R/idw.R

In ECO230/eco230r:

#' Generate Output for an Wilcoxon Rank-Sum Test
#'
#'This function will generate the output for an Wilcoxon rank-sum test for independent non-parametric samples
#'
#' @param x A formula dependent variable ~ dependent variable, a data frame variable can also be passed in.
#' @param y A data frame, necessary if data frame is not referenced in formula or piped in, if a data frame variable is passed in to x, a data frame variable will also be accepted.
#' @param tails An integer indicating whether the test is one-tailed '1' or two-tailed '2'
#'
#' @return A list of output for reporting $analysis_type, $results, $descriptive_statistics
#' @export
#'
#' @examples idw_results <- idw_data %>% idw(scones ~ tea)
#' idw_results <- idw(scones ~ tea, idw_data, tails = 1)
#' idw_results <- idw(idw_data, scones ~ tea)
#' idw_results <- idw(idw_data$scones ~ idw_data$tea)
idw <- function(x, y = NULL, tails = 2) {
  fd <- is.formula(x) * is.data.frame(y)
  df <- is.data.frame(x) * is.formula(y)
  fn <- is.formula(x) * missing(y)

  if (df) {
    formula = y #m_x
    data = x #m_y
    mf <- model.frame(formula,data)
  }
  else if (fd) {
    formula = x #m_x
    data = y #m_y
    mf <- model.frame(formula,data)
  }
  else if (fn) {
    formula = x
    mf <- model.frame(formula)
    colnames(mf) <- sapply(strsplit(colnames(mf),"\\$"),tail,1) #rename by splitting $ if present
    formula <- eval(parse(text=paste0(colnames(mf)[[1]],'~',colnames(mf)[[2]])))
    data <- mf
    mf <- model.frame(formula,data)

  }

  raw_rows = nrow(data)

  #convert independent variable to a factor
  if(!(is.factor(mf[[2]])))
  {
    mf[[2]] <- as.factor(mf[[2]])
    print('Independent variable converted to a factor using as.factor()')
  }

  #remove any infinite numbers any remaining nans
  mf <- mf[complete.cases(mf),]

  model_rows = nrow(mf)

  diff_rows = raw_rows - model_rows

  if(diff_rows > 0)
  {print(paste(as.character(diff_rows),'rows removed due to NA/Nan/Inf values in data.'))}

  #build model
  mod <- wilcox.test(formula=formula, data=mf)

  #tryCatch({
  #  bf <- '--'
  #  library(bayesWilcoxTest)
  #  bf <- bayes.wilcox.test(formula=formula, data=mf, paired = FALSE)
  #},error=function(e) {
  #  print(e)
  #}
  #)


  if (tails ==2) {
    an <- 'Wilcoxon Rank-Sum Test (Independent Samples), Two Tailed test'
  } else {
    an <- 'Wilcoxon Rank-Sum Test (Independent Samples), One Tailed test'
  }

  #if(typeof(bf)=='character'){
  #  #error in bayes factor calculation should return bf <- '-'
  #  byfct <- bf
  #}
  #else{
  #  byfct <- summary(bf)[7]
  #  byfct <- round(byfct,3)
  #}

  W <- mod$statistic
  p <- mod$p.value
  N <- length(mf[[1]])
  z <- abs(qnorm(p/2))
  r <- z/sqrt(N)
  if (tails == 1) {p <- p/2}
  res <- paste(c('W = ', round(W,3),', p = ', round(p,3),', r = ',round(r,3)), collapse = '')
  #descriptives
  colnames(mf) <- c('dv','iv')
  tmp <- as.matrix(aggregate(. ~ iv, mf, function(x) c(N = signif(length(x),3),
                                                       Median = signif(median(x),3))))
  dsc <- as.data.frame(tmp)
  colnames(dsc) <- c('Group','N','Median')
  list('analysis_type' = an, 'results' = res, 'descriptive_statistics' = dsc)
}