The `gam`

modelling function is designed to be able to use
the `negbin`

family (a modification of MASS library `negative.binomial`

family
by Venables and Ripley), or the `nb`

function designed for integrated estimation of
parameter `theta`

. *θ* is the parameter such that *var(y) = μ + μ^2/θ*, where *μ = E(y)*.

Two approaches to estimating `theta`

are available (with `gam`

only):

With

`negbin`

then if ‘performance iteration’ is used for smoothing parameter estimation (see`gam`

), then smoothing parameters are chosen by GCV and`theta`

is chosen in order to ensure that the Pearson estimate of the scale parameter is as close as possible to 1, the value that the scale parameter should have.If ‘outer iteration’ is used for smoothing parameter selection with the

`nb`

family then`theta`

is estimated alongside the smoothing parameters by ML or REML.

To use the first option, set the `optimizer`

argument of `gam`

to `"perf"`

(it can sometimes fail to converge).

1 2 |

`theta` |
Either i) a single value known value of theta or ii) two values of theta specifying the
endpoints of an interval over which to search for theta (this is an option only for |

`link` |
The link function: one of |

`nb`

allows estimation of the `theta`

parameter alongside the model smoothing parameters, but is only useable with `gam`

(not `bam`

or `gamm`

).

For `negbin`

, if a single value of `theta`

is supplied then it is always taken as the known fixed value and this is useable with `bam`

and `gamm`

. If `theta`

is two
numbers (`theta[2]>theta[1]`

) then they are taken as specifying the range of values over which to search for
the optimal theta. This option should only be used with performance iteration estimation (see `gam`

argument `optimizer`

), in which case the method
of estimation is to choose *theta* so that the GCV (Pearson) estimate
of the scale parameter is one (since the scale parameter
is one for the negative binomial). In this case *theta* estimation is nested within the IRLS loop
used for GAM fitting. After each call to fit an iteratively weighted additive model to the IRLS pseudodata,
the *theta* estimate is updated. This is done by conditioning on all components of the current GCV/Pearson
estimator of the scale parameter except *theta* and then searching for the
*theta* which equates this conditional estimator to one. The search is
a simple bisection search after an initial crude line search to bracket one. The search will
terminate at the upper boundary of the search region is a Poisson fit would have yielded an estimated
scale parameter <1.

For `negbin`

an object inheriting from class `family`

, with additional elements

`dvar` |
the function giving the first derivative of the variance function w.r.t. |

`d2var` |
the function giving the second derivative of the variance function w.r.t. |

`getTheta` |
A function for retrieving the value(s) of theta. This also useful for retriving the
estimate of |

For `nb`

an object inheriting from class `extended.family`

.

`gamm`

and `bam`

do not support `theta`

estimation

The negative binomial functions from the MASS library are no longer supported.

Simon N. Wood simon.wood@r-project.org
modified from Venables and Ripley's `negative.binomial`

family.

Wood, S.N., N. Pya and B. Saefken (2016), Smoothing parameter and model selection for general smooth models. Journal of the American Statistical Association. http://arxiv.org/abs/1511.03864

Venables, B. and B.R. Ripley (2002) Modern Applied Statistics in S, Springer.

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 | ```
library(mgcv)
set.seed(3)
n<-400
dat <- gamSim(1,n=n)
g <- exp(dat$f/5)
## negative binomial data...
dat$y <- rnbinom(g,size=3,mu=g)
## known theta fit ...
b0 <- gam(y~s(x0)+s(x1)+s(x2)+s(x3),family=negbin(3),data=dat)
plot(b0,pages=1)
print(b0)
## same with theta estimation...
b <- gam(y~s(x0)+s(x1)+s(x2)+s(x3),family=nb(),data=dat)
plot(b,pages=1)
print(b)
b$family$getTheta(TRUE) ## extract final theta estimate
## unknown theta via performance iteration...
b1 <- gam(y~s(x0)+s(x1)+s(x2)+s(x3),family=negbin(c(1,10)),
optimizer="perf",data=dat)
plot(b1,pages=1)
print(b1)
## another example...
set.seed(1)
f <- dat$f
f <- f - min(f)+5;g <- f^2/10
dat$y <- rnbinom(g,size=3,mu=g)
b2 <- gam(y~s(x0)+s(x1)+s(x2)+s(x3),family=nb(link="sqrt"),
data=dat,method="REML")
plot(b2,pages=1)
print(b2)
rm(dat)
``` |

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