S3 Class "gofTwoSample"
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.
Required Components
The following components must be included in a legitimate list of
class "gofTwoSample"
.
distribution |
a character string with the value |
statistic |
a numeric scalar with a names attribute containing the name and value of the goodness-of-fit statistic. |
sample.size |
a numeric scalar containing the number of non-missing observations in the sample used for the goodness-of-fit 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 |
p.value |
numeric scalar containing the p-value associated with the goodness-of-fit statistic. |
alternative |
character string indicating the alternative hypothesis. |
method |
character string indicating the name of the goodness-of-fit test. |
data |
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). |
data.name |
a character vector of length 2 indicating the name of the data
object used for the |
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.
bad.obs |
numeric vector of length 2 indicating the number of missing ( |
Since objects of class "gofTwoSample"
are lists, you may extract
their components with the $
and [[
operators.
Steven P. Millard (EnvStats@ProbStatInfo.com)
# 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'. #---------- # Clean up rm(dat1, dat2)
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