ce.mimp: Mean or median imputation
In dprep: Data Pre-Processing and Visualization Functions for Classification
Description
Usage
Arguments
Value
Note
Author(s)
References
Examples
Description
A function that detects the location of missing values
by class, then imputes the missing values that occur in the features,
using mean or median imputation, as selected by the user. If the feature
is nominal then imputation is done using the mode.
Usage
1
Arguments
w.cl
dataset with missing values.
method
either "mean" or "median"
atr
list of relevant features
nomatr
list of nominal features, imputation is done using mode
Value
w.cl
the original matrix with values imputed
Note
A description of all the imputations carried out may be stored in a report
that is later saved to the current workspace. To produce the report, lines
at the end of the code must be uncommented. The report objects name starts
with Imput.rep.
Author(s)
Caroline Rodriguez and Edgar Acuna
References
Acuna, E. and Rodriguez, C. (2004). The treatment of missing values and its effect in the classifier accuracy. In D. Banks, L. House, F.R. McMorris, P. Arabie, W. Gaul (Eds).
Classification, Clustering and Data Mining Applications. Springer-Verlag Berlin-Heidelberg, 639-648.
Examples
1
2
3
Example output
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE
3: .onUnload failed in unloadNamespace() for 'rgl', details:
call: fun(...)
error: object 'rgl_quit' not found
Summary of imputations using substitution of mean (mode for nominal features):
Row Column Class Imput.value
[1,] 1 19 2 66.571429
[2,] 2 19 2 66.571429
[3,] 3 19 2 66.571429
[4,] 4 4 2 1.540984
[5,] 5 16 2 101.313725
[6,] 5 19 2 66.571429
[7,] 7 15 1 2.543333
[8,] 7 16 1 122.375000
[9,] 7 17 1 99.833333
[10,] 7 18 1 3.151852
[11,] 7 19 1 43.500000
[12,] 8 16 2 101.313725
[13,] 8 17 2 82.438017
[14,] 8 18 2 3.977679
[15,] 8 19 2 66.571429
[16,] 9 16 2 101.313725
[17,] 9 19 2 66.571429
[18,] 10 16 2 101.313725
[19,] 10 19 2 66.571429
[20,] 15 15 2 1.146218
[21,] 15 16 2 101.313725
[22,] 15 18 2 3.977679
[23,] 15 19 2 66.571429
[24,] 17 19 2 66.571429
[25,] 27 19 2 66.571429
[26,] 32 9 1 1.888889
[27,] 32 10 1 1.518519
[28,] 32 16 1 122.375000
[29,] 32 18 1 3.151852
[30,] 32 19 1 43.500000
[31,] 36 19 1 43.500000
[32,] 38 19 2 66.571429
[33,] 42 9 2 1.813559
[34,] 42 10 2 1.598291
[35,] 42 11 2 1.848739
[36,] 42 12 2 1.756303
[37,] 42 13 2 1.949580
[38,] 42 14 2 1.941176
[39,] 42 19 2 66.571429
[40,] 45 15 2 1.146218
[41,] 45 16 2 101.313725
[42,] 45 18 2 3.977679
[43,] 45 19 2 66.571429
[44,] 46 19 2 66.571429
[45,] 47 19 2 66.571429
[46,] 51 19 2 66.571429
[47,] 52 19 2 66.571429
[48,] 56 18 2 3.977679
[49,] 56 19 2 66.571429
[50,] 57 6 2 1.426230
[51,] 57 7 2 1.688525
[52,] 57 8 2 1.819672
[53,] 57 9 2 1.813559
[54,] 57 10 2 1.598291
[55,] 57 11 2 1.848739
[56,] 57 12 2 1.756303
[57,] 57 13 2 1.949580
[58,] 57 14 2 1.941176
[59,] 57 15 2 1.146218
[60,] 57 16 2 101.313725
[61,] 57 17 2 82.438017
[62,] 57 18 2 3.977679
[63,] 57 19 2 66.571429
[64,] 60 18 2 3.977679
[65,] 60 19 2 66.571429
[66,] 66 16 2 101.313725
[67,] 66 19 2 66.571429
[68,] 67 19 2 66.571429
[69,] 68 16 1 122.375000
[70,] 70 19 2 66.571429
[71,] 71 19 2 66.571429
[72,] 72 18 1 3.151852
[73,] 72 19 1 43.500000
[74,] 73 9 2 1.813559
[75,] 73 10 2 1.598291
[76,] 73 11 2 1.848739
[77,] 73 12 2 1.756303
[78,] 73 13 2 1.949580
[79,] 73 14 2 1.941176
[80,] 73 19 2 66.571429
[81,] 74 16 2 101.313725
[82,] 75 19 2 66.571429
[83,] 77 19 1 43.500000
[84,] 80 19 2 66.571429
[85,] 81 16 2 101.313725
[86,] 81 19 2 66.571429
[87,] 84 11 2 1.848739
[88,] 84 12 2 1.756303
[89,] 84 13 2 1.949580
[90,] 84 14 2 1.941176
[91,] 84 19 2 66.571429
[92,] 87 18 1 3.151852
[93,] 88 19 1 43.500000
[94,] 89 19 1 43.500000
[95,] 92 16 1 122.375000
[96,] 92 19 1 43.500000
[97,] 93 9 2 1.813559
[98,] 93 10 2 1.598291
[99,] 93 16 2 101.313725
[100,] 93 19 2 66.571429
[101,] 94 16 2 101.313725
[102,] 94 19 2 66.571429
[103,] 98 19 2 66.571429
[104,] 100 15 2 1.146218
[105,] 100 16 2 101.313725
[106,] 100 18 2 3.977679
[107,] 100 19 2 66.571429
[108,] 102 16 2 101.313725
[109,] 102 18 2 3.977679
[110,] 102 19 2 66.571429
[111,] 106 16 2 101.313725
[112,] 106 19 2 66.571429
[113,] 107 9 1 1.888889
[114,] 107 10 1 1.518519
[115,] 107 19 1 43.500000
[116,] 108 16 2 101.313725
[117,] 108 18 2 3.977679
[118,] 108 19 2 66.571429
[119,] 111 19 2 66.571429
[120,] 113 19 2 66.571429
[121,] 114 19 2 66.571429
[122,] 115 19 2 66.571429
[123,] 116 18 2 3.977679
[124,] 116 19 2 66.571429
[125,] 117 16 2 101.313725
[126,] 117 19 2 66.571429
[127,] 119 9 1 1.888889
[128,] 119 10 1 1.518519
[129,] 119 15 1 2.543333
[130,] 119 16 1 122.375000
[131,] 119 17 1 99.833333
[132,] 119 18 1 3.151852
[133,] 119 19 1 43.500000
[134,] 120 19 2 66.571429
[135,] 121 19 1 43.500000
[136,] 123 18 2 3.977679
[137,] 123 19 2 66.571429
[138,] 124 16 2 101.313725
[139,] 124 19 2 66.571429
[140,] 127 9 2 1.813559
[141,] 127 10 2 1.598291
[142,] 127 16 2 101.313725
[143,] 127 19 2 66.571429
[144,] 132 16 1 122.375000
[145,] 132 19 1 43.500000
[146,] 133 19 2 66.571429
[147,] 137 16 2 101.313725
[148,] 137 19 2 66.571429
[149,] 141 19 2 66.571429
[150,] 142 9 1 1.888889
[151,] 142 10 1 1.518519
[152,] 143 16 2 101.313725
[153,] 145 16 1 122.375000
[154,] 145 19 1 43.500000
[155,] 147 19 1 43.500000
[156,] 148 9 1 1.888889
[157,] 148 10 1 1.518519
[158,] 148 11 1 1.612903
[159,] 148 12 1 1.290323
[160,] 148 13 1 1.548387
[161,] 148 14 1 1.645161
[162,] 149 10 2 1.598291
[163,] 149 19 2 66.571429
[164,] 150 19 2 66.571429
[165,] 151 16 1 122.375000
[166,] 152 19 2 66.571429
[167,] 153 19 2 66.571429
Total number of imputations per class:
1 2
45 122
Total number of imputations: 167
dprep documentation built on May 29, 2017, 11:01 a.m.
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Description Usage Arguments Value Note Author(s) References Examples
Description
A function that detects the location of missing values by class, then imputes the missing values that occur in the features, using mean or median imputation, as selected by the user. If the feature is nominal then imputation is done using the mode.
Usage
1 |
Arguments
w.cl |
dataset with missing values. |
method |
either "mean" or "median" |
atr |
list of relevant features |
nomatr |
list of nominal features, imputation is done using mode |
Value
w.cl |
the original matrix with values imputed |
Note
A description of all the imputations carried out may be stored in a report that is later saved to the current workspace. To produce the report, lines at the end of the code must be uncommented. The report objects name starts with Imput.rep.
Author(s)
Caroline Rodriguez and Edgar Acuna
References
Acuna, E. and Rodriguez, C. (2004). The treatment of missing values and its effect in the classifier accuracy. In D. Banks, L. House, F.R. McMorris, P. Arabie, W. Gaul (Eds). Classification, Clustering and Data Mining Applications. Springer-Verlag Berlin-Heidelberg, 639-648.
Examples
1 2 3 |
Example output
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE
3: .onUnload failed in unloadNamespace() for 'rgl', details:
call: fun(...)
error: object 'rgl_quit' not found
Summary of imputations using substitution of mean (mode for nominal features):
Row Column Class Imput.value
[1,] 1 19 2 66.571429
[2,] 2 19 2 66.571429
[3,] 3 19 2 66.571429
[4,] 4 4 2 1.540984
[5,] 5 16 2 101.313725
[6,] 5 19 2 66.571429
[7,] 7 15 1 2.543333
[8,] 7 16 1 122.375000
[9,] 7 17 1 99.833333
[10,] 7 18 1 3.151852
[11,] 7 19 1 43.500000
[12,] 8 16 2 101.313725
[13,] 8 17 2 82.438017
[14,] 8 18 2 3.977679
[15,] 8 19 2 66.571429
[16,] 9 16 2 101.313725
[17,] 9 19 2 66.571429
[18,] 10 16 2 101.313725
[19,] 10 19 2 66.571429
[20,] 15 15 2 1.146218
[21,] 15 16 2 101.313725
[22,] 15 18 2 3.977679
[23,] 15 19 2 66.571429
[24,] 17 19 2 66.571429
[25,] 27 19 2 66.571429
[26,] 32 9 1 1.888889
[27,] 32 10 1 1.518519
[28,] 32 16 1 122.375000
[29,] 32 18 1 3.151852
[30,] 32 19 1 43.500000
[31,] 36 19 1 43.500000
[32,] 38 19 2 66.571429
[33,] 42 9 2 1.813559
[34,] 42 10 2 1.598291
[35,] 42 11 2 1.848739
[36,] 42 12 2 1.756303
[37,] 42 13 2 1.949580
[38,] 42 14 2 1.941176
[39,] 42 19 2 66.571429
[40,] 45 15 2 1.146218
[41,] 45 16 2 101.313725
[42,] 45 18 2 3.977679
[43,] 45 19 2 66.571429
[44,] 46 19 2 66.571429
[45,] 47 19 2 66.571429
[46,] 51 19 2 66.571429
[47,] 52 19 2 66.571429
[48,] 56 18 2 3.977679
[49,] 56 19 2 66.571429
[50,] 57 6 2 1.426230
[51,] 57 7 2 1.688525
[52,] 57 8 2 1.819672
[53,] 57 9 2 1.813559
[54,] 57 10 2 1.598291
[55,] 57 11 2 1.848739
[56,] 57 12 2 1.756303
[57,] 57 13 2 1.949580
[58,] 57 14 2 1.941176
[59,] 57 15 2 1.146218
[60,] 57 16 2 101.313725
[61,] 57 17 2 82.438017
[62,] 57 18 2 3.977679
[63,] 57 19 2 66.571429
[64,] 60 18 2 3.977679
[65,] 60 19 2 66.571429
[66,] 66 16 2 101.313725
[67,] 66 19 2 66.571429
[68,] 67 19 2 66.571429
[69,] 68 16 1 122.375000
[70,] 70 19 2 66.571429
[71,] 71 19 2 66.571429
[72,] 72 18 1 3.151852
[73,] 72 19 1 43.500000
[74,] 73 9 2 1.813559
[75,] 73 10 2 1.598291
[76,] 73 11 2 1.848739
[77,] 73 12 2 1.756303
[78,] 73 13 2 1.949580
[79,] 73 14 2 1.941176
[80,] 73 19 2 66.571429
[81,] 74 16 2 101.313725
[82,] 75 19 2 66.571429
[83,] 77 19 1 43.500000
[84,] 80 19 2 66.571429
[85,] 81 16 2 101.313725
[86,] 81 19 2 66.571429
[87,] 84 11 2 1.848739
[88,] 84 12 2 1.756303
[89,] 84 13 2 1.949580
[90,] 84 14 2 1.941176
[91,] 84 19 2 66.571429
[92,] 87 18 1 3.151852
[93,] 88 19 1 43.500000
[94,] 89 19 1 43.500000
[95,] 92 16 1 122.375000
[96,] 92 19 1 43.500000
[97,] 93 9 2 1.813559
[98,] 93 10 2 1.598291
[99,] 93 16 2 101.313725
[100,] 93 19 2 66.571429
[101,] 94 16 2 101.313725
[102,] 94 19 2 66.571429
[103,] 98 19 2 66.571429
[104,] 100 15 2 1.146218
[105,] 100 16 2 101.313725
[106,] 100 18 2 3.977679
[107,] 100 19 2 66.571429
[108,] 102 16 2 101.313725
[109,] 102 18 2 3.977679
[110,] 102 19 2 66.571429
[111,] 106 16 2 101.313725
[112,] 106 19 2 66.571429
[113,] 107 9 1 1.888889
[114,] 107 10 1 1.518519
[115,] 107 19 1 43.500000
[116,] 108 16 2 101.313725
[117,] 108 18 2 3.977679
[118,] 108 19 2 66.571429
[119,] 111 19 2 66.571429
[120,] 113 19 2 66.571429
[121,] 114 19 2 66.571429
[122,] 115 19 2 66.571429
[123,] 116 18 2 3.977679
[124,] 116 19 2 66.571429
[125,] 117 16 2 101.313725
[126,] 117 19 2 66.571429
[127,] 119 9 1 1.888889
[128,] 119 10 1 1.518519
[129,] 119 15 1 2.543333
[130,] 119 16 1 122.375000
[131,] 119 17 1 99.833333
[132,] 119 18 1 3.151852
[133,] 119 19 1 43.500000
[134,] 120 19 2 66.571429
[135,] 121 19 1 43.500000
[136,] 123 18 2 3.977679
[137,] 123 19 2 66.571429
[138,] 124 16 2 101.313725
[139,] 124 19 2 66.571429
[140,] 127 9 2 1.813559
[141,] 127 10 2 1.598291
[142,] 127 16 2 101.313725
[143,] 127 19 2 66.571429
[144,] 132 16 1 122.375000
[145,] 132 19 1 43.500000
[146,] 133 19 2 66.571429
[147,] 137 16 2 101.313725
[148,] 137 19 2 66.571429
[149,] 141 19 2 66.571429
[150,] 142 9 1 1.888889
[151,] 142 10 1 1.518519
[152,] 143 16 2 101.313725
[153,] 145 16 1 122.375000
[154,] 145 19 1 43.500000
[155,] 147 19 1 43.500000
[156,] 148 9 1 1.888889
[157,] 148 10 1 1.518519
[158,] 148 11 1 1.612903
[159,] 148 12 1 1.290323
[160,] 148 13 1 1.548387
[161,] 148 14 1 1.645161
[162,] 149 10 2 1.598291
[163,] 149 19 2 66.571429
[164,] 150 19 2 66.571429
[165,] 151 16 1 122.375000
[166,] 152 19 2 66.571429
[167,] 153 19 2 66.571429
Total number of imputations per class:
1 2
45 122
Total number of imputations: 167
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