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Last data update: 2014.03.03

R: Wavelet levels selection procedure

selectLevel

R Documentation

Wavelet levels selection procedure

Description

A grouped backward variable selection procedure for selecting the most significant wavelet levels of a functional variable. The groups are the wavelet coefficients belonging to the same frequency level.

Usage

selectLevel(design, ydata, typeRF = ifelse(is.factor(ydata), "classif", "reg"), 
            verbose = TRUE, ntree = 500, ...)

Arguments

`design`	The design matrix of a functional variable.
`ydata`	The outcome data. Must be a factor for classification.
`typeRF`	The type of forest we want to construct, ‘classif’ for classification or ‘reg’ for regression.
`verbose`	Should the details be printed.
`ntree`	The number of trees in the forests (default: 500).
`...`	optional parameters to be passed to the ‘varImpGroup’ function.

Value

An object of class fRFE which is a list with the following components:

`nselected`	The number of selected wavelet levels.
`selection`	The selected wavelet levels.
`selectionIndexes`	The indexes of selected wavelet levels in the input matrix ‘design’.
`error`	The prediction error computed in each iteration of the backward procedure.
`typeRF`	The type of the forests, classification or regression.
`ranking`	The final ranking of the wavelet levels.
`rankingIndexes`	The final ranking indexes of the wavelet levels.

Author(s)

Baptiste Gregorutti

References

Gregorutti, B., Michel, B. and Saint Pierre, P. (2015). Grouped variable importance with random forests and application to multiple functional data analysis, Computational Statistics and Data Analysis 90, 15-35.

Examples

  data(toyRegFD)
  x <- toyRegFD$FDlist[[1]]
  y <- toyRegFD$Y

  design <- projectWavelet(xdata=x)
  summary(levSel <- selectLevel(design, y, ntree=100, verbose=TRUE))
  plot(levSel)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(RFgroove)
Loading required package: randomForest
randomForest 4.6-12
Type rfNews() to see new features/changes/bug fixes.
Loading required package: wmtsa
Loading required package: fda
Loading required package: splines
Loading required package: Matrix

Attaching package: 'fda'

The following object is masked from 'package:graphics':

    matplot

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/RFgroove/selectLevel.Rd_%03d_medium.png", width=480, height=480)
> ### Name: selectLevel
> ### Title: Wavelet levels selection procedure
> ### Aliases: selectLevel
> 
> ### ** Examples
> 
>   data(toyRegFD)
>   x <- toyRegFD$FDlist[[1]]
>   y <- toyRegFD$Y
> 
>   design <- projectWavelet(xdata=x)
>   summary(levSel <- selectLevel(design, y, ntree=100, verbose=TRUE))
Group names: s7 d7 d6 d5 d4 d3 d2 d1 	Nr of variable in each level: 1 1 2 4 8 16 32 64 
normalize = TRUE 
s7 d7 d6 d5 d4 d3 d2 d1 
 1  1  2  4  8 16 32 64 
Regression backward selection.
Splitting data into a training and a testing set...
Survival indexes : 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 
   d3    d1    d4    s7    d2    d7    d5    d6 
0.120 0.065 0.053 0.041 0.026 0.017 0.004 0.003 
d6 eliminated. 7 remaining groups of variables.	 Error = 1.46 


Survival indexes : 1 2 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 
   d3    d4    d1    s7    d2    d7    d5 
0.114 0.088 0.053 0.040 0.026 0.015 0.015 
d5 eliminated. 6 remaining groups of variables.	 Error = 1.38 


Survival indexes : 1 2 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 
   d3    s7    d1    d4    d2    d7 
0.104 0.073 0.072 0.053 0.033 0.001 
d7 eliminated. 5 remaining groups of variables.	 Error = 1.42 


Survival indexes : 1 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 
   d3    d1    d4    s7    d2 
0.120 0.070 0.066 0.036 0.027 
d2 eliminated. 4 remaining groups of variables.	 Error = 1.36 


Survival indexes : 1 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 
   d3    d1    s7    d4 
0.119 0.091 0.067 0.061 
d4 eliminated. 3 remaining groups of variables.	 Error = 1.38 


Survival indexes : 1 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 
   d3    d1    s7 
0.135 0.102 0.042 
s7 eliminated. 2 remaining groups of variables.	 Error = 1.41 


Survival indexes : 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 
   d3    d1 
0.135 0.108 
d1 eliminated. 1 remaining groups of variables.	 Error = 1.45 


Survival indexes : 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 
d3 eliminated. No remaining groups of variables.	 Error = 1.33 


Ending...
 1 selected variables:
 d3 



 --- 				 ---
 --- 	Summary functional RFE	 ---
 --- 				 ---


Number of selected variables using a validation set: 1 

Selected variables:
d3 

Validation error for the best model: 1.3325 

>   plot(levSel)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>