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#' Score the MetSSS
#'
#' Function requires systolic and diastolic blood pressure,
#' triglycerides, waist circumference, HDL cholesterol,
#' blood glucose, and sex.
#'
#' @param input data passed on as \code{newdata} to \code{\link{predictCS}}
#' @importFrom utils read.csv
#' @export
#' @examples
#' mydata <- data.frame(
#' sbp = c(122, 102.5),
#' dbp = c(76.5, 64),
#' trigs = c(1.47, 1.27),
#' hdl = c(2.22, 1.59),
#' waist = c(71, 91),
#' glucose = c(5.16, 5.82),
#' sex = c("Female", "Male"))
#'
#' MetSSS(mydata)
MetSSS <- function(input) {
#input can either be csv file or data
newdata <- if (is.character(input) && file.exists(input)) {
read.csv(input)
} else {
as.data.frame(input)
}
v <- c("sbp", "dbp", "trigs", "hdl", "waist", "glucose")
if (!all(v %in% names(newdata))) {
stop(sprintf("%s must be column names in the data",
paste(v[!v %in% names(newdata)], collapse = ", ")))
}
for (i in v) {
newdata[, i] <- as.numeric(newdata[, i])
}
## MetSSS_model is included with the package
metsss <- predictCS(MetSSS_model,
newdata = newdata[, v],
groups = as.character(newdata$sex))
if (!identical(length(metsss), nrow(newdata))) {
metsss <- metsss[seq_len(nrow(newdata))]
}
newdata$metsss <- metsss
return(newdata)
}
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