EnvStats (version 2.1.0)

gofOutlier.object: S3 Class "gofOutlier"

Description

Objects of S3 class "gofOutlier" are returned by the EnvStats function rosnerTest.

Value

• Required Components The following components must be included in a legitimate list of class "gofOutlier".
• distribution
  a character string indicating the name of the assumed distribution (see Distribution.df).
• statistic
  a numeric vector with a names attribute containing the names and values of the outlier test statistic for each outlier tested.
• sample.size
  a numeric scalar containing the number of non-missing observations in the sample used for the outlier test.
• parameters
  numeric vector with a names attribute containing the name(s) and value(s) of the parameter(s) associated with the test statistic given in the statistic component.
• alpha
  numeric scalar indicating the Type I error level.
• crit.value
  numeric vector containing the critical values associated with the test for each outlier.
• alternative
  character string indicating the alternative hypothesis.
• method
  character string indicating the name of the outlier test.
• data
  numeric vector containing the data actually used for the outlier test (i.e., the original data without any missing or infinite values).
• data.name
  character string indicating the name of the data object used for the goodness-of-fit test.
• all.stats
  data frame containing all of the results of the test.
• Optional Components The following component is included when the data object contains missing (NA), undefined (NaN) and/or infinite (Inf, -Inf) values.
• bad.obs
  numeric scalar indicating the number of missing (NA), undefined (NaN) and/or infinite (Inf, -Inf) values that were removed from the data object prior to performing the test for outliers.

Methods

Generic functions that have methods for objects of class "gofOutlier" include: print.

Details

Objects of S3 class "gofOutlier" are lists that contain information about the assumed distribution, the test statistics, the Type I error level, and the number of outliers detected.

See Also

rosnerTest, print.gofOutlier, Goodness-of-Fit Tests.

Examples

# Create an object of class "gofOutlier", then print it out. 
  # (Note: the call to set.seed simply allows you to reproduce 
  # this example.)

  set.seed(250) 

  dat <- c(rnorm(30, mean = 3, sd = 2), rnorm(3, mean = 10, sd = 1)) 

  gofOutlier.obj <- rosnerTest(dat, k = 4) 

  mode(gofOutlier.obj) 
  #[1] "list" 

  class(gofOutlier.obj) 
  #[1] "gofOutlier" 

  names(gofOutlier.obj) 
  # [1] "distribution" "statistic"    "sample.size"  "parameters"  
  # [5] "alpha"        "crit.value"   "n.outliers"   "alternative" 
  # [9] "method"       "data"         "data.name"    "bad.obs"     
  #[13] "all.stats"

  gofOutlier.obj 

  #Results of Outlier Test
  #-------------------------
  #
  #Test Method:                     Rosner's Test for Outliers
  #
  #Hypothesized Distribution:       Normal
  #
  #Data:                            dat
  #
  #Sample Size:                     33
  #
  #Test Statistics:                 R.1 = 2.848514
  #                                 R.2 = 3.086875
  #                                 R.3 = 3.033044
  #                                 R.4 = 2.380235
  #
  #Test Statistic Parameter:        k = 4
  #
  #Alternative Hypothesis:          Up to 4 observations are not
  #                                 from the same Distribution.
  #
  #Type I Error:                    5%
  #
  #Number of Outliers Detected:     3
  #
  #  i   Mean.i     SD.i      Value Obs.Num    R.i+1 lambda.i+1 Outlier
  #1 0 3.549744 2.531011 10.7593656      33 2.848514   2.951949    TRUE
  #2 1 3.324444 2.209872 10.1460427      31 3.086875   2.938048    TRUE
  #3 2 3.104392 1.856109  8.7340527      32 3.033044   2.923571    TRUE
  #4 3 2.916737 1.560335 -0.7972275      25 2.380235   2.908473   FALSE

  #==========

  # Extract the data frame with all the test results
  #-------------------------------------------------

  gofOutlier.obj$all.stats
  #  i   Mean.i     SD.i      Value Obs.Num    R.i+1 lambda.i+1 Outlier
  #1 0 3.549744 2.531011 10.7593656      33 2.848514   2.951949    TRUE
  #2 1 3.324444 2.209872 10.1460427      31 3.086875   2.938048    TRUE
  #3 2 3.104392 1.856109  8.7340527      32 3.033044   2.923571    TRUE
  #4 3 2.916737 1.560335 -0.7972275      25 2.380235   2.908473   FALSE

  #==========

  # Clean up
  #---------
  rm(dat, gofOutlier.obj)