impute.rfsrc.RdFast imputation mode. A random forest is grown and used to impute missing data. No ensemble estimates or error rates are calculated. Optionally, a final sweep can be performed to re-fit each variable that had original missingness on the final covariates and overwrite only its originally-missing entries.
# S3 method for class 'rfsrc'
impute(formula, data,
ntree = 100, nodesize = 1, nsplit = 10,
nimpute = 2, fast = FALSE, blocks,
mf.q, max.iter = 10, eps = 0.01,
ytry = NULL, always.use = NULL, verbose = TRUE,
full.sweep = FALSE, restore.integer = TRUE,
...)A symbolic model description used to specify on the fly imputation. Can be omitted for unsupervised imputation in settings where the outcomes are unspecified or if distinction between outcomes and predictors is unnecessary. Ignored for missForest.
Data to be imputed. Matrices, tibbles, and
data.table objects are converted to a plain data frame.
Column names must be unique and nonempty, each column must be a
vector, and numeric values must be finite or missing. Bare vectors
and nested matrix or list columns are not supported.
Number of trees grown for each imputation.
Minimum terminal node size in each tree.
Non-negative integer for specifying random splitting.
Number of iterations for the missing data
algorithm. Ignored for multivariate missForest, which iterates to
convergence unless capped by max.iter.
If TRUE, uses rfsrc.fast instead of
rfsrc. Increases speed but may reduce accuracy.
Number of row-wise blocks to divide the data into. May improve speed for large data, but can reduce imputation accuracy. No action if unspecified.
Enables missForest. Either a fraction (between 0 and 1) of
variables treated as responses, or an integer indicating number of
response variables. mf.q = 1 corresponds to standard
missForest.
Maximum number of iterations for multivariate missForest. Must be a positive integer.
Minimum decrease in the between-pass change statistic required to continue missForest iterations. Must be finite and nonnegative.
Number of variables used as pseudo-responses in unsupervised forests. See Details.
Character vector of variables always included as responses in multivariate missForest. Ignored by other methods.
If TRUE, prints progress during multivariate
missForest imputation.
If TRUE, performs a final sweep after the
main imputation (both standard and missForest). For each variable
that had any original missingness, a forest is fit on rows
where the variable was observed using the final imputed
covariates; predictions are then written back only to the originally
missing cells. This can improve self-consistency across variables at
the cost of extra computation.
If TRUE, generated values in variables
that were integer supported in the original data are rounded to the
integer grid. This includes columns stored as R integers and numeric
columns whose observed finite values are all integer-valued. Only
originally missing cells are rounded; observed numeric values are
preserved. Numeric columns retain numeric storage. Columns stored as
R integers are returned as integer vectors only when every nonmissing
completed value is exactly integral and within R's integer range;
otherwise numeric storage is retained. Set to FALSE to retain
unrounded generated numeric values.
Additional arguments passed to or from methods.
Recognized advanced options include full.sweep.options (a
named list) controlling the final sweep hyperparameters:
ntree (default 500), nodesize (default
NULL), nsplit (default 10); and the standard
rfsrc controls mtry, splitrule, bootstrap,
sampsize, samptype that apply to the sweep.
For a table containing both observed and missing values, observations and variables with all values missing are removed before imputation. A complete table or a table whose entries are all missing is returned without imputation after container normalization.
A forest is grown and used solely for imputation. No ensemble statistics (e.g., error rates) are computed. Use this function when imputation is the only goal.
For standard imputation (not missForest), splits are based only on non-missing data. If a split variable has missing values, they are temporarily imputed by randomly drawing from in-bag, non-missing values to allow node assignment.
If mf.q is specified, multivariate missForest imputation
is applied (Stekhoven and B\"uhlmann, 2012). A fraction (or integer
count) of variables are selected as multivariate responses, predicted
using the remaining variables with multivariate composite
splitting. Each round imputes a disjoint set of variables, and the
full cycle is repeated until convergence, controlled by
max.iter and eps. Setting mf.q = 1 reverts to
response-wise missForest when always.use = NULL. A
non-NULL always.use, including an empty or entirely
unmatched vector, selects the multivariate branch. The
change statistic averages factor disagreement and scaled numeric
changes over originally missing entries. Iteration stops when this
statistic no longer decreases by at least eps, or when
max.iter is reached. The last completed pass is retained.
If no formula is provided and mf.q is omitted,
unsupervised splitting is used. The default ytry is
sqrt(p), where p is the number of retained
variables. For each of mtry candidate variables, a random
subset of ytry variables is selected as pseudo-responses. A
multivariate composite splitting rule is applied, and the split is
made on the variable yielding the best result (Tang and Ishwaran,
2017).
If no missing values remain after preprocessing, the function returns the processed data without further action.
Additional forest controls forwarded through ...
are mtry, splitrule, bootstrap,
sampsize, and samptype. The optional final sweep
has its own full.sweep.options list.
Optional final sweep: if full.sweep = TRUE, a
post-imputation sweep is performed for every variable with original
missingness. Each such variable is re-fit on its observed rows using
the final imputed covariates, and predictions overwrite only the
originally missing entries. Defaults for the sweep are
ntree = 500, nodesize = NULL, nsplit = 10, and
can be customized via full.sweep.options passed through
.... This applies to both standard and missForest modes.
If restore.integer = TRUE, integer support is detected
from the original retained data before imputation begins. Numeric
values are compared with the nearest integer using an absolute
floating-point tolerance that does not increase with their magnitude. Restoration
is applied after the missForest rough-fix initialization, before and
after the optional final sweep, and once more before returning the
completed data. The missForest iterative updates themselves are not
rounded between variable or block updates.
Invisibly, the data frame containing the original data with imputed data overlaid.
Stekhoven D.J. and Buhlmann P. (2012). MissForest–non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1):112–118.
Tang F. and Ishwaran H. (2017). Random forest missing data algorithms. Statistical Analysis and Data Mining, 10:363–377.
# \donttest{
## ------------------------------------------------------------
## example of survival imputation
## ------------------------------------------------------------
## default everything - unsupervised splitting
data(pbc, package = "randomForestSRC")
pbc1.d <- impute(data = pbc)
## imputation using outcome splitting
f <- as.formula(Surv(days, status) ~ .)
pbc2.d <- impute(f, data = pbc, nsplit = 3)
## random splitting can be reasonably good
pbc3.d <- impute(f, data = pbc, splitrule = "random", nimpute = 5)
## optional final sweep (standard imputation)
pbc3.fs <- impute(f, data = pbc, splitrule = "random", nimpute = 5,
full.sweep = TRUE)
## ------------------------------------------------------------
## example of regression imputation
## ------------------------------------------------------------
air1.d <- impute(data = airquality, nimpute = 5)
air2.d <- impute(Ozone ~ ., data = airquality, nimpute = 5)
air3.d <- impute(Ozone ~ ., data = airquality, fast = TRUE)
## final sweep with custom options (e.g., larger forest)
air3.fs <- impute(Ozone ~ ., data = airquality, nimpute = 5,
full.sweep = TRUE,
full.sweep.options = list(ntree = 1000, nodesize = 5, nsplit = 0,
mtry = 3, splitrule = "random"))
## ------------------------------------------------------------
## multivariate missForest imputation
## ------------------------------------------------------------
data(pbc, package = "randomForestSRC")
## missForest algorithm - uses 1 variable at a time for the response
pbc.d <- impute(data = pbc, mf.q = 1)
## multivariate missForest - use 10 percent of variables as responses
pbc.mv <- impute(data = pbc, mf.q = .10)
## missForest but faster by using random splitting
pbc.fast <- impute(data = pbc, mf.q = 1, splitrule = "random")
## missForest + final sweep
pbc.fast.fs <- impute(data = pbc, mf.q = 1, splitrule = "random",
full.sweep = TRUE)
# }