predict.mModel {bgmm} | R Documentation |
For every row in the matrix X
the posterior probability of belonging to class i
is calculated.
## S3 method for class 'mModel' predict(object, X, knowns = NULL, B = NULL, P = NULL, ...)
object |
an object of the class |
X |
a |
knowns |
a |
P |
a matrix with plausibilities for object |
B |
a matrix with beliefs for object |
... |
all other arguments will be neglected. |
The matrix tij of posterior probabilities is calculated as normalized products of priors pi's and density of model components in values specified by rows of the matrix X
.
If arguments knowns
and B
are specified then the priors's for objects in knowns
are replaced by belief matrix B
.
If arguments knowns
and P
are specified then the priors's for objects in knowns
are multiplied by plausibility matrix P
.
An list with the following elements:
tij.X, tij.knowns |
the matrix tij.X is a matrix with number of rows equal to number of rows in the matrix |
class.X, class.knowns |
vactors of labels/classes obtained with the MAP rule. The vector |
Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy Tiuryn (2012), The R Package bgmm: Mixture Modeling with Uncertain Knowledge, Journal of Statistical Software.
http://bgmm.molgen.mpg.de
data(genotypes) modelSoft = soft(X=genotypes$X, knowns=genotypes$knowns, P=genotypes$B) preds = predict(modelSoft, X = genotypes$X) str(preds)