```
### generate data
set.seed(1907)
x1 <- rnorm(1000)
x2 <- rnorm(1000)
x3 <- rnorm(1000)
x4 <- rnorm(1000)
x5 <- rnorm(1000)
x6 <- rnorm(1000)
mu <- exp(1.5 + x1^2 +0.5 * x2 - 3 * sin(x3) -1 * x4)
sigma <- exp(-0.2 * x4 +0.2 * x5 +0.4 * x6)
y <- numeric(1000)
for( i in 1:1000)
y[i] <- rnbinom(1, size = sigma[i], mu = mu[i])
dat <- data.frame(x1, x2, x3, x4, x5, x6, y)
### fit a model
model <- gamboostLSS(y ~ ., families = NBinomialLSS(), data = dat,
control = boost_control(mstop = 100))
### Do not test the following line per default on CRAN as it takes some time to run:
### use a model with more iterations for a better fit
mstop(model) <- 400
### extract coefficients
coef(model)
### only for distribution parameter mu
coef(model, parameter = "mu")
### only for covariate x1
coef(model, which = "x1")
### plot complete model
par(mfrow = c(4, 3))
plot(model)
### plot first parameter only
par(mfrow = c(2, 3))
plot(model, parameter = "mu")
### now plot only effect of x3 of both parameters
par(mfrow = c(1, 2))
plot(model, which = "x3")
### first component second parameter (sigma)
par(mfrow = c(1, 1))
plot(model, which = 1, parameter = 2)
### Do not test the following code per default on CRAN as it takes some time to run:
### plot marginal prediction interval
pi <- predint(model, pi = 0.9, which = "x1")
pi <- predint(model, pi = c(0.8, 0.9), which = "x1")
plot(pi, log = "y") # warning as some y values are below 0
## here it would be better to plot x1 against
## sqrt(y) and sqrt(pi)
### set model to mstop = 300 (one-dimensional)
mstop(model) <- 300
### END (don't test automatically)
par(mfrow = c(2, 2))
plot(risk(model, parameter = "mu")[[1]])
plot(risk(model, parameter = "sigma")[[1]])
### Do not test the following code per default on CRAN as it takes some time to run:
### get back to orignal fit
mstop(model) <- 400
plot(risk(model, parameter = "mu")[[1]])
plot(risk(model, parameter = "sigma")[[1]])
### use different mstop values for the components
mstop(model) <- c(100, 200)
## same as
mstop(model) <- c(mu = 100, sigma = 200)
## or
mstop(model) <- list(mu = 100, sigma = 200)
## or
mstop(model) <- list(100, 200)
plot(risk(model, parameter = "mu")[[1]])
plot(risk(model, parameter = "sigma")[[1]])
### END (don't test automatically)
```

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