Density Plot for LMS Quantile Regression

Plots a probability density function associated with a LMS quantile regression.

Usage

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, ...)

Arguments

answer

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.

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.

add.arg

Logical. Add the density to an existing plot?

xlab, ylab

Caption for the x- and y-axes. See par.

xlim, ylim

Limits of the x- and y-axes. See par.

llty.arg

Line type. See the lty argument of par.

col.arg

Line color. See the col argument of par.

llwd.arg

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.

Details

The above graphical parameters offer some flexibility when plotting the quantiles.

Value

The list answer, which has components

newdata

The argument newdata above from the argument list of deplot.lmscreg, or a one-row data frame constructed out of the x0 argument.

y

The argument y.arg above.

density

Vector of the density function values evaluated at y.arg.

References

Yee, T. W. (2004). Quantile regression via vector generalized additive models. Statistics in Medicine, 23, 2295–2315.

Author

Thomas W. Yee

Note

While the graphical arguments of this function are useful to the user, this function should not be called directly.

Examples

fit <- vgam(BMI ~ s(age, df = c(4,2)), lms.bcn(zero = 1), bmi.nz)
#> VGAM  s.vam  loop  1 :  loglikelihood = -6429.7568
#> VGAM  s.vam  loop  2 :  loglikelihood = -6327.3502
#> VGAM  s.vam  loop  3 :  loglikelihood = -6313.2224
#> VGAM  s.vam  loop  4 :  loglikelihood = -6312.8069
#> VGAM  s.vam  loop  5 :  loglikelihood = -6312.8166
#> VGAM  s.vam  loop  6 :  loglikelihood = -6312.8032
#> VGAM  s.vam  loop  7 :  loglikelihood = -6312.8088
#> VGAM  s.vam  loop  8 :  loglikelihood = -6312.8062
#> VGAM  s.vam  loop  9 :  loglikelihood = -6312.8074
#> VGAM  s.vam  loop  10 :  loglikelihood = -6312.8068
#> VGAM  s.vam  loop  11 :  loglikelihood = -6312.8071
#> VGAM  s.vam  loop  12 :  loglikelihood = -6312.807
if (FALSE)  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)")
#> Error: object 'y' not found
deplot(fit, x0 = 40, y = y, add = TRUE, col = "blue", llwd = 2)
#> Error: object 'y' not found
deplot(fit, x0 = 60, y = y, add = TRUE, col = "orange", llwd = 2) -> aa
#> Error: object 'y' not found

names(aa@post$deplot)
#> Error: object 'aa' not found
aa@post$deplot$newdata
#> Error: object 'aa' not found
head(aa@post$deplot$y)
#> Error: object 'aa' not found
head(aa@post$deplot$density)  # \dontrun{}
#> Error: object 'aa' not found