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#' Location of knots for RCS
#'
#' @description
#' Location of knots from Harrell (2015), Regression Modeling Strategies.
#'
#' @param k number of knots, must be strictly between 3 and 7.
#'
#' @details
#' Harrell (2015) states that for many datasets, k = 4 offers an adequate fit of
#' the model and is a good compromise between flexibility and loss of pocesion
#' caused by overfitting a small sampleā. If the sample size is small, three knots
#' should be used in order to have enough observations in between the knots to be
#' able to fit each polynomial. If the sample size is large and if there is reason
#' to believe that the relationship being studied changes quickly, more than five
#' knots can be used.
#'
#' @return a numeric vector.
#'
#' @references
#' Harrell FE (2015). Regression models for continuous y and case study in ordinal
#' regression. In: Harrell FE, ed. Regression Modeling Strategies. New York:
#' Springer.
#' @export
#'
#' @examples
#' # Location of 3 knots
#' knot(3)
#'
#' # Location of 7 knots
#' knot(7)
knot <- function(k = 3){
if(k < 3 | k > 7){
stop("The number of knots must be strictly between 3 and 7.", call. = FALSE)
}
k <- as.character(k)
switch (k,
"3" = c(0.10, 0.50, 0.90),
"4" = c(0.05, 0.35, 0.65, 0.95),
"5" = c(0.05, 0.275, 0.50, 0.725, 0.95),
"6" = c(0.05, 0.23, 0.41, 0.59, 0.77, 0.95),
"7" = c(0.025, 0.1833, 0.3417, 0.50, 0.6583, 0.8167, 0.975)
)
}
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