FastImputation: Use the pattern learned from the training data to impute...

In FastImputation: Learn from Training Data then Quickly Fill in Missing Data

Use the pattern learned from the training data to impute (fill in good guesses for) missing values.

Description

Like Amelia, FastImputation assumes that the columns of the data are multivariate normal or can be transformed into approximately multivariate normal.

Usage

FastImputation(x, patterns, verbose = TRUE)

Arguments

x	Dataframe, possibly with some missing (NA) values.
patterns	An object of class 'FastImputationPatterns' generated by TrainFastImputation.
verbose	If TRUE then the progress in imputing the data will be shown.

Value

x, but with missing values filled in (imputed)

Author(s)

Stephen R. Haptonstahl [e-mail]

References

https://gking.harvard.edu/amelia

See Also

TrainFastImputation

Examples

data(FI_train)   # provides FItrain dataset
patterns <- TrainFastImputation(
  FI_train,
  constraints=list(list("bounded_below_2", list(lower=0)),
                   list("bounded_above_5", list(upper=0)),
                   list("bounded_above_and_below_6", list(lower=0, upper=1))
                   ),
  idvars="user_id_1",
  categorical="categorical_9")

data(FI_test)
FI_test          # note there is missing data
imputed_data <- FastImputation(FI_test, patterns)
imputed_data    # good guesses for missing values are filled in

data(FI_true)
continuous_cells_imputed <- is.na(FI_test[,2:8])
continuous_imputed_values <- imputed_data[,2:8][continuous_cells_imputed]
continuous_true_values <- FI_true[,2:8][continuous_cells_imputed]
rmse <- sqrt(median((continuous_imputed_values-continuous_true_values)^2))
rmse
median_relative_error <- median( abs((continuous_imputed_values - continuous_true_values) / 
  continuous_true_values) )
median_relative_error

imputed_data_column_means <- FI_test[,2:8]
for(j in 1:ncol(imputed_data_column_means)) {
  imputed_data_column_means[is.na(imputed_data_column_means[,j]),j] <- 
    mean(imputed_data_column_means[,j], na.rm=TRUE)
}
cont_imputed_vals_col_means <- imputed_data_column_means[continuous_cells_imputed]
rmse_column_means <- sqrt(median((cont_imputed_vals_col_means-continuous_true_values)^2))
rmse_column_means  # much larger error than using FastImputation
median_relative_error_col_means <- median( abs((cont_imputed_vals_col_means - 
  continuous_true_values) / continuous_true_values) )
median_relative_error_col_means  # larger error than using FastImputation

# Let's look at the accuracy of the imputation of the categorical variable
library("caret")
categorical_rows_imputed <- which(is.na(FI_test$categorical_9))
confusionMatrix(data=imputed_data$categorical_9[categorical_rows_imputed], 
                reference=FI_true$categorical_9[categorical_rows_imputed])
# Compare to imputing with the modal value
stat_mode <- function(x) {
  unique_values <- unique(x)
  unique_values <- unique_values[!is.na(unique_values)]
  unique_values[which.max(tabulate(match(x, unique_values)))]
}
categorical_rows_imputed_col_mode <- rep(stat_mode(FI_test$categorical_9), 
                                         length(categorical_rows_imputed))
confusionMatrix(data=categorical_rows_imputed_col_mode, 
                reference=FI_true$categorical_9[categorical_rows_imputed])
# less accurate than using FastImputation

FastImputation documentation built on Sept. 25, 2023, 5:06 p.m.