mid.conditional() calculates the data required to draw Individual Conditional Expectation (ICE) curves from a fitted MID model.
ICE curves visualize how a single observation's prediction changes as a specified variable's value varies, while all other variable are held constant.
Usage
mid.conditional(
object,
variable,
data = NULL,
n.samples = 100L,
max.nrow = 100000L,
type = c("response", "link"),
keep.effects = TRUE
)Arguments
- object
a "mid" object.
- variable
a character string or expression specifying the single predictor variable for which to calculate ICE curves.
- data
a data frame containing the observations to be used for the ICE calculations. If not provided, data is automatically extracted based on the function call.
- n.samples
the number of sample points for the
variable's range.- max.nrow
the maximum number of rows for the output data frames. If the number of evaluation points exceeds this limit, the original data is randomly subsampled.
- type
the type of prediction to return. "response" (default) for the original scale or "link" for the scale of the linear predictor.
- keep.effects
logical. If
TRUE, the effects of individual component functions are stored in the output object.
Value
mid.conditional() returns an object of class "mid.conditional". This is a list with the following components:
- terms
a character vector of relevant terms for the
variable.- observed
a data frame of the original observations used, along with their predictions.
- conditional
a data frame of the hypothetical observations and their corresponding predictions.
- values
a vector of the sample points for the
variableused in the ICE calculation
Details
The function generates a set of hypothetical observations by creating copies of the original data and varying the specified variable across a range of sample points.
It then obtains a prediction for each of these hypothetical observations from the MID model. The returned object can be plotted to visualize the ICE curves.
Examples
data(airquality, package = "datasets")
mid <- interpret(Ozone ~ .^2, data = airquality, lambda = 1)
#> 'model' not passed: response variable in 'data' is used
# Calculate the ICE values for a fitted MID model
ice <- mid.conditional(mid, variable = "Wind", data = airquality)
print(ice)
#>
#> Individual Conditional Expectation for 153 Observations
#>
#> Variable: Wind
#>
#> Sample Points:
#> [1] 2.3000 2.4859 2.6717 2.8576 3.0434 3.2293 3.4152 3.6010 3.7869
#> [10] 3.9727 4.1586 4.3444 4.5303 4.7162 4.9020 5.0879 5.2737 5.4596
#> [19] 5.6455 5.8313 6.0172 6.2030 6.3889 6.5747 6.7606 6.9465 7.1323
#> [28] 7.3182 7.5040 7.6899 7.8758 8.0616 8.2475 8.4333 8.6192 8.8051
#> [37] 8.9909 9.1768 9.3626 9.5485 9.7343 9.9202 10.1061 10.2919 10.4778
#> [46] 10.6636 10.8495 11.0354 11.2212 11.4071 11.5929 11.7788 11.9646 12.1505
#> [55] 12.3364 12.5222 12.7081 12.8939 13.0798 13.2657 13.4515 13.6374 13.8232
#> [64] 14.0091 14.1949 14.3808 14.5667 14.7525 14.9384 15.1242 15.3101 15.4960
#> [73] 15.6818 15.8677 16.0535 16.2394 16.4253 16.6111 16.7970 16.9828 17.1687
#> [82] 17.3545 17.5404 17.7263 17.9121 18.0980 18.2838 18.4697 18.6556 18.8414
#> [91] 19.0273 19.2131 19.3990 19.5848 19.7707 19.9566 20.1424 20.3283 20.5141
#> [100] 20.7000