vgam-class {VGAM} | R Documentation

## Class “vgam”

### Description

Vector generalized additive models.

### Objects from the Class

Objects can be created by calls of the form `vgam(...)`.

### Slots

`nl.chisq`:
  Object of class `"numeric"`. Nonlinear chi-squared values.
`nl.df`:
  Object of class `"numeric"`. Nonlinear chi-squared degrees of freedom values.
`spar`:
  Object of class `"numeric"` containing the (scaled) smoothing parameters.
`s.xargument`:
  Object of class `"character"` holding the variable name of any `s()` terms.
`var`:
  Object of class `"matrix"` holding approximate pointwise standard error information.
`Bspline`:
  Object of class `"list"` holding the scaled (internal and boundary) knots, and the fitted B-spline coefficients. These are used for prediction.
`extra`:
  Object of class `"list"`; the `extra` argument on entry to `vglm`. This contains any extra information that might be needed by the family function.
`family`:
  Object of class `"vglmff"`. The family function.
`iter`:
  Object of class `"numeric"`. The number of IRLS iterations used.
`predictors`:
  Object of class `"matrix"` with M columns which holds the M linear predictors.
`assign`:
  Object of class `"list"`, from class `"vlm"`. This named list gives information matching the columns and the (LM) model matrix terms.
`call`:
  Object of class `"call"`, from class `"vlm"`. The matched call.
`coefficients`:
  Object of class `"numeric"`, from class `"vlm"`. A named vector of coefficients.
`constraints`:
  Object of class `"list"`, from class `"vlm"`. A named list of constraint matrices used in the fitting.
`contrasts`:
  Object of class `"list"`, from class `"vlm"`. The contrasts used (if any).
`control`:
  Object of class `"list"`, from class `"vlm"`. A list of parameters for controlling the fitting process. See `vglm.control` for details.
`criterion`:
  Object of class `"list"`, from class `"vlm"`. List of convergence criterion evaluated at the final IRLS iteration.
`df.residual`:
  Object of class `"numeric"`, from class `"vlm"`. The residual degrees of freedom.
`df.total`:
  Object of class `"numeric"`, from class `"vlm"`. The total degrees of freedom.
`dispersion`:
  Object of class `"numeric"`, from class `"vlm"`. The scaling parameter.
`effects`:
  Object of class `"numeric"`, from class `"vlm"`. The effects.
`fitted.values`:
  Object of class `"matrix"`, from class `"vlm"`. The fitted values. This is usually the mean but may be quantiles, or the location parameter, e.g., in the Cauchy model.

`misc`:
  Object of class `"list"`, from class `"vlm"`. A named list to hold miscellaneous parameters.
`model`:
  Object of class `"data.frame"`, from class `"vlm"`. The model frame.
`na.action`:
  Object of class `"list"`, from class `"vlm"`. A list holding information about missing values.
`offset`:
  Object of class `"matrix"`, from class `"vlm"`. If non-zero, a M-column matrix of offsets.
`post`:
  Object of class `"list"`, from class `"vlm"` where post-analysis results may be put.
`preplot`:
  Object of class `"list"`, from class `"vlm"` used by `plotvgam`; the plotting parameters may be put here.
`prior.weights`:
  Object of class `"numeric"`, from class `"vlm"` holding the initially supplied weights.
`qr`:
  Object of class `"list"`, from class `"vlm"`. QR decomposition at the final iteration.
`R`:
  Object of class `"matrix"`, from class `"vlm"`. The R matrix in the QR decomposition used in the fitting.
`rank`:
  Object of class `"integer"`, from class `"vlm"`. Numerical rank of the fitted model.
`residuals`:
  Object of class `"matrix"`, from class `"vlm"`. The working residuals at the final IRLS iteration.
`rss`:
  Object of class `"numeric"`, from class `"vlm"`. Residual sum of squares at the final IRLS iteration with the adjusted dependent vectors and weight matrices.
`smart.prediction`:
  Object of class `"list"`, from class `"vlm"`. A list of data-dependent parameters (if any) that are used by smart prediction.
`terms`:
  Object of class `"list"`, from class `"vlm"`. The `terms` object used.
`weights`:
  Object of class `"matrix"`, from class `"vlm"`. The weight matrices at the final IRLS iteration. This is in matrix-band form.
`x`:
  Object of class `"matrix"`, from class `"vlm"`. The model matrix (LM, not VGLM).
`xlevels`:
  Object of class `"list"`, from class `"vlm"`. The levels of the factors, if any, used in fitting.
`y`:
  Object of class `"matrix"`, from class `"vlm"`. The response, in matrix form.
`Xm2`:
  Object of class `"matrix"`, from class `"vlm"`. See `vglm-class`).
`Ym2`:
  Object of class `"matrix"`, from class `"vlm"`. See `vglm-class`).
`callXm2`:
  Object of class `"call"`, from class `"vlm"`. The matched call for argument `form2`.

### Extends

Class `"vglm"`, directly. Class `"vlm"`, by class `"vglm"`.

### Methods

cdf
  `signature(object = "vglm")`: cumulative distribution function. Useful for quantile regression and extreme value data models.
deplot
  `signature(object = "vglm")`: density plot. Useful for quantile regression models.
deviance
  `signature(object = "vglm")`: deviance of the model (where applicable).
plot
  `signature(x = "vglm")`: diagnostic plots.
predict
  `signature(object = "vglm")`: extract the additive predictors or predict the additive predictors at a new data frame.
print
  `signature(x = "vglm")`: short summary of the object.
qtplot
  `signature(object = "vglm")`: quantile plot (only applicable to some models).
resid
  `signature(object = "vglm")`: residuals. There are various types of these.
residuals
  `signature(object = "vglm")`: residuals. Shorthand for `resid`.
rlplot
  `signature(object = "vglm")`: return level plot. Useful for extreme value data models.
summary
  `signature(object = "vglm")`: a more detailed summary of the object.

### Note

VGAMs have all the slots that `vglm` objects have (`vglm-class`), plus the first few slots described in the section above.

### Author(s)

Thomas W. Yee

### References

Yee, T. W. and Wild, C. J. (1996) Vector generalized additive models. Journal of the Royal Statistical Society, Series B, Methodological, 58, 481–493.

http://www.stat.auckland.ac.nz/~yee

### See Also

`vgam.control`, `vglm`, `s`, `vglm-class`, `vglmff-class`.

### Examples

    # Fit a nonparametric proportional odds model
    pneumo = transform(pneumo, let=log(exposure.time))
    vgam(cbind(normal, mild, severe) ~ s(let),
         cumulative(parallel=TRUE), pneumo)

[Package VGAM version 0.7-9 Index]