learnAlgo | R Documentation |
LearnAlgo
] classThere is two algorithms and two stopping rules possibles for a learning algorithm.
Algorithms:
Impute
: Impute the missing values during the iterations
Simul
: Simulate the missing values during the iterations
Stopping rules:
nbIteration
: Set the maximum number of iterations
epsilon
: Set relative increase of the log-likelihood criterion
Default values are 200
nbIteration
of Simul
.
The epsilon
value is not used when the algorithm is "Simul". It is worth noting
that if there is no missing values, the method should be "Impute" and nbIteration
should be set to 1!
learnAlgo(algo = "Simul", nbIteration = 200, epsilon = 1e-07)
algo |
character string with the estimation algorithm. Possible values are "Simul", "Impute". Default value is "Simul". |
nbIteration |
Integer defining the maximal number of iterations. Default value is 200. |
epsilon |
Real defining the epsilon value for the algorithm. Not used by the "Simul" algorithm. Default value is 1.e-7. |
a [LearnAlgo
] object
Serge Iovleff
learnAlgo()
learnAlgo(algo="simul", nbIteration=50)
learnAlgo(algo="impute", epsilon = 1e-06)
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