View source: R/stablelearner.R

stability | R Documentation |

Stability assessment of results from supervised statistical learning (i.e., recursive partitioning, support vector machines, neural networks, etc.). The procedure involves the pairwise comparison of results generated from learning samples randomly drawn from the original data set or directly from the data-generating process (if available).

```
stability(x, ..., data = NULL, control = stab_control(), weights = NULL,
applyfun = NULL, cores = NULL, names = NULL)
```

`x` |
fitted model object. Any model object can be used whose class is
registered in |

`...` |
additional fitted model objects. |

`data` |
an optional |

`control` |
a list with control parameters, see |

`weights` |
an optional matrix of dimension n * B that can be used to
weight the observations from the original learning data when the models
are refitted. If |

`applyfun` |
a |

`cores` |
integer. The number of cores to use in multicore computations
using |

`names` |
a vector of characters to specify a name for each fitted model object. By default, the objects are named by their class. |

Assesses the (overall) stability of a result from supervised statistical learning by quantifying the similarity of realizations from the distribution of possible results (given the algorithm, the formulated model, the data-generating process, the sample size, etc.). The stability distribution is estimated by repeatedly assessing the similarity between the results generated by training the algorithm on two different learning samples, by means of a similarity metric. The learning samples are generated by sampling from the learning data or the data-generating process in case of a simulation study. For more details, see Philipp et al. (2018).

For a single fitted model object, `stability`

returns an object of
class `"stablelearner"`

with the following components:

`call` |
the call from the model object |

`learner` |
the information about the learner retrieved from |

`B` |
the number of repetitions, |

`sval` |
a matrix containing the estimated similarity values for each
similarity measure specified in |

`sampstat` |
a list containing information on the size of the learning
samples ( |

`data` |
a language object referring to the |

`control` |
a list with control parameters used for assessing the stability, |

For several fitted model objects, `stability`

returns an object of
class `"stablelearnerList"`

which is a list of objects of class
`"stablelearner"`

.

Philipp M, Rusch T, Hornik K, Strobl C (2018).
“Measuring the Stability of Results from Supervised Statistical Learning”.
*Journal of Computational and Graphical Statistics*, **27**(4), 685–700.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10618600.2018.1473779")}

`boxplot.stablelearnerList`

, `summary.stablelearner`

```
## assessing the stability of a single result
library("partykit")
r1 <- ctree(Species ~ ., data = iris)
stab <- stability(r1)
summary(stab)
## assessing the stability of several results
library("rpart")
r2 <- rpart(Species ~ ., data = iris)
stab <- stability(r1, r2, control = stab_control(seed = 0))
summary(stab, names = c("ctree", "rpart"))
## using case-weights instead of resampling
stability(r1, weights = TRUE)
## using self-defined case-weights
n <- nrow(iris)
B <- 500
w <- array(sample(c(0, 1), size = n * B * 3, replace = TRUE), dim = c(n, B, 3))
stability(r1, weights = w)
## assessing stability for a given data-generating process
my_dgp <- function() dgp_twoclass(n = 100, p = 2, noise = 4, rho = 0.2)
res <- ctree(class ~ ., data = my_dgp())
stability(res, data = my_dgp)
```

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