Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/zTestGevdShape.R

Estimate the shape parameter of a generalized extreme value distribution and test the null hypothesis that the true value is equal to 0.

1 2 | ```
zTestGevdShape(x, pwme.method = "unbiased",
plot.pos.cons = c(a = 0.35, b = 0), alternative = "two.sided")
``` |

`x` |
numeric vector of observations.
Missing ( |

`pwme.method` |
character string specifying the method of estimating the probability-weighted
moments. Possible values are |

`plot.pos.cons` |
numeric vector of length 2 specifying the constants used in the formula for the
plotting positions. The default value is |

`alternative` |
character string indicating the kind of alternative hypothesis. The possible values
are |

Let *\underline{x} = x_1, x_2, …, x_n* be a vector of *n* observations
from a generalized extreme value distribution with parameters
`location=`

*η*, `scale=`

*θ*, and `shape=`

*κ*.
Furthermore, let *\hat{κ}_{pwme}* denote the probability-weighted moments
estimator (PWME) of the shape parameter *κ* (see the help file for
`egevd`

). Then the statistic

*z = \frac{\hat{κ}_{pwme}}{√{0.5633/n}} \;\;\;\;\;\; (1)*

is asymptotically distributed as a N(0,1) random variable under the null hypothesis
*H_0: κ = 0* (Hosking et al., 1985). The function `zTestGevdShape`

performs the usual one-sample z-test using the statistic computed in Equation (1).
The PWME of *κ* may be computed using either U-statistic type
probability-weighted moments estimators or plotting-position type estimators
(see `egevd`

). Although Hosking et al. (1985) base their statistic on
plotting-position type estimators, Hosking and Wallis (1995) recommend using the
U-statistic type estimators for almost all applications.

This test is only asymptotically correct. Hosking et al. (1985), however, found
that the *α*-level is adequately maintained for samples as small as
*n = 25*.

A list of class `"htest"`

containing the results of the hypothesis test.
See the help file for `htest.object`

for details.

Two-parameter extreme value distributions (EVD) have been applied extensively since the 1930's to several fields of study, including the distributions of hydrological and meteorological variables, human lifetimes, and strength of materials. The three-parameter generalized extreme value distribution (GEVD) was introduced by Jenkinson (1955) to model annual maximum and minimum values of meteorological events. Since then, it has been used extensively in the hydological and meteorological fields.

The three families of EVDs are all special kinds of GEVDs. When the shape parameter
*κ = 0*, the GEVD reduces to the Type I extreme value (Gumbel) distribution.
When *κ > 0*, the GEVD is the same as the Type II extreme value
distribution, and when *κ < 0* it is the same as the Type III extreme value
distribution.

Hosking et al. (1985) introduced the test used by the function `zTestGevdShape`

to test the null hypothesis *H_0: κ = 0*. They found this test has power
comparable to the modified likelihood-ratio test, which was found by Hosking (1984)
to be the best overall test the thirteen tests he considered.

Fill and Stedinger (1995) denote this test the “kappa test” and compare it
with the *L-C*s test suggested by Chowdhury et al. (1991) and the probability
plot correlation coefficient goodness-of-fit test for the Gumbel distribution given
by Vogel (1986) (see the sub-section for `test="ppcc"`

under the Details section
of the help file for `gofTest`

).

Steven P. Millard ([email protected])

Chowdhury, J.U., J.R. Stedinger, and L. H. Lu. (1991). Goodness-of-Fit Tests for
Regional Generalized Extreme Value Flood Distributions.
*Water Resources Research* **27**(7), 1765–1776.

Fill, H.D., and J.R. Stedinger. (1995). L Moment and Probability Plot Correlation
Coefficient Goodness-of-Fit Tests for the Gumbel Distribution and Impact of
Autocorrelation. *Water Resources Research* **31**(1), 225–229.

Hosking, J.R.M. (1984). Testing Whether the Shape Parameter is Zero in the
Generalized Extreme-Value Distribution. *Biometrika* **71**(2), 367–374.

Hosking, J.R.M., and J.R. Wallis (1995). A Comparison of Unbiased and
Plotting-Position Estimators of L Moments.
*Water Resources Research* **31**(8), 2019–2025.

Hosking, J.R.M., J.R. Wallis, and E.F. Wood. (1985). Estimation of the
Generalized Extreme-Value Distribution by the Method of Probability-Weighted
Moments. *Technometrics* **27**(3), 251–261.

Jenkinson, A.F. (1955). The Frequency Distribution of the Annual Maximum
(or Minimum) of Meteorological Events. *Quarterly Journal of the Royal
Meteorological Society* **81**, 158–171.

Vogel, R.M. (1986). The Probability Plot Correlation Coefficient Test for the
Normal, Lognormal, and Gumbel Distributional Hypotheses.
*Water Resources Research* **22**(4), 587–590.
(Correction, *Water Resources Research* **23**(10), 2013, 1987.)

GEVD, `egevd`

, EVD, `eevd`

,
Goodness-of-Fit Tests, `htest.object`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ```
# Generate 25 observations from a generalized extreme value distribution with
# parameters location=2, scale=1, and shape=1, and test the null hypothesis
# that the shape parameter is equal to 0.
# (Note: the call to set.seed simply allows you to reproduce this example.)
set.seed(250)
dat <- rgevd(25, location = 2, scale = 1, shape = 1)
zTestGevdShape(dat)
#Results of Hypothesis Test
#--------------------------
#
#Null Hypothesis: shape = 0
#
#Alternative Hypothesis: True shape is not equal to 0
#
#Test Name: Z-test of shape=0 for GEVD
#
#Estimated Parameter(s): shape = 0.6623014
#
#Estimation Method: Unbiased pwme
#
#Data: dat
#
#Sample Size: 25
#
#Test Statistic: z = 4.412206
#
#P-value: 1.023225e-05
#----------
# Clean up
#---------
rm(dat)
``` |

EnvStats documentation built on Oct. 10, 2017, 1:05 a.m.

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