trafficfam3 {gmfamm}

Draft of family for traffic example

Description

Fix four distributional assumptions and supply derivatives. Use gamma for speed data. Use negative binomial for count data.

Usage

trafficfam3(...)

Arguments

...

Not used.

Value

A bamlss family object.

Examples

# Construct data
set.seed(123)
# Number of subjects
n <- 10

# Number of observations
ni <- 3

# Covariate vector
x <- rep(rnorm(n), each = ni)
t <- rep(c(0, 0.5, 1), times = n)

# Additive predictor
eta_1 <- eta_2 <- eta_3 <- eta_4 <- t + 0.5*x

# Outcomes
y1 <- rnbinom(n*ni, exp(eta_1), 0.3)
y2 <- rnbinom(n*ni, exp(eta_2), 0.4)
y3 <- rgamma(n*ni, shape = 0.3, scale = exp(eta_3) / 0.3)
y4 <- rgamma(n*ni, shape = 0.4, scale = exp(eta_4) / 0.4)

# Data format
dat <- data.frame(
   id = factor(rep(seq_len(n), each = ni)),
   y = c(y1, y2, y3, y4),
   dim = factor(rep(c(1:4), each = n*ni)),
   t = t,
   x = x,
   fpc = 1
)

# Specify formula
f <- list(
  gm(y, dim) ~ t + x,
  sigma1 ~ 1,
  mu2 ~ t + x,
  sigma2 ~ 1,
  mu3 ~ t + x,
  sigma3 ~ 1,
  mu4 ~ t + x,
  sigma4 ~ 1,
  Lambda ~ -1 + s(id, by = fpc, bs = "re")
)

# Model
b <- bamlss(f, family = trafficfam3, n.iter = 20, burnin = 10,
            data = dat)