R/SmoothSim.R

In simPH: Simulate and Plot Estimates from Cox Proportional Hazards Models

#' Smooth values from one simulation using smoothing splines
#'
#' @param x a vector containing the x value.
#' @param y a vector containing the y value.
#' @param df numeric. The desired equivalent number of degrees of freedom
#' (trace of the smoother matrix).
#'
#' @references Chambers, J. M. and Hastie, T. J. (1992) Statistical Models in
#' S, Wadsworth & Brooks/Cole.
#'
#' Green, P. J. and Silverman, B. W. (1994) Nonparametric Regression and
#' Generalized Linear Models: A Roughness Penalty Approach. Chapman and Hall.
#'
#'Hastie, T. J. and Tibshirani, R. J. (1990) Generalized Additive Models.
#' Chapman and Hall.
#'
#' @importFrom stats smooth.spline fitted
#' @keywords internals
#' @noRd

SmoothOneSim <- function(x, y, df = 10){
    TempXY <- cbind(x, y)
    TempOut <- smooth.spline(TempXY, df = df)
    Out <- fitted(TempOut)
    return(Out)
}

#' Smooth values for all simulations
#'
#' @param SimIn data frame. Pre-smoothed simulation
#' @param xaxis character string. The column that will form the x-axis in the
#' plot.
#'
#' @importFrom dplyr group_by mutate select %>%
#'
#' @keywords internals
#' @noRd
SmoothSimulations <- function(SimIn, xaxis = "Xj"){
    # CRAN nonsense
    xtemp <- SimID <- Xj <- QI <- NULL

    names(SimIn)[names(SimIn) == xaxis] <- 'xtemp'

    # Spline smooth
    SimIn <- SimIn %>% dplyr::group_by(SimID)
    SimsFitted <- SimIn %>% dplyr::mutate(QI = SmoothOneSim(xtemp, QI))
    names(SimsFitted)[names(SimsFitted) == 'xtemp'] <- xaxis
    class(SimsFitted) <- 'data.frame'
    SimsFitted
}