rig: Generate inverse Gaussian random deviates

In mgcv: Mixed GAM Computation Vehicle with Automatic Smoothness Estimation

Generate inverse Gaussian random deviates

Description

Generates inverse Gaussian random deviates.

Usage

rig(n,mean,scale)

Arguments

n	the number of deviates required. If this has length > 1 then the length is taken as the number of deviates required.
mean	vector of mean values.
scale	vector of scale parameter values (lambda, see below)

Details

If x if the returned vector, then E(x) = mean while var(x) = scale*mean^3. For density and distribution functions see the statmod package. The algorithm used is Algorithm 5.7 of Gentle (2003), based on Michael et al. (1976). Note that scale here is the scale parameter in the GLM sense, which is the reciprocal of the usual ‘lambda’ parameter.

Value

A vector of inverse Gaussian random deviates.

Author(s)

Simon N. Wood [e-mail]

References

Gentle, J.E. (2003) Random Number Generation and Monte Carlo Methods (2nd ed.) Springer.

Michael, J.R., W.R. Schucany & R.W. Hass (1976) Generating random variates using transformations with multiple roots. The American Statistician 30, 88-90.

https://www.maths.ed.ac.uk/~swood34/

Examples

require(mgcv)
set.seed(7)
## An inverse.gaussian GAM example, by modify `gamSim' output... 
dat <- gamSim(1,n=400,dist="normal",scale=1)
dat$f <- dat$f/4 ## true linear predictor 
Ey <- exp(dat$f);scale <- .5 ## mean and GLM scale parameter
## simulate inverse Gaussian response...
dat$y <- rig(Ey,mean=Ey,scale=.2)
big <- gam(y~ s(x0)+ s(x1)+s(x2)+s(x3),family=inverse.gaussian(link=log),
          data=dat,method="REML")
plot(big,pages=1)
gam.check(big)
summary(big)

mgcv documentation built on May 29, 2024, 4:34 a.m.