The Gamma Distribution (Alternative Parameterization)
Density, distribution function, quantile function, and random generation
for the gamma distribution with parameters mean
and cv
.
dgammaAlt(x, mean, cv = 1, log = FALSE) pgammaAlt(q, mean, cv = 1, lower.tail = TRUE, log.p = FALSE) qgammaAlt(p, mean, cv = 1, lower.tail = TRUE, log.p = FALSE) rgammaAlt(n, mean, cv = 1)
x |
vector of quantiles. |
q |
vector of quantiles. |
p |
vector of probabilities between 0 and 1. |
n |
sample size. If |
mean |
vector of (positive) means of the distribution of the random variable. |
cv |
vector of (positive) coefficients of variation of the random variable. |
log, log.p |
logical; if |
lower.tail |
logical; if |
Let X be a random variable with a gamma distribution with parameters
shape=
α and scale=
β. The relationship
between these parameters and the mean (mean=
μ) and coefficient
of variation (cv=
τ) of this distribution is given by:
α = τ^{-2} \;\;\;\;\;\; (1)
β = μ/α \;\;\;\;\;\; (2)
μ = αβ \;\;\;\;\;\; (3)
τ = α^{-1/2} \;\;\;\;\;\; (4)
dgammaAlt
gives the density, pgammaAlt
gives the distribution function,
qgammaAlt
gives the quantile function, and rgammaAlt
generates random
deviates.
Invalid arguments will result in return value NaN
, with a warning.
The gamma distribution takes values on the positive real line. Special cases of the gamma are the exponential distribution and the chi-square distribution. Applications of the gamma include life testing, statistical ecology, queuing theory, inventory control and precipitation processes. A gamma distribution starts to resemble a normal distribution as the shape parameter α tends to infinity or the cv parameter τ tends to 0.
Some EPA guidance documents (e.g., Singh et al., 2002; Singh et al., 2010a,b) discourage using the assumption of a lognormal distribution for some types of environmental data and recommend instead assessing whether the data appear to fit a gamma distribution.
Steven P. Millard (EnvStats@ProbStatInfo.com)
Forbes, C., M. Evans, N. Hastings, and B. Peacock. (2011). Statistical Distributions, Fourth Edition. John Wiley and Sons, Hoboken, NJ.
Johnson, N. L., S. Kotz, and N. Balakrishnan. (1994). Continuous Univariate Distributions, Volume 1. Second Edition. John Wiley and Sons, New York.
Singh, A., A.K. Singh, and R.J. Iaci. (2002). Estimation of the Exposure Point Concentration Term Using a Gamma Distribution. EPA/600/R-02/084. October 2002. Technology Support Center for Monitoring and Site Characterization, Office of Research and Development, Office of Solid Waste and Emergency Response, U.S. Environmental Protection Agency, Washington, D.C.
Singh, A., R. Maichle, and N. Armbya. (2010a). ProUCL Version 4.1.00 User Guide (Draft). EPA/600/R-07/041, May 2010. Office of Research and Development, U.S. Environmental Protection Agency, Washington, D.C.
Singh, A., N. Armbya, and A. Singh. (2010b). ProUCL Version 4.1.00 Technical Guide (Draft). EPA/600/R-07/041, May 2010. Office of Research and Development, U.S. Environmental Protection Agency, Washington, D.C.
# Density of a gamma distribution with parameters mean=10 and cv=2, # evaluated at 7: dgammaAlt(7, mean = 10, cv = 2) #[1] 0.02139335 #---------- # The cdf of a gamma distribution with parameters mean=10 and cv=2, # evaluated at 12: pgammaAlt(12, mean = 10, cv = 2) #[1] 0.7713307 #---------- # The 25'th percentile of a gamma distribution with parameters # mean=10 and cv=2: qgammaAlt(0.25, mean = 10, cv = 2) #[1] 0.1056871 #---------- # A random sample of 4 numbers from a gamma distribution with # parameters mean=10 and cv=2. # (Note: the call to set.seed simply allows you to reproduce this example.) set.seed(10) rgammaAlt(4, mean = 10, cv = 2) #[1] 3.772004230 1.889028078 0.002987823 8.179824976
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