randomForestSRC (version 3.4.1)

impute.rfsrc: Impute Only Mode

Description

Fast imputation mode. A random forest is grown and used to impute missing data. No ensemble estimates or error rates are calculated.

Usage

# S3 method for 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,
  ...)

Value

Invisibly, the data frame containing the orginal data with imputed data overlaid.

Arguments

formula

A symbolic model description. Can be omitted if outcomes are unspecified or if distinction between outcomes and predictors is unnecessary. Ignored for multivariate missForest.

data

A data frame containing variables to be imputed.

ntree

Number of trees grown for each imputation.

nodesize

Minimum terminal node size in each tree.

nsplit

Non-negative integer for specifying random splitting.

nimpute

Number of iterations for the missing data algorithm. Ignored for multivariate missForest, which iterates to convergence unless capped by max.iter.

fast

If TRUE, uses rfsrcFast instead of rfsrc. Increases speed but may reduce accuracy.

blocks

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.

mf.q

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.

max.iter

Maximum number of iterations for multivariate missForest.

eps

Convergence threshold for multivariate missForest (change in imputed values).

ytry

Number of variables used as pseudo-responses in unsupervised forests. See Details.

always.use

Character vector of variables always included as responses in multivariate missForest. Ignored by other methods.

verbose

If TRUE, prints progress during multivariate missForest imputation.

...

Additional arguments passed to or from methods.

Author

Hemant Ishwaran and Udaya B. Kogalur

Details

1. Before imputation, observations and variables with all values missing are removed.

2. 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.

3. 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.

4. 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 standard missForest. This method is typically the most accurate, but also the most computationally intensive.

5. If no formula is provided, unsupervised splitting is used. The default ytry is sqrt(p), where p is the number of 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).

6. If no missing values remain after preprocessing, the function returns the processed data without further action.

7. All standard rfsrc options apply; see examples below for illustration.

References

Ishwaran H., Kogalur U.B., Blackstone E.H. and Lauer M.S. (2008). Random survival forests, Ann. App. Statist., 2:841-860.

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.

See Also

rfsrc, rfsrc.fast

Examples

# \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)

## ------------------------------------------------------------
## 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)

## ------------------------------------------------------------
## 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
## i.e. multivariate missForest
pbc.d <- impute(data = pbc, mf.q = .01)

## missForest but faster by using random splitting
pbc.d <- impute(data = pbc, mf.q = 1, splitrule = "random")

## missForest but faster by increasing nodesize
pbc.d <- impute(data = pbc, mf.q = 1, nodesize = 20, splitrule = "random")

## missForest but faster by using rfsrcFast
pbc.d <- impute(data = pbc, mf.q = 1, fast = TRUE)

## ------------------------------------------------------------
## another example of multivariate missForest imputation
## (suggested by John Sheffield)
## ------------------------------------------------------------

test_rows <- 1000

set.seed(1234)

a <- rpois(test_rows, 500)
b <- a + rnorm(test_rows, 50, 50)
c <- b + rnorm(test_rows, 50, 50)
d <- c + rnorm(test_rows, 50, 50)
e <- d + rnorm(test_rows, 50, 50)
f <- e + rnorm(test_rows, 50, 50)
g <- f + rnorm(test_rows, 50, 50)
h <- g + rnorm(test_rows, 50, 50)
i <- h + rnorm(test_rows, 50, 50)

fake_data <- data.frame(a, b, c, d, e, f, g, h, i)

fake_data_missing <- data.frame(lapply(fake_data, function(x) {
  x[runif(test_rows) <= 0.4] <- NA
  x
}))
  
imputed_data <- impute(
  data = fake_data_missing,
  mf.q = 0.2,
  ntree = 100,
  fast = TRUE,
  verbose = TRUE
)

par(mfrow=c(3,3))
o=lapply(1:ncol(imputed_data), function(j) {
  pt <- is.na(fake_data_missing[, j])
  x <- fake_data[pt, j]
  y <- imputed_data[pt, j]
  plot(x, y, pch = 16, cex = 0.8, xlab = "raw data",
    ylab = "imputed data", col = 2)
  points(x, y, pch = 1, cex = 0.8, col = gray(.9))
  lines(supsmu(x, y, span = .25), lty = 1, col = 4, lwd = 4)
  mtext(colnames(imputed_data)[j])
  NULL
})

# }