The main aim of the gamlssx package is to enable a generalized extreme
value (GEV) to be used as the response distribution in a generalized
additive model for location scale and shape (GAMLSS), as implemented in
the gamlss R package. The
gamlss.dist R package
does offer reversed GEV distribution via in RGE family, but (a) this
is not the usual parameterization of a GEV distribution (for block
maxima), and (b) in RGE, the shape parameter is restricted to have a
particular sign, which is undesirable because the sign of the shape
parameter influences strongly extremal behaviour. The gamlssx package
uses the usual parameterization, with a shape parameter
See Rigby and Stasinopoulos (2005) and the gamlss home page for details of the GAMLSS methodology. See also Gavin Simpson’s blog post Modelling extremes using generalized additive models for an overview of the use of GAMs for modelling extreme values, which uses the mgcv R package to fit similar models. The VGAM and evgam R packages can also be used
We consider the fremantle data include in the gamlssx package, which
is a copy of data of the same name from the ismev R
package. These data contain
86 annual maximum seas levels recorded at Fremantle, Australia during
1987-1989. In addition to the year of each sea level, we have available
the value of the Southern Oscillation Index (SOI). We use the fitGEV()
function provided in gamlssx to fit a model to these data that is
similar to the first one fitted, to the same data, in Gavin Simpson’s
blog post.
The fitGEV() function calls the function gamlss::gamlss(), which
offers 3 fitting algorithms: RS (Rigby and Stasinopoulos), CG (Cole
and Green) and mixed (RS initially followed by CG). In the code
below, we use the default RS algorithm. fitGEV() offers 2 scoring
methods to calculate the weights used in the algorithm. Here, we use the
default, Fisher’s scoring, based on the expected Fisher information. The
code below does not do justice to the functionality of the gamlss
package. See the GAMLSS books
for more information.
# Load gamlss, for the function pb()
library(gamlss)
# Load gamlssx
library(gamlssx)
# Transform Year so that it is centred on 0
fremantle <- transform(fremantle, cYear = Year - median(Year))
# Plot sea level against year and against SOI
plot(fremantle$Year, fremantle$SeaLevel, xlab = "year", ylab = "sea level (m)")
plot(fremantle$SOI, fremantle$SeaLevel, xlab = "SOI", ylab = "sea level (m)")
# Fit a model with P-spline effects of cYear and SOI on location and scale
# The default links are identity for location and log for scale
mod <- fitGEV(SeaLevel ~ pb(cYear) pb(SOI),
sigma.formula = ~ pb(cYear) pb(SOI),
data = fremantle)
#> stepLength = 1
#> GAMLSS-RS iteration 1: Global Deviance = -112.2422
#> GAMLSS-RS iteration 2: Global Deviance = -117.4965
#> GAMLSS-RS iteration 3: Global Deviance = -118.3007
#> GAMLSS-RS iteration 4: Global Deviance = -118.6081
#> GAMLSS-RS iteration 5: Global Deviance = -118.7582
#> GAMLSS-RS iteration 6: Global Deviance = -118.8344
#> GAMLSS-RS iteration 7: Global Deviance = -118.8731
#> GAMLSS-RS iteration 8: Global Deviance = -118.8987
#> GAMLSS-RS iteration 9: Global Deviance = -118.9102
#> GAMLSS-RS iteration 10: Global Deviance = -118.9188
#> GAMLSS-RS iteration 11: Global Deviance = -118.9258
#> GAMLSS-RS iteration 12: Global Deviance = -118.9269
#> GAMLSS-RS iteration 13: Global Deviance = -118.9351
#> GAMLSS-RS iteration 14: Global Deviance = -118.9359# Summary of model fit
summary(mod)
#> ******************************************************************
#> Family: c("GEV", "Generalized Extreme Value")
#>
#> Call: gamlss::gamlss(formula = SeaLevel ~ pb(cYear) pb(SOI),
#> sigma.formula = ~pb(cYear) pb(SOI), family = GEVfisher(mu.link = "identity",
#> sigma.link = "log", nu.link = "identity"),
#> data = fremantle, mu.step = 1, sigma.step = 1, nu.step = 1)
#>
#> Fitting method: RS()
#>
#> ------------------------------------------------------------------
#> Mu link function: identity
#> Mu Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 1.5007933 0.0149733 100.231 < 2e-16 ***
#> pb(cYear) 0.0019490 0.0004982 3.912 0.000195 ***
#> pb(SOI) 0.0680347 0.0174751 3.893 0.000208 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> ------------------------------------------------------------------
#> Sigma link function: log
#> Sigma Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -2.128696 0.088447 -24.068 <2e-16 ***
#> pb(cYear) -0.004574 0.002614 -1.750 0.0841 .
#> pb(SOI) 0.275258 0.112736 2.442 0.0169 *
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> ------------------------------------------------------------------
#> Nu link function: identity
#> Nu Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -0.25619 0.08582 -2.985 0.00379 **
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> ------------------------------------------------------------------
#> NOTE: Additive smoothing terms exist in the formulas:
#> i) Std. Error for smoothers are for the linear effect only.
#> ii) Std. Error for the linear terms maybe are not accurate.
#> ------------------------------------------------------------------
#> No. of observations in the fit: 86
#> Degrees of Freedom for the fit: 8.375675
#> Residual Deg. of Freedom: 77.62432
#> at cycle: 14
#>
#> Global Deviance: -118.9359
#> AIC: -102.1846
#> SBC: -81.62773
#> ******************************************************************# Model diagnostic plots
plot(mod)
#> ******************************************************************
#> Summary of the Quantile Residuals
#> mean = -0.006109438
#> variance = 1.036755
#> coef. of skewness = 0.09394729
#> coef. of kurtosis = 2.322565
#> Filliben correlation coefficient = 0.9951795
#> ******************************************************************
# Plot of the fitted component smooth functions
# Note: gamlss::term.plot() does not include uncertainty about the intercept
# Location mu
term.plot(mod, rug = TRUE, pages = 1)
# Scale sigma
term.plot(mod, what = "sigma", rug = TRUE, pages = 1)
To get the current released version from CRAN:
install.packages("gamlssx")