qtplot.lmscreg: Quantile Plot for LMS Quantile Regression

In VGAM: Vector Generalized Linear and Additive Models

Quantile Plot for LMS Quantile Regression

Description

Plots quantiles associated with a LMS quantile regression.

Usage

qtplot.lmscreg(object, newdata = NULL,
               percentiles = object@misc$percentiles,
               show.plot = TRUE, ...)

Arguments

object	A VGAM quantile regression model, i.e., an object produced by modelling functions such as vglm and vgam with a family function beginning with "lms.", e.g., lms.yjn.
newdata	Optional data frame for computing the quantiles. If missing, the original data is used.
percentiles	Numerical vector with values between 0 and 100 that specify the percentiles (quantiles). The default are the percentiles used when the model was fitted.
show.plot	Logical. Plot it? If FALSE no plot will be done.
...	Graphical parameter that are passed into plotqtplot.lmscreg.

Details

The ‘primary’ variable is defined as the main covariate upon which the regression or smoothing is performed. For example, in medical studies, it is often the age. In VGAM, it is possible to handle more than one covariate, however, the primary variable must be the first term after the intercept.

Value

A list with the following components.

fitted.values	A vector of fitted percentile values.
percentiles	The percentiles used.

Note

plotqtplot.lmscreg does the actual plotting.

Author(s)

Thomas W. Yee

References

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

See Also

plotqtplot.lmscreg, deplot.lmscreg, lms.bcn, lms.bcg, lms.yjn.

Examples

## Not run: 
fit <- vgam(BMI ~ s(age, df = c(4, 2)), lms.bcn(zero=1), bmi.nz)
qtplot(fit)
qtplot(fit, perc = c(25, 50, 75, 95), lcol = 4, tcol = 4, llwd = 2)

## End(Not run)

VGAM documentation built on Sept. 18, 2024, 9:09 a.m.