nodeHarvest
Node Harvest
Computes the node harvest estimator
Usage
nodeHarvest(X, Y, nodesize = 10,
nodes = 1000,
maxinter = 2,
mode = "mean",
lambda = Inf,
addto = NULL,
onlyinter = NULL,
silent = FALSE,
biascorr = FALSE)
Arguments
 X
 A n x p  dimensional data matrix, where n is sample size and p is the dimensionality of the predictor variable. Factorial variables are currently converted to numerical variables (will be changed in the future). Missing values are supported.
 Y
 A numerical vector of length n, containing the observations of the response variable. Can be continuous (regression) or binary 0/1 (classification).
 nodesize
 Minimal number of samples in each node.
 nodes
 Number of nodes in the initial large ensemble of nodes.
 maxinter
 Maximal interaction depth (1 = only main effects; 2 = twofactor interactions etc).
 mode

If mode is equal to
"mean"
, predictions are weighted group means. If equal to"outbag"
(experimental version), the diagonal elements of the smoothing matrix are set to 0.  lambda
 Optional upper bound on the inverse of the average weighted fraction of samples within each node.
 addto
 A previous node harvest estimator to which additional nodes should be attached (useful for iterative growth of the estimator when hitting memory constraints).
 onlyinter
 Allow interactions only for this list of variables.
 silent

If
TRUE
, no comments are printed.  biascorr
 Use bias correction? Experimental. Can be useful for high signaltonoise ratio data.
Details
The number of nodes should be chosen as large as possible under the available computational resources.
If these resources are limited, an estimator can be build by iteratively calling the function, adding the previous
estimator via the addto
argument.
Feedback and feature requests are more than welcome (email below).
Value

A list with entries
 nodes
 A list of all selected nodes
 predicted
 Predicted values on training data
 connection
 Connectivity matrix between selected nodes (used for plotting)
 varnames
 Variable names
 Y
 The original observations
References
Node harvest: simple and interpretable regression and classification' (arxiv:0910.2145)
See Also
Examples
## Load Boston Housing dataset
data(BostonHousing)
X < BostonHousing[,1:13]
Y < BostonHousing[,14]
## Divide data into training and test data
n < nrow(X)
training < sample(1:n,round(n/2))
testing < (1:n)[training]
## Train Node Harvest and plot and print the estimator
NH < nodeHarvest( X[training,], Y[training], nodes=500 )
plot(NH)
print(NH, nonodes=6)
## Predict on test data and explain prediction of the first sample in the test set
predicttest < predict(NH, X[testing,], explain=1)
plot( predicttest, Y[testing] )
Community examples
Looks like there are no examples yet.