R/s_LM.R

In egenn/rtemis: Machine Learning and Visualization

# s_LM.R
# ::rtemis::
# 2015 E.D. Gennatas www.lambdamd.org

#' Linear model
#'
#' Fit a linear model and validate it. Options include base `lm()`, robust linear model using
#'   `MASS:rlm()`, generalized least squares using `nlme::gls`, or polynomial regression
#'   using `stats::poly` to transform features
#'
#' GLS can be useful in place of a standard linear model, when there is correlation among
#'   the residuals and/or they have unequal variances.
#'   Warning: `nlme`'s implementation is buggy, and `predict` will not work
#'   because of environment problems, which means it fails to get predicted values if
#'   `x.test` is provided.
#'   `robut = TRUE` trains a robust linear model using `MASS::rlm`.
#'   `gls = TRUE` trains a generalized least squares model using `nlme::gls`.
#'
#' @inheritParams s_GLM
#' @param robust Logical: if TRUE, use `MASS::rlm()` instead of base `lm()`
#' @param gls Logical: if TRUE, use `nlme::gls`
#' @param polynomial Logical: if TRUE, run lm on `poly(x, poly.d)` (creates orthogonal polynomials)
#' @param poly.d Integer: degree of polynomial
#' @param poly.raw Logical: if TRUE, use raw polynomials.
#'   Default, which should not really be changed is FALSE
#' @param plot.fitted Logical: if TRUE, plot True (y) vs Fitted
#' @param plot.predicted Logical: if TRUE, plot True (y.test) vs Predicted.
#'   Requires `x.test` and `y.test`
#' @param plot.theme Character: "zero", "dark", "box", "darkbox"
#' @param na.action How to handle missing values. See `?na.fail`
#' @param question Character: the question you are attempting to answer with this model, in plain language.
#' @param verbose Logical: If TRUE, print summary to screen.
#' @param outdir Path to output directory.
#'   If defined, will save Predicted vs. True plot, if available,
#'   as well as full model output, if `save.mod` is TRUE
#' @param save.mod Logical. If TRUE, save all output as RDS file in `outdir`
#'   `save.mod` is TRUE by default if an `outdir` is defined. If set to TRUE, and no `outdir`
#'   is defined, outdir defaults to `paste0("./s.", mod.name)`
#' @param ... Additional arguments to be passed to `MASS::rlm` if `robust = TRUE`
#'   or `MASS::lm.gls` if `gls = TRUE`
#' @return `rtMod`
#' @author E.D. Gennatas
#' @seealso [train_cv] for external cross-validation
#' @family Supervised Learning
#' @examples
#' x <- rnorm(100)
#' y <- .6 * x + 12 + rnorm(100) / 2
#' mod <- s_LM(x, y)
#' @export

s_LM <- function(x, y = NULL,
                 x.test = NULL, y.test = NULL,
                 x.name = NULL, y.name = NULL,
                 weights = NULL,
                 ifw = TRUE,
                 ifw.type = 2,
                 upsample = FALSE,
                 downsample = FALSE,
                 resample.seed = NULL,
                 intercept = TRUE,
                 robust = FALSE,
                 gls = FALSE,
                 polynomial = FALSE,
                 poly.d = 3,
                 poly.raw = FALSE,
                 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_LM))
    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)
  if (robust) {
    mod.name <- "RLM"
  } else if (gls) {
    mod.name <- "GLS"
  } else if (polynomial) {
    mod.name <- "POLY"
  } else {
    mod.name <- "LM"
  }

  # Dependencies ----
  if (robust) {
    dependency_check("MASS")
  }
  if (gls) {
    dependency_check("nlme")
  }

  # Arguments ----
  if (is.null(y) && NCOL(x) < 2) {
    print(args(s_LM))
    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 (sum(c(robust, gls, polynomial)) > 1) {
    stop("Can only specify one of 'robust', 'gls', or 'polynomial'")
  }
  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,
    ifw = ifw,
    ifw.type = ifw.type,
    upsample = upsample,
    downsample = downsample,
    verbose = verbose
  )
  x <- dt$x
  y <- dt$y
  x.test <- dt$x.test
  y.test <- dt$y.test
  xnames <- dt$xnames
  type <- dt$type
  # .weights <- if (is.null(weights) & ifw) dt$weights else weights
  if (verbose) dataSummary(x, y, x.test, y.test, type)
  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 <- data.frame(y = y, x)
  if (!polynomial) {
    features <- paste(xnames, collapse = " + ")
    formula.str <- paste0(y.name, " ~ ", features)
  } else {
    features <- paste0("poly(", paste0(xnames, ", degree = ", poly.d, ", raw = ", poly.raw, ")",
      collapse = " + poly("
    ))
    formula.str <- paste0(y.name, " ~ ", features)
  }
  # Intercept
  if (!intercept) formula.str <- paste(formula.str, "- 1")
  myformula <- as.formula(formula.str)

  # LM & POLY ----
  if (!robust && !gls) {
    if (verbose) msg2("Training linear model...", newline.pre = TRUE)
    mod <- lm(myformula,
      data = df.train,
      weights = weights,
      na.action = na.action, ...
    )
  }
  # RLM
  if (robust) {
    if (verbose) msg2("Training robust linear model...", newline.pre = TRUE)
    mod <- MASS::rlm(myformula,
      data = df.train,
      weights = weights,
      na.action = na.action, ...
    )
  }
  # GLS
  if (gls) {
    if (verbose) msg2("Training generalized least squares...", newline.pre = TRUE)
    mod <- nlme::gls(myformula,
      data = df.train,
      weights = weights,
      na.action = na.action, ...
    )
  }

  if (trace > 0) print(summary(mod))

  # Fitted ----
  if (!gls) {
    fitted <- predict(mod, x, se.fit = TRUE)
    se.fit <- as.numeric(fitted$se.fit)
    fitted <- as.numeric(fitted$fit)
  } else {
    se.fit <- NULL
    fitted <- as.numeric(predict(mod, x))
  }

  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)) {
    if (gls) {
      assign("myformula", myformula) # why need this? nlme is buggy?
      predicted <- as.numeric(predict(mod, x.test))
    } else {
      predicted <- predict(mod, x.test, se.fit = TRUE)
      se.prediction <- predicted$se.fit
      predicted <- as.numeric(predicted$fit)
    }

    if (!is.null(y.test)) {
      error.test <- mod_error(y.test, predicted)
      if (verbose) errorSummary(error.test, mod.name)
    }
  }

  # Outro ----
  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 = se.fit,
    error.train = error.train,
    predicted = predicted,
    se.prediction = se.prediction,
    varimp = mod$coefficients[-1],
    error.test = error.test,
    question = question
  )

  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_LM


#' Robust linear model
#'
#' Convenience alias for `s_LM(robust = T)`. Uses `MASS::rlm`
#'
#' @inheritParams s_GLM
#' @param ... Additional parameters to be passed to `MASS::rlm`
#' @export

s_RLM <- function(x, y, x.test = NULL, y.test = NULL, ...) {
  s_LM(x, y, x.test = x.test, y.test = y.test, robust = TRUE, ...)
} # rtemis::s_RLM