randomForestSRC (version 2.6.0)

impute: 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 = 500, mtry = NULL,
  xvar.wt = NULL, nodesize = 1, splitrule = NULL, nsplit = 10,
  
  na.action = c("na.impute"),
  
   
  nimpute = 2, mf.q, blocks,
  always.use = NULL, max.iter = 10, eps = 0.01, verbose = TRUE,
  do.trace = FALSE, ...)

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.

data

Data frame containing the data to be imputed.

ntree

Number of trees to grow.

mtry

Number of variables randomly sampled at each split.

nodesize

Forest average terminal node size.

splitrule

Splitting rule used to grow trees. Do not use for unsupervised or multivariate splitting unless splitrule="random" (used as a strategy for speeding up calculations).

nsplit

Non-negative integer value used to specify random splitting.

na.action

Missing value action. See details below.

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).

mf.q

Fraction of variables (between 0 and 1) used as responses in multivariate missForest imputation. By default, multivariate missForest imputation is not performed if left unspecifed. Can be an integer, in which case this equals the number of multivariate responses.

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.

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.

xvar.wt

Weights for selecting variables for splitting on.

max.iter

Maximum number of iterations used when implementing multivariate missForest imputation.

eps

Tolerance value used to determine convergence of multivariate missForest imputation.

verbose

Send verbose output to terminal (only applies to multivariate missForest imputation).

do.trace

Number of seconds between updates to the user on approximate time to completion.

...

Further arguments passed to or from other methods.

Value

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

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.

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.

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

Examples

# NOT RUN {
## ------------------------------------------------------------
## example of survival imputation
## ------------------------------------------------------------

## unsupervised splitting
data(pbc, package = "randomForestSRC")
pbc1.d <- impute(data = pbc, nimpute = 5)

#imputation using outcome splitting
f <- as.formula(Surv(days, status) ~ .)
pbc2.d <- impute(f, data = pbc, nsplit = 3)

#random splitting can be reasonably good
pbc4.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, nimpute = 5,
           splitrule = "random", nodesize = 1)

## ------------------------------------------------------------
## multivariate missForest imputation
## ------------------------------------------------------------

data(pbc, package = "randomForestSRC")

## use 10 percent of variables as responses
## i.e. multivariate missForest
pbc.d <- impute(data = pbc, mf.q = .01)

## use 1 variable as the response
## i.e. original missForest algorithm
pbc.d <- impute(data = pbc, mf.q = 1)

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

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


# }