] - 1) * length(year_label) + c(1:length(year_label))
quants <- rbind(quants, temp[where, ])
}
}else{
quants <- temp
}
n <- dim(quants)[1]
m <- length(unstruct)
quants <- rbind(quants, getquants(unstruct, lower = lower, upper = upper))
quants$years <- c(label, label.unstruct)
if(!is.null(inla_mod$age.rw.group)){
quants$group <- c(group, rep(NA, m))
}
quants$years.num <- suppressWarnings(as.numeric(as.character(quants$years)))
quants$label <- c(rep("Structured", n), rep("IID", m))
quants$is.yearly <- !(quants$years %in% year_label)
}else if(field == "space"){
N <- dim(Amat)[1]
if("region.unstruct" %in% names(inla_mod$fit$marginals.random)){
struct <- inla_mod$fit$marginals.random$region.struct
unstruct <- inla_mod$fit$marginals.random$region.unstruct
group <- rep(c("Besag", "IID"), each = N)
}else if("region.struct" %in% names(inla_mod$fit$marginals.random)){
struct <- inla_mod$fit$marginals.random$region.struct[1:N]
unstruct <- inla_mod$fit$marginals.random$region.struct[(N+1):(N*2)]
group <- rep(c("Total", "Besag"), each = N)
}else{
stop("No spatial term used in this model.")
}
quants <- rbind(getquants(struct, lower = lower, upper = upper), getquants(unstruct, lower = lower, upper = upper))
quants$region <- rep(colnames(Amat), 2)
quants$label <- group
}else if(field == "spacetime"){
if("region.struct" %in% names(inla_mod$fit$marginals.random)){
N <- dim(Amat)[1]
}else{
stop("No spatial term used in this model.")
}
if(is.yearly){
label <- c(year_range[1] : year_range[2], year_label)
}else{
label <- year_label
}
has.random.slope <- sum(grepl("slope", names(inla_mod$fit$summary.random)))
if(has.random.slope){
fixed <- c("st.slope.id", "region.int", "time.area")
fixed <- fixed[fixed %in% c(rownames(inla_mod$fit$summary.fixed), names(inla_mod$fit$summary.random))]
select <- list()
for(i in 1:length(fixed)){
select[[i]] <- 0
names(select)[i] <- fixed[i]
}
if(is.null(draws)){
message("Posterior draws not provided. Start new posterior sampling...")
sampAll <- INLA::inla.posterior.sample(n = nsim, result = inla_mod$fit, intern = TRUE, selection = select, verbose = FALSE)
}else{
sampAll <- draws
}
fields <- rownames(sampAll[[1]]$latent)
A <- inla_mod$fit$.args$data
A <- A[, colnames(A) %in% c(fixed, "time.unstruct", "region.struct")]
A <- unique(A)
AA.loc <- A
AA.loc$time.area <- match(paste0("time.area:", AA.loc$time.area), fields)
region.int.index <- (AA.loc$time.unstruct - 1) * max(AA.loc$region.struct) + AA.loc$region.struct
AA.loc$region.int <- match(paste0("region.int:", region.int.index), fields)
T <- max(A$time.unstruct)
AA.loc$ststar <- (AA.loc$time.unstruct - (T + 1)/2) / (T + 1)
AA.loc$st.slope <- match(paste0("st.slope.id:", AA.loc$region.struct), fields)
sum <- matrix(NA, dim(AA.loc)[1], length(sampAll))
for(i in 1:length(sampAll)){
s1 <- sampAll[[i]]$latent[AA.loc$time.area]
s2 <- sampAll[[i]]$latent[AA.loc$region.int]
s3 <- sampAll[[i]]$latent[AA.loc$st.slope] * AA.loc$ststar
if(sum(!is.na(s1)) == 0){
sum[, i] <- s2 + s3
}else{
sum[, i] <- s1 + s3
}
}
group <- colnames(Amat)[AA.loc$region.struct]
label <- label[AA.loc$time.unstruct]
struct <- NULL
quants <- t(apply(sum, 1, function(x, lower, upper){as.numeric(stats::quantile(x, c(lower, 0.5, upper)))}, lower, upper))
quants <- data.frame(quants)
colnames(quants) <- c("lower", "median", "upper")
}else if("region.int" %in% names(inla_mod$fit$marginals.random)){
group <- rep(colnames(Amat), length(label))
label <- rep(label, each = N)
struct <- inla_mod$fit$marginals.random$region.int
}else{
group <- colnames(Amat)[inla_mod$time.area$region_number]
label <- label[inla_mod$time.area$time.unstruct]
struct <- inla_mod$fit$marginals.random$time.area
}
if(!is.null(struct) && length(struct) != length(label)) stop("The input year_range or year_label does not match the fitted model. Please double check.")
if(!is.null(struct)) quants <- getquants(struct, lower = lower, upper = upper)
quants$years <- label
quants$years.num <- suppressWarnings(as.numeric(as.character(quants$years)))
quants$region <- group
quants$is.yearly <- !(quants$years %in% year_label)
}else{
stop("The field argument needs to be space, time, or spacetime.")
}
class(quants) <- c("SUMMERproj", "data.frame")
return(quants)
}
martinbryan/SUMMER documentation built on April 10, 2024, 5:03 a.m.
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Defines functions getDiag
Documented in getDiag
#' Extract posterior summaries of random effects
#'
#' @param inla_mod output from \code{\link{smoothDirect}} or \code{\link{smoothCluster}}
#' @param field which random effects to plot. It can be one of the following: space, time, and spacetime.
#' @param CI Desired level of credible intervals
#' @param draws Posterior samples drawn from the fitted model. This argument allows the previously sampled draws (by setting save.draws to be TRUE) be used in new aggregation tasks.
#' @param nsim number of simulations, only applicable for the cluster-level model space-time interaction terms when random slopes are included.
#' @param ... Unused arguments, for users with fitted object from the package before v1.0.0, arguments including Amat, year_label, and year_range can still be specified manually.
#'
#' @return List of diagnostic plots
#' @author Zehang Richard Li
#' @examples
#' \dontrun{
#' data(DemoMap)
#' years <- levels(DemoData[[1]]$time)
#'
#' # obtain direct estimates
#' data <- getDirectList(births = DemoData,
#' years = years,
#' regionVar = "region", timeVar = "time",
#' clusterVar = "~clustid+id",
#' ageVar = "age", weightsVar = "weights",
#' geo.recode = NULL)
#' # obtain direct estimates
#' data_multi <- getDirectList(births = DemoData, years = years,
#' regionVar = "region", timeVar = "time", clusterVar = "~clustid+id",
#' ageVar = "age", weightsVar = "weights", geo.recode = NULL)
#' data <- aggregateSurvey(data_multi)
#'
#' # national model
#' years.all <- c(years, "15-19")
#' fit1 <- smoothDirect(data = data, geo = DemoMap$geo, Amat = DemoMap$Amat,
#' year_label = years.all, year_range = c(1985, 2019),
#' rw = 2, is.yearly=FALSE, m = 5)
#' random.time <- getDiag(fit1, field = "time")
#' random.space <- getDiag(fit1, field = "space")
#' random.spacetime <- getDiag(fit1, field = "spacetime")
#' }
#'
#' @export
getDiag <- function(inla_mod, field = c("space", "time", "spacetime")[1], CI = 0.95, draws = NULL, nsim = 1000, ...){
lower <- (1 - CI) / 2
upper <- 1 - lower
if(!is.null(inla_mod$year_range)){
year_range <- inla_mod$year_range
}else{
warning("The fitted object was from an old version of SUMMER, please specify 'year_range' argument when calling getDiag()")
}
if(!is.null(inla_mod$year_label)){
year_label <- inla_mod$year_label
}else{
warning("The fitted object was from an old version of SUMMER, please specify 'year_label' argument when calling getDiag()")
}
if(!is.null(inla_mod$has.Amat)){
Amat <- inla_mod$Amat
}else{
warning("The fitted object was from an old version of SUMMER, please specify 'Amat' argument when calling getDiag()")
}
getquants <- function(mlist, lower, upper){
quants <- data.frame(matrix(NA, length(mlist), 3))
for(i in 1:length(mlist)){
quants[i, ] <- INLA::inla.qmarginal(c(lower, 0.5, upper), mlist[[i]])
}
colnames(quants) <- c("lower", "median", "upper")
return(quants)
}
is.yearly = inla_mod$is.yearly
has.slope <- sum(grepl("slope", rownames(inla_mod$fit$summary.fixed)))
if(field == "time" && has.slope == 0){
struct <- inla_mod$fit$marginals.random$time.struct
unstruct <- inla_mod$fit$marginals.random$time.unstruct
if(is.yearly){
label <- label.unstruct <- c(year_range[1] : year_range[2], year_label)
}else{
label <- label.unstruct <- year_label
}
if(!is.null(inla_mod$age.rw.group)){
group <- rep(inla_mod$age.groups, each = length(label))
label <- rep(label, length(inla_mod$age.rw.group))
}
expand <- 1
if(!is.null(inla_mod$age.rw.group)) expand <- max(inla_mod$age.rw.group) / length(inla_mod$age.rw.group)
if(length(struct) != length(label) * expand) stop("The input year_range or year_label does not match the fitted model. Please double check.")
temp <- getquants(struct, lower = lower, upper = upper)
quants <- NULL
if(!is.null(inla_mod$age.rw.group)){
for(i in 1:length(inla_mod$age.groups)){
where <- (inla_mod$age.rw.group[i] - 1) * length(year_label) + c(1:length(year_label))
quants <- rbind(quants, temp[where, ])
}
}else{
quants <- getquants(struct, lower = lower, upper = upper)
}
n <- dim(quants)[1]
m <- length(unstruct)
quants <- rbind(quants, getquants(unstruct, lower = lower, upper = upper))
quants$years <- c(label, label.unstruct)
if(!is.null(inla_mod$age.rw.group)){
quants$group <- c(group, rep("IID", m))
}
quants$years.num <- suppressWarnings(as.numeric(as.character(quants$years)))
quants$label <- c(rep("Structured", n), rep("IID", m))
quants$is.yearly <- !(quants$years %in% year_label)
}else if(field == "time" && has.slope > 0){
fixed <- rownames(inla_mod$fit$summary.fixed)
fixed <- fixed[grepl("slope", fixed)]
fixed <- c(fixed, "time.struct")
select <- list()
for(i in 1:length(fixed)){
select[[i]] <- 0
names(select)[i] <- fixed[i]
}
if(is.null(draws)){
message("AR1 model diagnostics are still experimental, starting posterior sampling...")
sampAll <- INLA::inla.posterior.sample(n = 1e3, result = inla_mod$fit, intern = TRUE, selection = select, verbose = FALSE)
}else{
sampAll <- draws
}
#inla_mod$fit$marginals.random$time.struct
re <- grep("time.struct", rownames(sampAll[[1]]$latent))
fe <- grep("time.slope.group", rownames(sampAll[[1]]$latent))
fe0 <- grep("time.slope:1", rownames(sampAll[[1]]$latent))
if(length(fe0) > 0){
fe <- rep(fe0, length(inla_mod$age.rw.group))
}
struct.all <- matrix(0, length(re), length(sampAll))
T <- length(re) / length(fe)
xx <- ((1:T) - (T + 1)/2) / (T + 1)
for(j in 1:length(sampAll)){
for(k in 1:length(inla_mod$age.rw.group)){
group.index <- inla_mod$age.rw.group[k]
where <- ((group.index-1) * T + 1) :(group.index * T )
struct.all[where, j] <- sampAll[[j]]$latent[re[where], 1] + sampAll[[j]]$latent[fe[group.index], 1] * xx
}
}
temp <- data.frame(t(apply(struct.all, 1, stats::quantile, c(lower, 0.5, upper))))
colnames(temp) <- c("lower", "median", "upper")
rownames(temp) <- NULL
unstruct <- inla_mod$fit$marginals.random$time.unstruct
if(is.yearly){
label <- label.unstruct <- c(year_range[1] : year_range[2], year_label)
}else{
label <- label.unstruct <- year_label
}
if(!is.null(inla_mod$age.rw.group)){
group <- rep(inla_mod$age.groups, each = length(label))
# Now in this version, do not repeat the same effects
label <- rep(label, length(inla_mod$age.rw.group))
}
expand <- 1
if(!is.null(inla_mod$age.rw.group)) expand <- max(inla_mod$age.rw.group) / length(inla_mod$age.rw.group)
if(nrow(temp) != length(label) * expand) stop("The input year_range or year_label does not match the fitted model. Please double check.")
quants <- NULL
if(!is.null(inla_mod$age.rw.group)){
for(i in 1:length(inla_mod$age.groups)){
where <- (inla_mod$age.rw.group[i] - 1) * length(year_label) + c(1:length(year_label))
quants <- rbind(quants, temp[where, ])
}
}else{
quants <- temp
}
n <- dim(quants)[1]
m <- length(unstruct)
quants <- rbind(quants, getquants(unstruct, lower = lower, upper = upper))
quants$years <- c(label, label.unstruct)
if(!is.null(inla_mod$age.rw.group)){
quants$group <- c(group, rep(NA, m))
}
quants$years.num <- suppressWarnings(as.numeric(as.character(quants$years)))
quants$label <- c(rep("Structured", n), rep("IID", m))
quants$is.yearly <- !(quants$years %in% year_label)
}else if(field == "space"){
N <- dim(Amat)[1]
if("region.unstruct" %in% names(inla_mod$fit$marginals.random)){
struct <- inla_mod$fit$marginals.random$region.struct
unstruct <- inla_mod$fit$marginals.random$region.unstruct
group <- rep(c("Besag", "IID"), each = N)
}else if("region.struct" %in% names(inla_mod$fit$marginals.random)){
struct <- inla_mod$fit$marginals.random$region.struct[1:N]
unstruct <- inla_mod$fit$marginals.random$region.struct[(N+1):(N*2)]
group <- rep(c("Total", "Besag"), each = N)
}else{
stop("No spatial term used in this model.")
}
quants <- rbind(getquants(struct, lower = lower, upper = upper), getquants(unstruct, lower = lower, upper = upper))
quants$region <- rep(colnames(Amat), 2)
quants$label <- group
}else if(field == "spacetime"){
if("region.struct" %in% names(inla_mod$fit$marginals.random)){
N <- dim(Amat)[1]
}else{
stop("No spatial term used in this model.")
}
if(is.yearly){
label <- c(year_range[1] : year_range[2], year_label)
}else{
label <- year_label
}
has.random.slope <- sum(grepl("slope", names(inla_mod$fit$summary.random)))
if(has.random.slope){
fixed <- c("st.slope.id", "region.int", "time.area")
fixed <- fixed[fixed %in% c(rownames(inla_mod$fit$summary.fixed), names(inla_mod$fit$summary.random))]
select <- list()
for(i in 1:length(fixed)){
select[[i]] <- 0
names(select)[i] <- fixed[i]
}
if(is.null(draws)){
message("Posterior draws not provided. Start new posterior sampling...")
sampAll <- INLA::inla.posterior.sample(n = nsim, result = inla_mod$fit, intern = TRUE, selection = select, verbose = FALSE)
}else{
sampAll <- draws
}
fields <- rownames(sampAll[[1]]$latent)
A <- inla_mod$fit$.args$data
A <- A[, colnames(A) %in% c(fixed, "time.unstruct", "region.struct")]
A <- unique(A)
AA.loc <- A
AA.loc$time.area <- match(paste0("time.area:", AA.loc$time.area), fields)
region.int.index <- (AA.loc$time.unstruct - 1) * max(AA.loc$region.struct) + AA.loc$region.struct
AA.loc$region.int <- match(paste0("region.int:", region.int.index), fields)
T <- max(A$time.unstruct)
AA.loc$ststar <- (AA.loc$time.unstruct - (T + 1)/2) / (T + 1)
AA.loc$st.slope <- match(paste0("st.slope.id:", AA.loc$region.struct), fields)
sum <- matrix(NA, dim(AA.loc)[1], length(sampAll))
for(i in 1:length(sampAll)){
s1 <- sampAll[[i]]$latent[AA.loc$time.area]
s2 <- sampAll[[i]]$latent[AA.loc$region.int]
s3 <- sampAll[[i]]$latent[AA.loc$st.slope] * AA.loc$ststar
if(sum(!is.na(s1)) == 0){
sum[, i] <- s2 + s3
}else{
sum[, i] <- s1 + s3
}
}
group <- colnames(Amat)[AA.loc$region.struct]
label <- label[AA.loc$time.unstruct]
struct <- NULL
quants <- t(apply(sum, 1, function(x, lower, upper){as.numeric(stats::quantile(x, c(lower, 0.5, upper)))}, lower, upper))
quants <- data.frame(quants)
colnames(quants) <- c("lower", "median", "upper")
}else if("region.int" %in% names(inla_mod$fit$marginals.random)){
group <- rep(colnames(Amat), length(label))
label <- rep(label, each = N)
struct <- inla_mod$fit$marginals.random$region.int
}else{
group <- colnames(Amat)[inla_mod$time.area$region_number]
label <- label[inla_mod$time.area$time.unstruct]
struct <- inla_mod$fit$marginals.random$time.area
}
if(!is.null(struct) && length(struct) != length(label)) stop("The input year_range or year_label does not match the fitted model. Please double check.")
if(!is.null(struct)) quants <- getquants(struct, lower = lower, upper = upper)
quants$years <- label
quants$years.num <- suppressWarnings(as.numeric(as.character(quants$years)))
quants$region <- group
quants$is.yearly <- !(quants$years %in% year_label)
}else{
stop("The field argument needs to be space, time, or spacetime.")
}
class(quants) <- c("SUMMERproj", "data.frame")
return(quants)
}
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