Evaluate imputation performance by simulation
SimEval(data, task = NULL, p = 0.1, n.sim = 100, ini = "mean",
method = NULL, guess = FALSE, guess.method = NULL, other = NULL,
verbose = TRUE, seed = 1234)is the complete data matrix that will be used for simulation
task type, either be 1 for regression, 2 for classification or 3 for mixed type
is the percentage of missing values that will be introduction into data, it has to be a value between 0 and 1
the number of simulations, default is 100 times
is the initialization setting for some relevant imputation methods
, the default setting is "mean", while "median" and "random" can also be
used. See also guess
the imputaion method based on variable selection for simulation some other imputation method can be passed to the 'other' argument
logical value, if is TRUE, then guess will be used
as the imputation method for simulation
guess type for the guess function. It cannot be NULL if guess is TRUE
some other imputation method that is based on variable selection can be used. The requirement for this 'other' method is strict: it receives a data matrix including missing values and returns a complete data matrix.
logical, if TRUE, additional output information will be provided during iterations, i.e., the method that is using, the iteration number, the convegence difference as compared to the precious iteration. The progression bar will show up irrespective of this option and it can not be got rid of.
set the seed for simulation so simulations using different imputation methods are comparable. The default value is set to 1234, which is not supposed to mean anything. But if 1234 is used, then the seed for simulating the first missing data matrix is 1234, then it sums by one for every subsequent simulationg data matrix.
a list of componentes including
the method used for imputation
the name of the task
computational time
the imputation error
the number of iterations to converge
# NOT RUN {
data(parkinson)
# WARNING: simulation may take considerable time.
# }
# NOT RUN {
SimEval(parkinson, method = "lassoR")
# }
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