NCPC Robustness from resampling

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Description

Estimate the NCPC robustness using either jackknife or bootstrap resampling.

Usage

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ncpcResampling(obj, method="bootstrap", method.param, verbose=TRUE, ...)

Arguments

obj

the DDDataSet object

method

the method to use to estimate how robust is the feature selection (valid values: "jackknife", or "bootstrap").

method.param

the parameter to method, either number of data points to remove for "jackknife" (default: 1) or number of boostrap runs for "bootstrap" (default: 100).

verbose

if to print out the progress

...

other parameters to pass to ncpc()

Details

Estimate the robustness of NCPC predictions (i.e. variable types: direct, joint, indirect, no dependence) using resampling. Two type of resampling are available: bootstrap (where the whole dataset is resampled with replacement), and jackknifing (where 1 or more observation are removed at each resampling step).

NCPC is run for the resampled datasets and statistics is produced about how many times is each variable assigned one of the four types (direct, joint, indirect, no dependence). The final call for each variable is then made according to the following algorithm (#direct is number of times variable is called direct):

  1. if #no dependence > #direct+joint+indirect => "no dependence"

  2. else if #indirect > #direct+joint => "indirect"

  3. else if #joint > #direct => "joint"

  4. else "direct"

Value

NCPCRobustness object with the raw results from resampling and summarized results

Examples

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## Not run: 
# load the example data
data(mesoBin)

# run bootstrap resampling for NCPC with alpha=0.05
ncpcResampling(mesoBin$VM_SM, "bootstrap", 100, alpha=0.05)
# run bootstrap resampling for NCPC* with alpha=0.05
ncpcResampling(mesoBin$VM_SM, "bootstrap", 100, alpha=0.05, star=TRUE)

# run jackknifing for NCPC
ncpcResampling(mesoBin$VM_SM, "jackknife", 1, alpha=0.05)

## End(Not run)

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