R: Cumulative Distribution Function for LMS Quantile Regression

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 `vglm` and `vgam` with a family function beginning with `"lms."`.
`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 `cdf.lms.yjn`.

### 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`.

### 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)))

[Package VGAM version 1.1-3 Index]