summary.ndlCrossvalidate {ndl} | R Documentation |
A summary of a crossvalidation of a Naive Discriminatory Reader Model
Description
A summarization method for an object of the class "ndlCrossvalidate"
.
Usage
## S3 method for class 'ndlCrossvalidate'
summary(object, ...)
## S3 method for class 'summary.ndlCrossvalidate'
print(x, digits = max(3, getOption("digits") - 3), ...)
Arguments
object |
An object of class |
x |
An object of class |
digits |
the number of significant digits to use when printing. |
... |
further arguments passed to or from other methods. |
Details
Calculates overall descriptive statistics of the crossvalidation of a fitted Naive Discriminatory Reader model and prints a nice summary of the key results.
Value
summary.ndlCrossvalidate
returns an object of the class
"summary.ndlCrossvalidate"
, a list with the following components:
call
The call matched to fit the
"ndlCrossvalidate"
object.formula
The formula specified for the
"ndlCrossvalidate"
object.statistics.summary
The means, minima and maxima of a range descriptive statistics for the fit and performance of individual folds; see
ndlStatistics
.crosstable.summary
The means of the crosstabulation of observed and predicted outcomes for the held-out test data.
recall.predicted.summary
The means of the recall values for the individual outcomes predicted with the held-out test data.
precision.predicted.summary
The means of the precision values for the individual outcomes predicted with the held-out test data.
statistics.all
All the values for a range descriptive statistics for the fit and performance of individual folds on the held-out test data; see
ndlStatistics
.k
The number of folds.
n.total
The sum frequency of all data points in
data
.n.train
The sum frequency of data points used for training the individual models (excluding the individual folds).
n.test
The sum frequency of data points in the individual held-out folds used for testing the individual models.
Author(s)
Antti Arppe
References
Arppe, A. and Baayen, R. H. (in prep.)
See Also
ndlCrossvalidate, ndlClassify, ndlStatistics
Examples
## For examples see examples(ndlCrossvalidate).