print.fdiscd.predict: Printing results of discriminant analysis of probability density functions

Description

print function, applied to an object of class "fdiscd.predict", prints numerical results of fdiscd.predict .

Usage

# S3 method for fdiscd.predict
print(x, dist.print=TRUE, prox.print=FALSE, digits=2, ...)

Arguments

x: object of class "fdiscd.predict", returned by fdiscd.predict.
dist.print: logical. If TRUE (the default), prints the matrix of distances between, on one side, the groups (densities) and, on the other side, the classes (of groups or densities).
prox.print: logical. Its default value is FALSE. If TRUE, prints the matrix of proximity indices between, on one side, the groups (densities) and, on the other side, the classes (of groups or densities).
digits: numerical. Number of significant digits for the display of numerical results.
...: optional arguments to print methods.

Author

Rachid Boumaza, Pierre Santagostini, Smail Yousfi, Gilles Hunault, Sabine Demotes-Mainard

Details

By default, are printed:

if available (if misclass.ratio argument of fdiscd.predict was TRUE), the whole misallocation ratio, the confusion matrix (allocations versus origins) and the misallocation ratio per class are printed.
the data frame the rows of which are the groups, and the columns of which are of the origin (NA if not available) and allocation classes.

If dist.print = TRUE or prox.print = TRUE, the distances or proximity indices between groups and classes, are displayed.

References

Boumaza, R. (2004). Discriminant analysis with independently repeated multivariate measurements: an $L^{2}$ approach. Computational Statistics & Data Analysis, 47, 823-843.

Rudrauf, J.M., Boumaza, R. (2001). Contribution à l'étude de l'architecture médiévale: les caractéristiques des pierres à bossage des châteaux forts alsaciens, Centre de Recherches Archéologiques médiévales de Saverne, 5, 5-38.

Examples

Run this code

data(castles.dated)
data(castles.nondated)
castles.stones <- rbind(castles.dated$stones, castles.nondated$stones)
castles.periods <- rbind(castles.dated$periods, castles.nondated$periods)
castlesfh <- folderh(castles.periods, "castle", castles.stones)
result <- fdiscd.predict(castlesfh, "period")
print(result)
print(result, prox.print=TRUE)

Run the code above in your browser using DataLab

Last chance! 50% off unlimited learning