# stability

##### Stability Assessment for Results from Supervised Statistical Learning

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).

- Keywords
- resampling, similarity

##### Usage

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

##### Arguments

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

`LearnerList`

. Users can add classes for the current R session to`LearnerList`

, see`addLearner`

.- …
additional fitted model objects.

- data
an optional

`data.frame`

or a data-generating function. By default the learning data from`x`

is used (if this can be inferred from the`getCall`

of`x`

.- control
a list with control parameters, see

`stab_control`

.- 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

`weights = true`

, the weights are computed internally according to the`sampler`

defined in`control`

. If`weight = NULL`

(default), no case-weights are used and the`sampler`

defined in`control`

will be applied to the original data set.- applyfun
a

`lapply`

-like function. The default is to use`lapply`

unless`cores`

is specified in which case`mclapply`

is used (for multicore computations on platforms that support these).- cores
integer. The number of cores to use in multicore computations using

`mclapply`

(see above).- names
a vector of characters to specify a name for each fitted model object. By default, the objects are named by their class.

##### Details

FIXME:

##### Value

For a single fitted model object, `stability`

returns an object of
class `"stablelearner"`

with the following components:

the call from the model object `x`

,

the information about the learner retrieved from `LearnerList`

,

the number of repetitions,

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

,

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

), the size of the overlap between the learning samples
(`lo`

), the size of the evaluation sample (`es`

) and the size of
the overlap between the evaluation and the learning samples (`eo`

)
in each repetition.

a language object referring to the `data.frame`

or the
data-generating function used for assessing the stability,

a list with control parameters used for assessing the stability,

##### References

Philipp M, Rusch T, Hornik K, Strobl C (2018). Measuring the “Stability of Results from Supervised Statistical Learning”. Journal of Computational and Graphical Statistics. Forthcoming

##### See Also

##### Examples

```
# NOT RUN {
# }
# NOT RUN {
## 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)
# }
# NOT RUN {
# }
```

*Documentation reproduced from package stablelearner, version 0.1-2, License: GPL-2 | GPL-3*