ale_ixn | R Documentation |

This is the central function that manages the creation of ALE data and plots
for two-way ALE interactions. For simple one-way ALE, see `ale()`

.
See documentation there for functionality shared between both functions.

For details, see the introductory vignette for this package or the details and examples below.

For the plots, `n_y_quant`

is the number of quantiles into which to
divide the predicted variable (y). The middle quantiles are grouped specially:

The middle quantile is the first confidence interval of

`median_band_pct`

(`median_band_pct[1]`

) around the median. This middle quantile is special because it generally represents no meaningful interaction.The quantiles above and below the middle are extended from the borders of the middle quantile to the regular borders of the other quantiles.

There will always be an odd number of quantiles: the special middle quantile plus an equal number of quantiles on each side of it. If n_y_quant is even, then a middle quantile will be added to it. If n_y_quant is odd, then the number specified will be used, including the middle quantile.

```
ale_ixn(
data,
model,
x1_cols = NULL,
x2_cols = NULL,
y_col = NULL,
...,
parallel = parallel::detectCores(logical = FALSE) - 1,
model_packages = as.character(NA),
output = c("plots", "data"),
pred_fun = function(object, newdata, type = pred_type) {
stats::predict(object =
object, newdata = newdata, type = type)
},
pred_type = "response",
x_intervals = 100,
relative_y = "median",
y_type = NULL,
median_band_pct = c(0.05, 0.5),
rug_sample_size = 500,
min_rug_per_interval = 1,
ale_xs = NULL,
n_x1_int = 20,
n_x2_int = 20,
n_y_quant = 10,
compact_plots = FALSE,
silent = FALSE
)
```

`data` |
See documentation for |

`model` |
See documentation for |

`x1_cols` , `x2_cols` |
character. Vectors of column names from |

`y_col` |
See documentation for |

`...` |
not used. Inserted to require explicit naming of subsequent arguments. |

`parallel` |
See documentation for |

`model_packages` |
See documentation for |

`output` |
See documentation for |

`pred_fun` , `pred_type` |
See documentation for |

`x_intervals` |
See documentation for |

`relative_y` |
See documentation for |

`y_type` |
See documentation for |

`median_band_pct` |
See documentation for |

`rug_sample_size` , `min_rug_per_interval` |
See documentation for |

`ale_xs` |
See documentation for |

`n_x1_int` , `n_x2_int` |
positive scalar integer. Number of intervals for the x1 or x2 axes respectively for interaction plot. These values are ignored if x1 or x2 are not numeric (i.e, if they are logical or factors). |

`n_y_quant` |
positive scalar integer. Number of intervals over which the range of y values is divided for the colour bands of the interaction plot. See details. |

`compact_plots` |
See documentation for |

`silent` |
See documentation for |

list of ALE interaction data tibbles and plots. The list has two levels of depth:

The first level is named by the x1 variables.

Within each x1 variable list, the second level is named by the x2 variables.

Within each x1-x2 list element, the data or plot is returned as requested in the

`output`

argument.

```
set.seed(0)
diamonds_sample <- ggplot2::diamonds[sample(nrow(ggplot2::diamonds), 1000), ]
# Create a GAM model with flexible curves to predict diamond price
# Smooth all numeric variables and include all other variables
gam_diamonds <- mgcv::gam(
price ~ s(carat) + s(depth) + s(table) + s(x) + s(y) + s(z) +
cut + color + clarity,
data = diamonds_sample
)
summary(gam_diamonds)
# ALE two-way interactions
ale_ixn_gam_diamonds <- ale_ixn(
diamonds_sample, gam_diamonds,
parallel = 2 # CRAN limit (delete this line on your own computer)
)
# Print interaction plots
ale_ixn_gam_diamonds$plots |>
# extract list of x1 ALE outputs
purrr::walk(\(.x1) {
# plot all x2 plots in each .x1 element
patchwork::wrap_plots(.x1) |>
print()
})
```

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