| cdf.lmscreg {VGAM} | R Documentation |
Cumulative Distribution Function for LMS Quantile Regression
Description
Computes the cumulative distribution function (CDF) for observations, based on a LMS quantile regression.
Usage
cdf.lmscreg(object, newdata = NULL, ...)
Arguments
object |
A VGAM quantile regression model, i.e.,
an object produced by modelling functions such as
|
newdata |
Data frame where the predictions are to be made. If missing, the original data is used. |
... |
Parameters which are passed into functions such as
|
Details
The CDFs returned here are values lying in [0,1] giving
the relative probabilities associated with the quantiles
newdata. For example, a value near 0.75 means it is
close to the upper quartile of the distribution.
Value
A vector of CDF values lying in [0,1].
Note
The data are treated like quantiles, and the
percentiles are returned. The opposite is performed by
qtplot.lmscreg.
The CDF values of the model have been placed in
@post$cdf when the model was fitted.
Author(s)
Thomas W. Yee
References
Yee, T. W. (2004). Quantile regression via vector generalized additive models. Statistics in Medicine, 23, 2295–2315.
See Also
deplot.lmscreg,
qtplot.lmscreg,
lms.bcn,
lms.bcg,
lms.yjn,
CommonVGAMffArguments.
Examples
fit <- vgam(BMI ~ s(age, df=c(4, 2)), lms.bcn(zero = 1), data = bmi.nz)
head(fit@post$cdf)
head(cdf(fit)) # Same
head(depvar(fit))
head(fitted(fit))
cdf(fit, data.frame(age = c(31.5, 39), BMI = c(28.4, 24)))