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 `"matrix"`, 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.

`ResSS`:

  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.

### 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), data = pneumo)

[Package VGAM version 1.1-1 Index]