R/mice.impute.rfcont.R

Defines functions mice.impute.rfcont

Documented in mice.impute.rfcont

mice.impute.rfcont <- function(y, ry, x,
	ntree_cont = NULL, nodesize_cont = NULL, 
	maxnodes_cont = NULL, ntree = NULL, ...){
	# y is the vector of y (observed and unobserved)
	# ry is a vector of indicators as to whether y is observed
	# x is the matrix of predictors
	x <- as.matrix(x)
	bootsample <- sample(sum(ry), replace = TRUE)
	
	# Use ntree to pass the number of trees (consistent with
	# mice.impute.rf in the mice package)
	if (is.null(ntree_cont) & !is.null(ntree)){
		ntree_cont <- ntree
	}

	if (is.null(ntree_cont)){
		if (is.null(getOption('CALIBERrfimpute_ntree_cont'))){
			ntree_cont <- 10
		} else {
			ntree_cont <- getOption('CALIBERrfimpute_ntree_cont')
		}
	}

	if (is.null(nodesize_cont)){
		if (is.null(getOption('CALIBERrfimpute_nodesize_cont'))){
			nodesize_cont <- 5
		} else {
			nodesize_cont <- getOption('CALIBERrfimpute_nodesize_cont')
		}
	}

	if (is.null(maxnodes_cont)){
		# default is NULL
		maxnodes_cont <- getOption('CALIBERrfimpute_maxnodes_cont')
	}

	# Only bootstrap if more than one tree, because Random Forest
	# fits to a bootstrap sample. Use drop = FALSE to ensure that the
	# predictor matrix remains a matrix
	if (ntree_cont > 1){
		yobs <- y[ry][bootsample]
		xobs <- x[ry, , drop = FALSE][bootsample, , drop = FALSE]
	} else {
		yobs <- y[ry]
		xobs <- x[ry, , drop = FALSE]
	}
	xmiss <- x[!ry, , drop = FALSE]
	# Build a random forest
	rf <- randomForest(xobs, yobs, ntree = ntree_cont,
		nodesize = nodesize_cont, maxnodes = maxnodes_cont, ...)

	yhat <- predict(rf, xmiss)
	# Draw imputed values from normal distributions
	# centred on the means predicted by Random Forest 
	yimp <- rnorm(length(yhat), mean = yhat, sd = sqrt(rf$mse[ntree_cont]))		
	return(yimp)
}

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CALIBERrfimpute documentation built on June 12, 2018, 3 a.m.