deplot.lmscreg {VGAM} | R Documentation |
Plots a probability density function associated with a LMS quantile regression.
deplot.lmscreg(object, newdata = NULL, x0, y.arg, plot.it = TRUE, ...)
object |
A VGAM quantile regression model, i.e.,
an object produced by modelling functions such as |
newdata |
Optional data frame containing secondary variables such as sex. It should have a maximum of one row. The default is to use the original data. |
x0 |
Numeric. The value of the primary variable at which to make the ‘slice’. |
y.arg |
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. |
plot.it |
Logical. Plot it? If |
... |
Graphical parameter that are passed into
|
This function calls, e.g., deplot.lms.yjn
in order to compute
the density function.
The original object
but with a list
placed in the slot post
, called
@post$deplot
. The list has components
newdata |
The argument |
y |
The argument |
density |
Vector of the density function values evaluated at |
plotdeplot.lmscreg
actually does the plotting.
Thomas W. Yee
Yee, T. W. (2004) Quantile regression via vector generalized additive models. Statistics in Medicine, 23, 2295–2315.
Documentation accompanying the VGAM package at http://www.stat.auckland.ac.nz/~yee contains further information and examples.
plotdeplot.lmscreg
,
qtplot.lmscreg
,
lms.bcn
,
lms.bcg
,
lms.yjn
.
## Not run: fit = vgam(BMI ~ s(age, df=c(4,2)), fam=lms.bcn(zero=1), data=bminz) ygrid = seq(15, 43, by=0.25) deplot(fit, x0=20, y=ygrid, xlab="BMI", col="green", llwd=2, main="BMI distribution at ages 20 (green), 40 (blue), 60 (red)") deplot(fit, x0=40, y=ygrid, add=TRUE, col="blue", llwd=2) deplot(fit, x0=60, y=ygrid, add=TRUE, col="red", llwd=2) -> a names(a@post$deplot) a@post$deplot$newdata head(a@post$deplot$y) head(a@post$deplot$density) ## End(Not run)