shash: Sinh-arcsinh model family
In mfasiolo/mgcFam: Extra Families for mgcv

Description Usage Arguments Details Value Author(s) References Examples

View source: R/shash.R

The shash family implements the four-parameter sinh-arcsinh (shash) distribution of Jones and Pewsey (2009). The location, scale, skewness and kurtosis of the density can depend on additive smooth predictors. Useable only with gam, the linear predictors are specified via a list of formulae.

1 2	shash(link = list("identity", "logeb", "identity", "identity"), b = 1e-2, phiPen = 1e-3)

`link`	vector of four characters indicating the link function for location, scale, skewness and kurtosis parameters.
`b`	positive parameter of the logeb link function, see Details.
`phiPen`	positive multiplier of a ridge penalty on kurtosis parameter. Do not touch it unless you know what you are doing, see Details.

The density function of the shash family is

p(y|μ,σ,ε,δ)=C(z) exp{-S(z)^2/2} / σ{2π(1+z^2)}^1/2,

where C(z)={1+S(z)^2}^1/2 , S(z)=sinh{δ sinh^(-1)(z)-ε} and z=(y-μ)/(σδ). Here μ and σ > 0 control, respectively, location and scale, ε determines skewness, while δ > 0 controls tailweight. shash can model skewness to either side, depending on the sign of ε. Also, shash can have tails that are lighter (δ>1) or heavier (0<δ<1) that a normal. For fitting purposes, here we are using τ = log(σ) and φ = log(δ).

The link function used for τ is logeb with is η = log{exp(τ)-b} so that the inverse link is τ = log(σ) = log{exp(η)+b}. The point is that we are don't allow σ to become smaller than a small constant b. The likelihood includes a ridge penalty - phiPen * φ^2, which shrinks φ toward zero. When sufficient data is available the ridge penalty does not change the fit much, but it is useful to include it when fitting the model to small data sets, to avoid φ diverging to +infinity (a problem already identified by Jones and Pewsey (2009)).

An object inheriting from class general.family.

Matteo Fasiolo <matteo.fasiolo@gmail.com> and Simon N. Wood.

Jones, M. and A. Pewsey (2009). Sinh-arcsinh distributions. Biometrika 96 (4), 761<e2><80><93>780.

Wood, Simon N., Pya, N. and Safken, B. (2017). Smoothing parameter and model selection for general smooth models. Journal of the American Statistical Association.

#########
# Shash dataset
#########
##  Simulate some data form shash
set.seed(847)
n <- 1000
x <- seq(-4, 4, length.out = n)

X <- cbind(1, x, x^2)
beta <- c(4, 1, 1)
mu <- X %*% beta 

sigma =  .5+0.4*(x+4)*.5            # Scale
eps = 2*sin(x)                      # Skewness
del = 1 + 0.2*cos(3*x)              # Kurtosis

dat <-  mu + (del * sigma) * sinh((1/del) * asinh(qnorm(runif(n))) + (eps/del))
dataf <- data.frame(cbind(dat, x))
names(dataf) <- c("y", "x")
plot(x, dat, xlab = "x", ylab = "y")

## Fit model
fit <- gam(list(y ~ s(x), # <- model for location 
                  ~ s(x),   # <- model for log-scale
                  ~ s(x),   # <- model for skewness
                  ~ s(x, k = 20)), # <- model for log-kurtosis
           data = dataf, 
           family = shash, # <- new family 
           optimizer = "efs") # Here we are using a new optimizer 

## Plotting truth and estimates for each parameters of the density 
muE <- fit$fitted[ , 1]
sigE <- exp(fit$fitted[ , 2])
epsE <- fit$fitted[ , 3]
delE <- exp(fit$fitted[ , 4])

par(mfrow = c(2, 2))
plot(x, muE, type = 'l', ylab = expression(mu(x)), lwd = 2)
lines(x, mu, col = 2, lty = 2, lwd = 2)
legend("top", c("estimated", "truth"), col = 1:2, lty = 1:2, lwd = 2)

plot(x, sigE, type = 'l', ylab = expression(sigma(x)), lwd = 2)
lines(x, sigma, col = 2, lty = 2, lwd = 2)

plot(x, epsE, type = 'l', ylab = expression(epsilon(x)), lwd = 2)
lines(x, eps, col = 2, lty = 2, lwd = 2)

plot(x, delE, type = 'l', ylab = expression(delta(x)), lwd = 2)
lines(x, del, col = 2, lty = 2, lwd = 2)

## Plotting true and estimated conditional density
par(mfrow = c(1, 1))
plot(x, dat, pch = '.', col = "grey", ylab = "y", ylim = c(-35, 70))
for(qq in c(0.001, 0.01, 0.1, 0.5, 0.9, 0.99, 0.999)){
  est <- fit$family$qf(p=qq, mu = fit$fitted)
  true <- mu + (del * sigma) * sinh((1/del) * asinh(qnorm(qq)) + (eps/del))
  lines(x, est, type = 'l', col = 1, lwd = 2)
  lines(x, true, type = 'l', col = 2, lwd = 2, lty = 2)
}
legend("topleft", c("estimated", "truth"), col = 1:2, lty = 1:2, lwd = 2)

##########
## Motorcycle example
##########
# Here shash is an overkill, in fact the fit is not good, relative
# to what we would get with mgcv::gaulss
library(MASS)

b <- gam(list(accel~s(times, k=20, bs = "ad"), ~ s(times, k = 10), ~ 1, ~ 1),
         data=mcycle, family=shash)

par(mfrow = c(1, 1))
xSeq <- data.frame(cbind("accel" = rep(0, 1e3), "times" = seq(2, 58, length.out = 1e3)))
pred <- predict(b, newdata = xSeq)
plot(mcycle$times, mcycle$accel, ylim = c(-180, 100))
for(qq in c(0.1, 0.3, 0.5, 0.7, 0.9)){
  est <- b$family$qf(p=qq, mu = pred)
  lines(xSeq$times, est, type = 'l', col = 2)
}

plot(b, pages = 1, scale = FALSE)