Plots a probability density function associated with a LMS quantile regression.
plotdeplot.lmscreg(answer, y.arg, add.arg = FALSE,
xlab = "", ylab = "density", xlim = NULL, ylim = NULL,
llty.arg = par()$lty, col.arg = par()$col,
llwd.arg = par()$lwd, ...)
Output from functions of the form
deplot.???
where ???
is the name of the
VGAM LMS family function, e.g., lms.yjn
.
See below for details.
Numerical vector. The values of the response variable at which to evaluate the density. This should be a grid that is fine enough to ensure the plotted curves are smooth.
Logical. Add the density to an existing plot?
Caption for the x- and y-axes. See par
.
Limits of the x- and y-axes. See par
.
Line type.
See the lty
argument of par
.
Line color.
See the col
argument of par
.
Line width.
See the lwd
argument of par
.
Arguments passed into the plot
function
when setting up the entire plot. Useful arguments here include
main
and las
.
The list answer
, which has components
The argument newdata
above from
the argument list of deplot.lmscreg
,
or a one-row
data frame constructed out of the x0
argument.
The argument y.arg
above.
Vector of the density function values evaluated at y.arg
.
The above graphical parameters offer some flexibility when plotting the quantiles.
Yee, T. W. (2004) Quantile regression via vector generalized additive models. Statistics in Medicine, 23, 2295--2315.
# NOT RUN {
fit <- vgam(BMI ~ s(age, df = c(4,2)), lms.bcn(zero = 1), bmi.nz)
# }
# NOT RUN {
y = seq(15, 43, by = 0.25)
deplot(fit, x0 = 20, y = y, xlab = "BMI", col = "green", llwd = 2,
main = "BMI distribution at ages 20 (green), 40 (blue), 60 (orange)")
deplot(fit, x0 = 40, y = y, add = TRUE, col = "blue", llwd = 2)
deplot(fit, x0 = 60, y = y, add = TRUE, col = "orange", llwd = 2) -> aa
names(aa@post$deplot)
aa@post$deplot$newdata
head(aa@post$deplot$y)
head(aa@post$deplot$density)
# }
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