learnAlgo {MixAll}

Create an instance of the [LearnAlgo] class

Description

There 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!

Usage

learnAlgo(algo = "Simul", nbIteration = 200, epsilon = 1e-07)

Arguments

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.

Value

a [LearnAlgo] object

Author(s)

Serge Iovleff

Examples

learnAlgo()
learnAlgo(algo="simul", nbIteration=50)
learnAlgo(algo="impute", epsilon = 1e-06)