crosstableStatistics takes a contingency table of observed vs. predicted values for a binary or polytomous response variable as input, and calculates a range of statistics about prediction accuracy.
crosstableStatistics(ctable)
Arguments
ctable: A contingency table cross-classifying observed and predicted values.
Returns
A list with the following components:
accuracy: Overall prediction accuracy
recall.predicted: Recall of prediction for each outcome value
precision.predicted: Precision of prediction for each outcome value
lambda.prediction: lambda for prediction accuracy (improvement over baseline of always predicting mode)
tau.classification: tau for classification accuracy (improvement over baseline of homogeneous distribution of predicted outcomes)
d.lambda.prediction: d(lambda): used for calculating P(lambda)
d.tau.classification: d(tau): used for calculating P(tau)
p.lambda.prediction: P(lambda): probability of reaching lambda by chance
p.tau.classification: P(tau): probability of reaching tau by chance
References
Arppe, A. 2008. Univariate, bivariate and multivariate methods in corpus-based lexicography -- a study of synonymy. Publications of the Department of General Linguistics, University of Helsinki, No. 44. URN: http://urn.fi/URN:ISBN:978-952-10-5175-3.
Arppe, A. and Baayen, R. H. (in prep.). Statistical classification and principles of human learning.
Menard, Scott (1995). Applied Logistic Regression Analysis. Sage University Paper Series on Quantitative Applications in the Social Sciences 07-106. Thousand Oaks: Sage Publications.
Author(s)
Antti Arppe and Harald Baayen
See Also
See also modelStatistics, ndlStatistics, ndlClassify.