R/s_GLS.R

In egenn/rtemis: Machine Learning and Visualization

# s_GLS.R
# ::rtemis::
# 2017 E.D. Gennatas www.lambdamd.org

#' Generalized Least Squares \[R\]
#'
#' Train a Generalized Least Squares regression model using `nlme::gls`
#'
#' @inheritParams s_GLM
#' @param nway.interactions Integer: Include n-way interactions. This integer defines 
#' the n in: \code{formula = y ~^n}
#' @param covariate Character: Name of column. Will include interactions between all 
#' features this variable.
#' @param ... Additional arguments
#' 
#' @return `rtMod`
#' @author E.D. Gennatas
#' @family Supervised Learning
#' @export

s_GLS <- function(x, y = NULL,
                  x.test = NULL, y.test = NULL,
                  x.name = NULL, y.name = NULL,
                  interactions = FALSE,
                  nway.interactions = 0,
                  covariate = NULL,
                  weights = NULL,
                  intercept = TRUE,
                  print.plot = FALSE,
                  plot.fitted = NULL,
                  plot.predicted = NULL,
                  plot.theme = rtTheme,
                  na.action = na.exclude,
                  question = NULL,
                  verbose = TRUE,
                  trace = 0,
                  outdir = NULL,
                  save.mod = ifelse(!is.null(outdir), TRUE, FALSE), ...) {
  # Intro ----
  if (missing(x)) {
    print(args(s_GLS))
    return(invisible(9))
  }
  if (!is.null(outdir)) outdir <- normalizePath(outdir, mustWork = FALSE)
  logFile <- if (!is.null(outdir)) {
    paste0(outdir, "/", sys.calls()[[1]][[1]], ".", format(Sys.time(), "%Y%m%d.%H%M%S"), ".log")
  } else {
    NULL
  }
  start.time <- intro(verbose = verbose, logFile = logFile)

  # Dependencies
  dependency_check("nlme")

  # Arguments ----
  if (is.null(y) && NCOL(x) < 2) {
    print(args(s_GLS))
    stop("y is missing")
  }
  if (is.null(x.name)) x.name <- getName(x, "x")
  if (is.null(y.name)) y.name <- getName(y, "y")
  if (!verbose) print.plot <- FALSE
  verbose <- verbose | !is.null(logFile)
  if (save.mod && is.null(outdir)) outdir <- paste0("./s.", mod.name)
  if (!is.null(outdir)) outdir <- paste0(normalizePath(outdir, mustWork = FALSE), "/")

  # Data ----
  dt <- prepare_data(x, y, x.test, y.test, verbose = verbose)
  x <- dt$x
  y <- dt$y
  x.test <- dt$x.test
  y.test <- dt$y.test
  xnames <- dt$xnames
  type <- dt$type
  checkType(type, "Regression", mod.name)
  if (verbose) dataSummary(x, y, x.test, y.test, type)
  mod.name <- "GLS"
  if (print.plot) {
    if (is.null(plot.fitted)) plot.fitted <- if (is.null(y.test)) TRUE else FALSE
    if (is.null(plot.predicted)) plot.predicted <- if (!is.null(y.test)) TRUE else FALSE
  } else {
    plot.fitted <- plot.predicted <- FALSE
  }

  # Formula ----
  df.train <- cbind(x, y = y)

  if (nway.interactions > 0) {
    .formula <- paste0(y.name, " ~ .^", nway.interactions)
  } else if (interactions) {
    .formula <- paste(y.name, "~ .*.")
  } else if (!is.null(covariate)) {
    features <- xnames[!grepl(covariate, xnames)]
    .formula <- paste(y.name, "~", paste(features, "*", covariate, collapse = " + "))
  } else {
    .formula <- paste(y.name, "~ .")
  }

  # Intercept
  if (!intercept) .formula <- paste(.formula, "- 1")
  .formula <- as.formula(.formula)

  # GLS ----
  if (verbose) msg2("Trainings GLS...", newline.pre = TRUE)
  args <- c(
    list(model = .formula, data = df.train, na.action = na.action),
    list(...)
  )
  mod <- do.call(nlme::gls, args)
  if (trace > 0) print(summary(mod))

  # Fitted ----
  fitted <- as.numeric(mod$fitted)
  error.train <- mod_error(y, fitted)
  if (verbose) errorSummary(error.train, mod.name)

  # Predicted ----
  predicted <- se.prediction <- error.test <- NULL
  if (!is.null(x.test)) {
    predicted <- predict(mod, x.test)
    if (!is.null(y.test) && length(y.test) > 1) {
      error.test <- mod_error(y.test, predicted)
      if (verbose) errorSummary(error.test, mod.name)
    }
  }

  # Outro ----
  extra <- list(formula = .formula)
  extra <- list()
  rt <- rtModSet(
    rtclass = "rtMod",
    mod = mod,
    mod.name = mod.name,
    type = type,
    y.train = y,
    y.test = y.test,
    x.name = x.name,
    y.name = y.name,
    xnames = xnames,
    fitted = fitted,
    se.fit = NULL,
    error.train = error.train,
    predicted = predicted,
    se.prediction = NULL,
    error.test = error.test,
    varimp = mod$coefficients[-1],
    question = question,
    extra = extra
  )

  rtMod.out(
    rt,
    print.plot,
    plot.fitted,
    plot.predicted,
    y.test,
    mod.name,
    outdir,
    save.mod,
    verbose,
    plot.theme
  )

  outro(start.time, verbose = verbose, sinkOff = ifelse(is.null(logFile), FALSE, TRUE))
  rt
} # rtemis::s_GLS