library("gamlss2")
data("film90", package = "gamlss.data")
## model formula
f <- ~ s(lboopen)
f <- rep(list(f), 4)
f[[1]] <- update(f[[1]], lborev1 ~ .)
## estimate model
b <- gamlss2(f, data = film90, family = BCPE)
## compute quantiles using "newdata"
nd <- film90[1:10, ]
print(quantile(b, newdata = nd))
## plot sorted quantiles
quantile(b, plot = TRUE)
## quantile plot using covariate data
quantile(b, plot = TRUE, variable = TRUE)
## plot without raw data
quantile(b, plot = TRUE, variable = TRUE, data = FALSE)Quantiles for GAMLSS
Description
The function computes estimated quantiles and optionally produces a plot.
Usage
## S3 method for class 'gamlss2'
quantile(x, probs = c(0.025, 0.25, 0.50, 0.75, 0.975),
variable = NULL, newdata = NULL,
plot = FALSE, data = TRUE,
n = 100L, ...)
Arguments
x
|
An object of class “gamlss2”.
|
probs
|
Numeric vector of probabilities with values in [0,1]. |
variable
|
Logical or integer, should quantiles be plotted using the covariate data? Note that the variable option is only possible for single covariate models. |
newdata
|
Data frame that should be used for computing the quantiles. |
plot
|
Logical, should a plot be shown? |
data
|
Logical, should the raw data be added to the plot? |
n
|
Integer, number of observations that should be used to compute an equidistant grid for the selected variable.
|
…
|
Arguments such as col, legend = TRUE/FALSE. See the examples.
|
Details
The function applies the predict method to determine the parameters of the response distribution. It then computes the quantiles as specified in the argument probs.
Value
A data frame of the estimated quantiles.
See Also
gamlss2.