Fills in missing values using either multiple imputation (mice) or
a random-forest based approach (missForest). The method must be
chosen explicitly, this function does not guess for you.
Usage
tr_impute(data, method = c("mice", "missForest"), m = 5, seed = 123)Arguments
- data
A data frame with missing values, typically the output of
tr_load_clinical().- method
Character. Either
"mice"(default) or"missForest".- m
Integer. Number of multiple imputations to run if
method = "mice". Defaults to 5. Ignored for"missForest".- seed
Integer. Random seed for reproducibility. Defaults to 123.
Value
A completed data frame with missing values filled in. If
method = "mice", the first completed dataset is returned, and the
full mids object is attached as an attribute ("mice_object") in
case the user wants to inspect all imputations.
Examples
df <- data.frame(
a = c(5, 7, 3, 9, 2, 8, 6, 4, 5, 7),
b = c(1, 3, 2, 4, 5, 3, 2, NA, 1, 3)
)
completed <- tr_impute(df, method = "mice", m = 2)
#> Imputation complete using method: mice