gofOutlier.object.RdObjects of S3 class "gofOutlier" are returned by the EnvStats function
rosnerTest.
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.
Generic functions that have methods for objects of class
"gofOutlier" include: print.
Required Components
The following components must be included in a legitimate list of
class "gofOutlier".
a character string indicating the name of the
assumed distribution (see Distribution.df).
a numeric vector with a names attribute containing the names and values of the outlier test statistic for each outlier tested.
a numeric scalar containing the number of non-missing observations in the sample used for the outlier test.
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.
numeric scalar indicating the Type I error level.
numeric vector containing the critical values associated with the test for each outlier.
character string indicating the alternative hypothesis.
character string indicating the name of the outlier test.
numeric vector containing the data actually used for the outlier test (i.e., the original data without any missing or infinite values).
character string indicating the name of the data object used for the goodness-of-fit test.
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.
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.
Since objects of class "gofOutlier" are lists, you may extract
their components with the $ and [[ operators.
# 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"
#[1] "list"
class(gofOutlier.obj)
#> [1] "gofOutlier"
#[1] "gofOutlier"
names(gofOutlier.obj)
#> [1] "distribution" "statistic" "sample.size" "parameters" "alpha"
#> [6] "crit.value" "n.outliers" "alternative" "method" "data"
#> [11] "data.name" "bad.obs" "all.stats"
# [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
#>
#>
#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
# 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)