Perform inplace imputation by filling missing values with aggregates computed on the "na.rm'd" vector. Additionally, it's possible to perform imputation based on groupings of columns from within data; these columns can be passed by index or name to the by parameter. If a factor column is supplied, then the method must be "mode".

h2o.impute(data, column = 0, method = c("mean", "median", "mode"),
combine_method = c("interpolate", "average", "lo", "hi"), by = NULL,
groupByFrame = NULL, values = NULL)

## Arguments

data The dataset containing the column to impute. A specific column to impute, default of 0 means impute the whole frame. "mean" replaces NAs with the column mean; "median" replaces NAs with the column median; "mode" replaces with the most common factor (for factor columns only); If method is "median", then choose how to combine quantiles on even sample sizes. This parameter is ignored in all other cases. group by columns Impute the column col with this pre-computed grouped frame. A vector of impute values (one per column). NaN indicates to skip the column

## Value

an H2OFrame with imputed values

## Details

The default method is selected based on the type of the column to impute. If the column is numeric then "mean" is selected; if it is categorical, then "mode" is selected. Other column types (e.g. String, Time, UUID) are not supported.

## Examples

# NOT RUN {
h2o.init()
iris_hf <- as.h2o(iris)
iris_hf[sample(nrow(iris_hf), 40), 5] <- NA  # randomly replace 50 values with NA
# impute with a group by
iris_hf <- h2o.impute(iris_hf, "Species", "mode", by = c("Sepal.Length", "Sepal.Width"))
# }