R: Rootograms (S4 generic) for Assessing Goodness of Fit of...

rootogram4 {VGAM} | R Documentation

## Rootograms (S4 generic) for Assessing Goodness of Fit of Probability Models

### Description

A graphical technique for comparing the observed and fitted counts from a probability model, on a square root scale.

### Usage

    rootogram4(object, ...)
    rootogram4vglm(object, newdata = NULL, breaks = NULL, max = NULL,
                   xlab = NULL, main = NULL, width = NULL, ...)

### Arguments

`object` | an object of class `"vglm"`. zz This includes `"vgam"` because `"vlm"` handles both VGLM and VGAM objects.
`newdata` | Data upon which to base the calculations. The default is the one used to fit the model.
`breaks` | numeric. Breaks for the histogram intervals.
`max` | maximum count displayed.
`xlab, main` | graphical parameters.
`width` | numeric. Widths of the histogram bars.

`...` | any additional arguments to `rootogram.default` and `plot.rootogram` in countreg.

### Details

Rootograms are a useful graphical technique for comparing the observed counts with the expected counts given a probability model.

This S4 implementation is based very heavily on `rootogram` coming from countreg. This package is primarily written by A. Zeileis and C. Kleiber. That package is currently on R-Forge but not CRAN, and it is based on S3. Since VGAM is written using S4, it was necessary to define an S4 generic function called `rootogram4()` which dispatches appropriately for S4 objects.

Currently, only a selected number of VGAM family functions are implemented. Over time, hopefully more and more will be completed.

### Value

See `rootogram` in countreg; an object of class `"rootogram0"` inheriting from `"data.frame"` with about 8 variables.

### Warning

This function is rudimentary and based totally on the implementation in countreg.

### Note

The function names used coming from countreg have been renamed slightly to avoid conflict.

### Author(s)

Package countreg is primarily written by A. Zeileis and C. Kleiber. Function `rootogram4()` is based very heavily on countreg. T. W. Yee wrote code to unpack variables from many various models and feed them into the appropriate `d`-type function.

### References

Friendly, M. and Meyer, D. (2016). Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data, Boca Raton, FL, USA: Chapman & Hall/CRC Press.

Kleiber, C. and Zeileis, A. (2016) “Visualizing Count Data Regressions Using Rootograms.” The American Statistician, 70(3), 296–303. doi: 10.1080/00031305.2016.1173590.

Tukey, J. W. (1977) Exploratory Data Analysis, Reading, MA, USA: Addison-Wesley.

### See Also

`vglm`, `vgam`, `glm`, `zipoisson`, `zapoisson`, `rootogram` in countreg.

### Examples

    ## Not run:
    data("hspider", package = "VGAM")  # Count responses
    hs.p   <- vglm(Pardlugu ~ CoveHerb,   poissonff, data = hspider)
    hs.nb  <- vglm(Pardlugu ~ CoveHerb, negbinomial, data = hspider)
    hs.zip <- vglm(Pardlugu ~ CoveHerb,   zipoisson, data = hspider)
    hs.zap <- vglm(Pardlugu ~ CoveHerb,   zapoisson, data = hspider)

    opar <- par(mfrow = c(2, 2))  # Plot the rootograms
    rootogram4(hs.p,   max = 15, main = "poissonff")
    rootogram4(hs.nb,  max = 15, main = "negbinomial")
    rootogram4(hs.zip, max = 15, main = "zipoisson")
    rootogram4(hs.zap, max = 15, main = "zapoisson")
    par(opar)

    ## End(Not run)

[Package VGAM version 1.1-3 Index]