# Plot Components of a GAM Object

### Description

A plot method for GAM objects, which can be used on GLM and LM objects as well. It focuses on terms (main-effects), and produces a suitable plot for terms of different types

### Usage

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### Arguments

`x` |
a |

`object` |
same as |

`residuals` |
if |

`rugplot` |
if |

`se` |
if |

`scale` |
a lower limit for the number of units covered by the limits on the ‘y’ for each plot. The default is |

`ask` |
if |

`newdata` |
if supplied to |

`terms` |
subsets of the terms can be selected |

`...` |
Additonal plotting arguments, not all of which will work (like xlim) |

### Value

a plot is produced for each of the terms in the object `x`

. The function currently knows how to plot all main-effect functions of one or two predictors. So in particular, interactions are not plotted. An appropriate ‘x-y’ is produced to display each of the terms, adorned with residuals, standard-error curves, and a rugplot, depending on the choice of options. The form of the plot is different, depending on whether the ‘x’-value for each plot is numeric, a factor, or a matrix.

When `ask=TRUE`

, rather than produce each plot sequentially, `plot.gam()`

displays a menu listing all the terms that can be plotted, as well as switches for all the options.

A `preplot.gam`

object is a list of precomputed terms. Each such
term (also a `preplot.gam`

object) is a list with components
`x`

, `y`

and others—the basic ingredients needed for each
term plot. These are in turn handed to the specialized plotting function
`gplot()`

, which has methods for different classes of the leading
`x`

argument. In particular, a different plot is produced if
`x`

is numeric, a category or factor, a matrix, or a
list. Experienced users can extend this range by creating more
`gplot()`

methods for other classes. Graphical parameters (see
`par`

) may also be supplied as arguments to this function.
This function is a method for the generic function `plot()`

for
class `"gam"`

.

It can be invoked by calling `plot(x)`

for an
object `x`

of the appropriate class, or directly by
calling `plot.gam(x)`

regardless of the
class of the object.

### Author(s)

Written by Trevor Hastie, following closely the design in the "Generalized Additive Models" chapter (Hastie, 1992) in Chambers and Hastie (1992).

### References

Hastie, T. J. (1992)
*Generalized additive models.*
Chapter 7 of *Statistical Models in S*
eds J. M. Chambers and T. J. Hastie, Wadsworth \& Brooks/Cole.

Hastie, T. and Tibshirani, R. (1990)
*Generalized Additive Models.*
London: Chapman and Hall.

### See Also

`preplot`

, `predict.gam`

### Examples

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