R/bStatCor_v1.R

In idrblab/NOREVA2020: R Package for Assessing Normalization for Metabolomic Data

# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Library General Public
# License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Library General Public License for more details.
#
# You should have received A copy of the GNU Library General
# Public License along with this library; if not, write to the
# Free Foundation, Inc., 59 Temple Place, Suite 330, Boston,
# MA  02111-1307  USA


################################################################################
# FUNCTION:               BASIC STATISTICS:
#  basicStats              Returns a basic statistics summary
################################################################################

bStatCor <- function(x, ci = 0.95)
{   # A function implemented by Diethelm Wuertz

  # Description:
  #   Calculates Basic Statistics

  # Arguments:
  #   x - an object which can be transformed by the function
  #       as.matrix() into an object of class matrix.
  #   ci - a numeric value setting the confidence interval.

  # Value:
  #   a two-column data frame, where the first column takes the
  #   value of the statistics, and the second its name, e.g.
  #   "nobs", "NAs",  "Minimum", "Maximum", "1. Quartile",
  #   "3. Quartile",  "Mean", "Median", "Sum",  "SE Mean",
  #   "LCL Mean", "UCL Mean", "Variance", "Stdev", "Skewness",
  #   "Kurtosis")

  # FUNCTION:

  # Univariate/Multivariate:
  y = as.matrix(x)


  # Handle Column Names:
  if (is.null(colnames(y))) {
    Dim = dim(y)[2]
    if (Dim == 1) {
      colnames(y) = paste(substitute(x), collapse = ".")
    } else if (Dim > 1) {
      colnames(y) =
        paste(paste(substitute(x), collapse = ""), 1:Dim, sep = "")
    }
  }

  # Internal Function - CL Levels:
  cl.vals = function(x, ci) {
    x = x[!is.na(x)]
    n = length(x)
    if(n <= 1) return(c(NA, NA))
    se.mean = sqrt(var(x)/n)
    t.val = qt((1 - ci)/2, n - 1)
    mn = mean(x)
    lcl = mn + se.mean * t.val
    ucl = mn - se.mean * t.val
    c(lcl, ucl)
  }

  # Basic Statistics:
  nColumns = dim(y)[2]
  ans = NULL
  for (i in 1:nColumns) {
    X = y[, i]
    # Observations:
    X.length = length(X)
    X = X[!is.na(X)]
    X.na = X.length - length(X)
    # Basic Statistics:
    z = c(
      X.length, X.na, min(X), max(X),
      as.numeric(quantile(X, prob = 0.25, na.rm = TRUE)),
      as.numeric(quantile(X, prob = 0.75, na.rm = TRUE)),
      mean(X), median(X), sum(X), sqrt(var(X)/length(X)),
      cl.vals(X, ci)[1], cl.vals(X, ci)[2], var(X),
      sqrt(var(X)), sqrt(var(X))/mean(X))
    # Row Names:
    znames = c(
      "nobs", "NAs",  "Minimum", "Maximum",
      "1. Quartile",  "3. Quartile",  "Mean", "Median",
      "Sum",  "SE Mean", "LCL Mean", "UCL Mean",
      "Variance", "Stdev","RSD")
    # Output as data.frame
    result = matrix(z, ncol = 1)
    row.names(result) = znames
    ans = cbind(ans, result)
  }

  # Column Names:
  colnames(ans) = colnames(y)

  # Return Value:
  data.frame(round(ans, digits = 6))
}