R: Quantile Plot for LMS Quantile Regression

qtplot.lmscreg {VGAM} | R Documentation

## 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), data = bmi.nz)
    qtplot(fit)
    qtplot(fit, perc = c(25, 50, 75, 95), lcol = "blue", tcol = "blue", llwd = 2)

    ## End(Not run)

[Package VGAM version 1.0-3 Index]