Description Usage Arguments Details Examples

This is a simple wrapper to fit and plot a basic type of generalized additive model. The fitted model is of the form `gam(Y ~ s(X, k))`

, which can be fitted by a specified grouping variable (using the `g`

argument). Also supported is an optional random effect, in which case the model fitted is `gamm(Y ~ s(X, k=k), random = list(R=~1), data=dfr)`

.

1 2 3 4 5 |

`x` |
Variable for X axis (unquoted) |

`y` |
Variable for Y axis (unquoted) |

`g` |
Variable for grouping (unquoted); optional |

`data` |
Dataframe containing x and y |

`fittype` |
Either 'gam' (default), or 'lm' in which case a simple linear model is fit - useful for comparison. |

`kgam` |
the |

`R` |
An optional random effect (quoted) |

`log` |
Whether to add log axes for x or y (but no transformations are done). |

`axes` |
Logical (default TRUE), whether to add axes to the plot. |

`fitoneline` |
Whether to fit only one curve to the entire dataset, regardless of whether a grouping variable was defined. Default FALSE. |

`points.col` |
Colours of the points, can be a vector (one value for each group, if present). |

`lines.col` |
Colours of the lines, can be a vector (one value for each group, if present). |

`ci.col` |
Colour of the confidence band, if plotted. Defaults to a transparent grey colour. |

`xlab` |
X-axis label |

`ylab` |
Y-axis label |

`band` |
Logical. If true, plots the confidence band (as a transparent polygon). |

`plotit` |
Logical (default TRUE); if FALSE, suppresses the plot. |

`add` |
Logical (default FALSE), if TRUE, adds to an existing plot. |

`npred` |
Number of x values to use for prediction |

`lwd` |
Line thickness (see |

`...` |
Further arguments passed to |

In either case, the `k`

parameter necessary for the GAM fit can be set using the `kgam`

argument. See`choose.k`

for details on this setting (it is important you don't just use the default value here!).

1 2 3 4 5 6 7 | ```
data(Loblolly)
plot_gam(age, height, data=Loblolly)
plot_gam(age, height, Seed, data=Loblolly, band=FALSE, lines.col="black")
plot_gam(age, height, Seed, data=Loblolly, band=FALSE, lines.col="black", fittype="lm")
data(ChickWeight)
plot_gam(Time, weight, Diet, R="Chick", data=ChickWeight, lines.col=rainbow(4))
``` |

RemkoDuursma/nlshelper documentation built on Nov. 29, 2017, 1:13 a.m.

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