dist.Inverse.Gamma | R Documentation |
This is the density function and random generation from the inverse gamma distribution.
dinvgamma(x, shape=1, scale=1, log=FALSE)
rinvgamma(n, shape=1, scale=1)
n |
This is the number of draws from the distribution. |
x |
This is the scalar location to evaluate density. |
shape |
This is the scalar shape parameter |
scale |
This is the scalar scale parameter |
log |
Logical. If |
Application: Continuous Univariate
Density: p(\theta) = \frac{\beta^\alpha}{\Gamma(\alpha)}
\theta^{-(\alpha + 1)} \exp(-\frac{\beta}{\theta}), \quad \theta > 0
Inventor: Unknown (to me, anyway)
Notation 1: \theta \sim \mathcal{G}^{-1}(\alpha, \beta)
Notation 2: p(\theta) = \mathcal{G}^{-1}(\theta | \alpha,
\beta)
Parameter 1: shape \alpha > 0
Parameter 2: scale \beta > 0
Mean: E(\theta) = \frac{\beta}{\alpha - 1}
, for \alpha > 1
Variance: var(\theta) = \frac{\beta^2}{(\alpha - 1)^2
(\alpha - 2)}, \alpha > 2
Mode: mode(\theta) = \frac{\beta}{\alpha + 1}
The inverse-gamma is the conjugate prior distribution for the normal
or Gaussian variance, and has been traditionally specified as a vague
prior in that application. The density is always finite; its integral is
finite if \alpha > 0
. Prior information decreases as
\alpha, \beta \rightarrow 0
.
These functions are similar to those in the MCMCpack
package.
dinvgamma
gives the density and
rinvgamma
generates random deviates. The parameterization
is consistent with the Gamma Distribution in the stats package.
dgamma
,
dnorm
,
dnormp
, and
dnormv
.
library(LaplacesDemon)
x <- dinvgamma(4.3, 1.1)
x <- rinvgamma(10, 3.3)
#Plot Probability Functions
x <- seq(from=0.1, to=20, by=0.1)
plot(x, dinvgamma(x,1,1), ylim=c(0,1), type="l", main="Probability Function",
ylab="density", col="red")
lines(x, dinvgamma(x,1,0.6), type="l", col="green")
lines(x, dinvgamma(x,0.6,1), type="l", col="blue")
legend(2, 0.9, expression(paste(alpha==1, ", ", beta==1),
paste(alpha==1, ", ", beta==0.6), paste(alpha==0.6, ", ", beta==1)),
lty=c(1,1,1), col=c("red","green","blue"))
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