Summarize Results from Stability Assessment

Description

Summarizes and prints the results from stability assessments performed by stability.

Usage

## S3 method for class 'stablelearnerList'
summary(object, ..., reverse = TRUE, 
  probs = c(0.05, 0.25, 0.5, 0.75, 0.95), digits = 3, names = NULL)

Arguments

object	a object of class “stablelearner” or “stablelearnerList” to be summarized.
…	Arguments passed from or to other functions (currently ignored).
reverse	logical. If reverse = TRUE (default), the similarity values summarized in the summary output are transformed (reversed) such that higher values indicate a higher stability.
digits	integer. The number of digits used to summarize the similarity distribution in the summary output.
probs	a vector of probabilities used tosummarize the similarity distribution in the summary output.
names	a vector of characters to specify a name for each result from statistical learning in the summary output.

See Also

stability

Examples

library("stablelearner")




library("partykit")

rval <- ctree(Species ~ ., data = iris)
stab <- stability(rval)

summary(stab)

Call:
ctree(formula = Species ~ ., data = iris)

Sampler:
B = 500 
Resampling method = Bootstrap sampling with 100.0% data 
Evaluation method = OOB 

Sampling summary:
                           avg min max
Learning sample size   150.000 150 150
Learning overlap        60.354  48  76
Evaluation sample size  20.206   9  30
Evaluation overlap       0.000   0   0

Similarity summary:

, , Total variation distance (reversed)

           5%    25%   50%   75%   95%  
constparty 0.873 0.930 0.959 0.980 0.996

summary(stab, reverse = FALSE)

Call:
ctree(formula = Species ~ ., data = iris)

Sampler:
B = 500 
Resampling method = Bootstrap sampling with 100.0% data 
Evaluation method = OOB 

Sampling summary:
                           avg min max
Learning sample size   150.000 150 150
Learning overlap        60.354  48  76
Evaluation sample size  20.206   9  30
Evaluation overlap       0.000   0   0

Similarity summary:

, , Total variation distance

           5%      25%     50%     75%     95%    
constparty 0.00396 0.02000 0.04137 0.06963 0.12655

summary(stab, probs = c(0.25, 0.5, 0.75))

Call:
ctree(formula = Species ~ ., data = iris)

Sampler:
B = 500 
Resampling method = Bootstrap sampling with 100.0% data 
Evaluation method = OOB 

Sampling summary:
                           avg min max
Learning sample size   150.000 150 150
Learning overlap        60.354  48  76
Evaluation sample size  20.206   9  30
Evaluation overlap       0.000   0   0

Similarity summary:

, , Total variation distance (reversed)

           25%   50%   75%  
constparty 0.930 0.959 0.980

summary(stab, names = "conditional inference tree")

Call:
ctree(formula = Species ~ ., data = iris)

Sampler:
B = 500 
Resampling method = Bootstrap sampling with 100.0% data 
Evaluation method = OOB 

Sampling summary:
                           avg min max
Learning sample size   150.000 150 150
Learning overlap        60.354  48  76
Evaluation sample size  20.206   9  30
Evaluation overlap       0.000   0   0

Similarity summary:

, , Total variation distance (reversed)

                           5%    25%   50%   75%   95%  
conditional inference tree 0.873 0.930 0.959 0.980 0.996