TrainFastImputation() uses training data to describe a multivariate normal distribution that the data approximates or can be transformed into approximating and stores this information as an object of class 'FastImputationPatterns'. FastImputation() function uses this 'FastImputationPatterns' object to impute (make a good guess at) missing data in a single line or a whole data frame of data. This approximates the process used by 'Amelia' <http://gking.harvard.edu/amelia/> but is much faster when filling in values for a single line of data.
|Author||Stephen R. Haptonstahl|
|Date of publication||2017-03-12 09:02:10|
|Maintainer||Stephen R. Haptonstahl <email@example.com>|
|License||GPL (>= 2)|
BoundNormalizedVariable: Take a normalized variable and transform it back to a bounded...
CovarianceWithMissing: Estimate covariance when data is missing
FastImputation: Use the pattern learned from the training data to impute...
FI_test: Imputation Test Data
FI_train: Imputation Training Data
FI_true: Imputation "True" Data
NormalizeBoundedVariable: Take a variable bounded above/below/both and return an...
TrainFastImputation: Learn from the training data so that later you can fill in...
UnfactorColumns: Convert columns of a dataframe from factors to character or...
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