print.fdiscd.predict {dad} | R Documentation |
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 class 'fdiscd.predict'
print(x, dist.print=TRUE, prox.print=FALSE, digits=2, ...)
Arguments
x |
object of class |
dist.print |
logical. If |
prox.print |
logical. Its default value is |
digits |
numerical. Number of significant digits for the display of numerical results. |
... |
optional arguments to |
Details
By default, are printed:
if available (if
misclass.ratio
argument offdiscd.predict
wasTRUE
), 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.
Author(s)
Rachid Boumaza, Pierre Santagostini, Smail Yousfi, Gilles Hunault, Sabine Demotes-Mainard
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.
See Also
Examples
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)