Description Usage Arguments Details Value Author(s) References See Also Examples
This function receives as main argument the object resulting from a
call to the pairedComparisons
function and produces a
list with the subset of the paired comparisons using the t test and
the Wilcoxon Signed Rank test that are statistically
significant given a certain p value limit.
1 2 3  signifDiffs(ps, p.limit=0.05,
metrics=names(ps),
tasks=rownames(ps[[1]]$avgScores))

ps 
An object resulting from a call to the

p.limit 
A number indicating the maximum value of the confidence level (p.value) of the statistical hypothesis test for a paired comparison to be considered statistically significant (defaults to 0.05). All paired comparisons with a p value below this limit will appear in the results of this function. 
metrics 
A vector with the names of the metrics for which we want the results (defaults to all metrics included in the paired comparison). 
tasks 
A vector with the names of the prediction tasks for which we want the results (defaults to all tasks included in the paired comparison). 
This function produces a list with as many components as the selected metrics (defaulting to all metrics in the paired comparison). Each of the components is another list with two components: i) one with the results for the t tests; and ii) the other with the results for the Wilcoxon Signed Rank test. Each of these two components is an array with 3 dimensions, with the rows representing the workflows, the columns a set of statistics and the thrid dimension being the task. The first row of these arrays will contain the baseline workflow against which all others are being compared (by either the t test or through the Wilcoxon Signed Rank test). The remaining rows will include the workflows whose comparison against this baseline is statistically significant, i.e. whose p value of the paired comparison is below the provided p limit.
The result of this function is a list (see the Details section).
Luis Torgo ltorgo@dcc.fc.up.pt
Torgo, L. (2014) An InfraStructure for Performance Estimation and Experimental Comparison of Predictive Models in R. arXiv:1412.0436 [cs.MS] http://arxiv.org/abs/1412.0436
pairedComparisons
,
performanceEstimation
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29  ## Not run:
## Estimating MSE for 3 variants of both
## regression trees and SVMs, on two data sets, using one repetition
## of 10fold CV
library(e1071)
data(iris)
data(Satellite,package="mlbench")
data(LetterRecognition,package="mlbench")
## running the estimation experiment
res < performanceEstimation(
c(PredTask(Species ~ .,iris),
PredTask(classes ~ .,Satellite,"sat"),
PredTask(lettr ~ .,LetterRecognition,"letter")),
workflowVariants(learner="svm",
learner.pars=list(cost=1:4,gamma=c(0.1,0.01))),
EstimationTask(metrics=c("err","acc"),method=CV()))
## now let us assume that we will choose "svm.v2" as our baseline
## carry out the paired comparisons
pres < pairedComparisons(res,"svm.v2")
## Obtaining the subset of differences that are significant
## with 99% confidence
sds < signifDiffs(res,p.limit=0.01)
## End(Not run)

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