gofTwoSample.object.Rd
Objects of S3 class "gofTwoSample"
are returned by the EnvStats function
gofTest
when both the x
and y
arguments are supplied.
Objects of S3 class "gofTwoSample"
are lists that contain
information about the assumed distribution, the estimated or
user-supplied distribution parameters, and the test statistic
and p-value.
Generic functions that have methods for objects of class
"gofTwoSample"
include: print
, plot
.
Required Components
The following components must be included in a legitimate list of
class "gofTwoSample"
.
a character string with the value "Equal"
.
a numeric scalar with a names attribute containing the name and value of the goodness-of-fit statistic.
a numeric scalar containing the number of non-missing observations in the sample used for the goodness-of-fit 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 containing the p-value associated with the goodness-of-fit statistic.
character string indicating the alternative hypothesis.
character string indicating the name of the goodness-of-fit test.
a list of length 2 containing the numeric vectors actually used for the goodness-of-fit test (i.e., the original data but with any missing or infinite values removed).
a character vector of length 2 indicating the name of the data
object used for the x
argument and the name of the data object used
for the y
argument in the goodness-of-fit test.
Optional Component
The following component is included when the arguments x
and/or y
contain missing (NA
), undefined (NaN
) and/or infinite
(Inf
, -Inf
) values.
numeric vector of length 2 indicating the number of missing (NA
),
undefined (NaN
) and/or infinite (Inf
, -Inf
)
values that were removed from the data in the x
and y
arguments
prior to performing the goodness-of-fit test.
Since objects of class "gofTwoSample"
are lists, you may extract
their components with the $
and [[
operators.
# Create an object of class "gofTwoSample", then print it out.
# Generate 20 observations from a normal distribution with mean=3 and sd=2, and
# generate 10 observaions from a normal distribution with mean=2 and sd=2 then
# test whether these sets of observations come from the same distribution.
# (Note: the call to set.seed simply allows you to reproduce this example.)
set.seed(300)
dat1 <- rnorm(20, mean = 3, sd = 2)
dat2 <- rnorm(10, mean = 1, sd = 2)
gofTest(x = dat1, y = dat2, test = "ks")
#>
#> Results of Goodness-of-Fit Test
#> -------------------------------
#>
#> Test Method: 2-Sample K-S GOF
#>
#> Hypothesized Distribution: Equal
#>
#> Data: x = dat1
#> y = dat2
#>
#> Sample Sizes: n.x = 20
#> n.y = 10
#>
#> Test Statistic: ks = 0.7
#>
#> Test Statistic Parameters: n = 20
#> m = 10
#>
#> P-value: 0.001669561
#>
#> Alternative Hypothesis: The cdf of 'dat1' does not equal
#> the cdf of 'dat2'.
#Results of Goodness-of-Fit Test
#-------------------------------
#
#Test Method: 2-Sample K-S GOF
#
#Hypothesized Distribution: Equal
#
#Data: x = dat1
# y = dat2
#
#Sample Sizes: n.x = 20
# n.y = 10
#
#Test Statistic: ks = 0.7
#
#Test Statistic Parameters: n = 20
# m = 10
#
#P-value: 0.001669561
#
#Alternative Hypothesis: The cdf of 'dat1' does not equal
# the cdf of 'dat2'.
#----------
# Clean up
rm(dat1, dat2)