impute.rfsrc {randomForestSRC} | R Documentation

## 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 class 'rfsrc'
    impute(formula, data,
      ntree = 500, nodesize = 1, nsplit = 10,
      nimpute = 2, fast = FALSE, blocks,
      mf.q, max.iter = 10, eps = 0.01,
      ytry = NULL, always.use = NULL, verbose = TRUE,
      ...)

### Arguments

`formula` | A symbolic description of the model to be fit. Can be left unspecified if there are no outcomes or we don't care to distinguish between y-outcomes and x-variables in the imputation. Ignored when using multivariate missForest imputation.
`data` | Data frame containing the data to be imputed.
`ntree` | Number of trees to grow.
`nodesize` | Forest average terminal node size.
`nsplit` | Non-negative integer value used to specify random splitting.
`nimpute` | Number of iterations of the missing data algorithm. Ignored for multivariate missForest; in which case the algorithm iterates until a convergence criteria is achieved (users can however enforce a maximum number of iterations with the option `max.iter`).
`fast` | Use fast random forests, `rfsrcFast`, in place of `rfsrc`? Improves speed but is less accurate.
`blocks` | Integer value specifying the number of blocks the data should be broken up into (by rows). This can improve computational efficiency when the sample size is large but imputation efficiency decreases. By default, no action is taken if left unspecified.
`mf.q` | Use this to turn on missForest (which is off by default). Specifies fraction of variables (between 0 and 1) used as responses in multivariate missForest imputation. Can also be an integer, in which case this equals the number of multivariate responses.
`max.iter` | Maximum number of iterations used when implementing multivariate missForest imputation.
`eps` | Tolerance value used to determine convergence of multivariate missForest imputation.
`ytry` | Number of variables used as pseudo-responses in unsupervised forests. See details below.
`always.use` | Character vector of variable names to always be included as a response in multivariate missForest imputation. Does not apply for other imputation methods.
`verbose` | Send verbose output to terminal (only applies to multivariate missForest imputation).
`...` | Further arguments passed to or from other methods.

### Details

1. Grow a forest and use this to impute data. All external calculations such as ensemble calculations, error rates, etc. are turned off. Use this function if your only interest is imputing the data.

2. Split statistics are calculated using non-misssing data only. If a node splits on a variable with missing data, the variable's missing data is imputed by randomly drawing values from non-missing in-bag data. The purpose of this is to make it possible to assign cases to daughter nodes based on the split.

3. If no formula is specified, unsupervised splitting is implemented using a `ytry` value of sqrt(`p`) where `p` equals the number of variables. More precisely, `mtry` variables are selected at random, and for each of these a random subset of `ytry` variables are selected and defined as the multivariate pseudo-responses. A multivariate composite splitting rule of dimension `ytry` is then applied to each of the `mtry` multivariate regression problems and the node split on the variable leading to the best split (Tang and Ishwaran, 2017).

4. If `mf.q` is specified, a multivariate version of missForest imputation (Stekhoven and Buhlmann, 2012) is applied. A fraction `mf.q` of variables are used as multivariate responses and split by the remaining variables using multivariate composite splitting (Tang and Ishwaran, 2017). Missing data for responses are imputed by prediction. The process is repeated using a new set of variables for responses (mutually exclusive to the previous fit), until all variables have been imputed. This is one iteration. The entire process is repeated, and the algorithm iterated until a convergence criteria is met (specified using options `max.iter` and `eps`). Integer values for `mf.q` are allowed and interpreted as a request that `mf.q` variables be selected for the multivariate response. This is generally the most accurate of all the imputation procedures, but also the most computationally demanding. However see examples below for strategies to increase speed.

5. Prior to imputation, the data is processed and records with all values missing are removed, as are variables having all missing values.

6. If there is no missing data, either before or after processing of the data, the algorithm returns the processed data and no imputation is performed.

7. The default choice `nimpute=2` is chosen for coherence with the default missing data algorithm implemented in grow mode. Thus, if the user imputes data with `nimpute=2` and runs a grow forest using this imputed data, then performance values such as VIMP and error rates will coincide with those obtained by running a grow forest on the original non-imputed data using `na.action = "na.impute"`. Ignored for multivariate missForest.

8. All options are the same as `rfsrc` and the user should consult the `rfsrc` help file for details.

### Value

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

### Author(s)

Hemant Ishwaran and Udaya B. Kogalur

### 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

    ## ------------------------------------------------------------
    ## 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,
      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
    })

[Package randomForestSRC version 2.9.3 Index]