`pdpVars.Rd`

Displays the individual conditional expectation (ICE) curves and aggregated partial dependence for each variable in a grid.

pdpVars( data, fit, response, vars = NULL, pal = rev(RColorBrewer::brewer.pal(11, "RdYlBu")), gridSize = 10, nmax = 500, class = 1, nIce = 30, predictFun = NULL, limits = NULL, colorVar = NULL, draw = TRUE, probability = FALSE )

data | Data frame used for fit. |
---|---|

fit | A supervised machine learning model, which understands condvis2::CVpredict |

response | The name of the response for the fit. |

vars | The variables to plot (and their order), defaults to all variables other than response. |

pal | A vector of colors to show predictions, for use with scale_fill_gradientn |

gridSize | The size of the grid for evaluating the predictions. |

nmax | Uses sample of nmax data rows for the pdp. Default is 500. Use all rows if NULL. |

class | Category for classification, a factor level, or a number indicating which factor level. |

nIce | Number of ice curves to be plotted, defaults to 30. |

predictFun | Function of (fit, data) to extract numeric predictions from fit. Uses condvis2::CVpredict by default, which works for many fit classes. |

limits | A vector determining the limits of the predicted values. |

colorVar | Which variable to colour the predictions by. |

draw | If FALSE, then the plot will not be drawn. Default is TRUE. |

probability | if TRUE, then returns the partial dependence for classification on the probability scale. If FALSE (default), then the partial dependence is returned on a near logit scale. |

A grid displaying ICE curves and univariate partial dependence.

# \donttest{ # Load in the data: aq <- na.omit(airquality) fit <- lm(Ozone ~ ., data = aq) pdpVars(aq, fit, "Ozone") # Classification library(ranger) rfClassif <- ranger(Species ~ ., data = iris, probability = TRUE) pdpVars(iris, rfClassif, "Species", class = 3) pp <- pdpVars(iris, rfClassif, "Species", class = 2, draw = FALSE) pp[[1]] pdpVars(iris, rfClassif, "Species", class = 2, colorVar = "Species") # }