Spatial microsimulation is a methodology aiming to simulate entities such as households, individuals or business in the finer possible scale. This process requires the use of micro-datasets with individual based information. The sms library presented in this work, facilitates the production of small area population micro-data by combining various other datasets. This is a parallelized implementation of random selection with thoptimization to select a micro-dataset with characteristics that mach a macro description. The sms library contains functions for preparing micro-data from census and longitudinal datasets. This process is called fiting and is the primary step in spatial microsimulation methodology. It actualy fits small area datasets to census area descriptions. There is a need for a library in Statistcal Programming Language R for preparing small area microdata. The functions of the livbrary connect various data-sources and produce small area population dataset. The functions use multi-core approaches of modern personal desktop computers in order to simulate relatively big areas in less computational time. The sms library, uses the parallel interface of R platform to divide the main simulation process into smaller simulations which then run in parallel. The R library presented in this work, use parallel processing approaches for the efficient production of small area population micro-data, which can be later used for geographical analysis.
This is an example of using sms library. It uses the default data (census, survey) and prepares fitted microdata for the areas described in the census dataset.
In order to illustrate the use of sms library, it is useful to use a simple case study. The following case study will produce micro-data of the population of Lesvos island in Greece. The data-sources used are from the census of population of Greece and from an artificial panel dataset constructed for this case study. Similar panel datasets can be found from Eurostat.
library(sms)
## Loading required package: doParallel
## Loading required package: foreach
## Loading required package: iterators
## Loading required package: parallel
data(survey)
head(survey)
## pid he female agemature car_owner house_owner working annualIncome
## 1 6001 0 1 1 1 0 1 16567
## 2 6002 1 1 1 1 1 1 2458
## 3 6003 0 1 0 0 1 0 9437
## 4 6004 1 0 0 1 0 0 22130
## 5 6005 1 0 0 0 1 1 3936
## 6 6006 1 0 0 1 0 1 16695
data(census)
census
## areaid population he females
## 1 8301 56 46 42
## 2 8302 73 42 15
## 3 8303 58 12 10
## 4 8304 78 43 21
## 5 8305 73 17 60
## 6 8306 77 15 11
## 7 8307 66 37 20
## 8 8308 78 41 42
## 9 8309 77 56 10
## 10 8310 78 55 26
## 11 8311 58 19 60
## 12 8312 68 20 60
## 13 8313 60 56 51
Now both datasets have been loaded as data.frames. We need another data.frame which will hold the associations between the census and the survey data.frames. It will hold the names of the columns from both datasets. This is called “data lexicon” and is just holding column names (Strings)
in.lexicon=createLexicon()
in.lexicon=addDataAssociation(in.lexicon, c("he","he"))
in.lexicon=addDataAssociation(in.lexicon, c("females","female"))
in.lexicon
## con_1 con_2
## census_row he females
## survey_row he female
The first column of your census data should have a column named: areaid which should be a unique identifier of each area. ALso notice that the survey data sould use binary (0 or 1) values for all variables.
We need to construct a microsimulation object which will hold all necessery objects of our simulation. Thos objects are the imported data and after the simulation, the results will be stored inside the microsimulation object. This is the main object of every simulation process and it holds all necessary information and results of the fitting process. The following command creates a microsimulation object with the name insms and attach the census and survey data to it. It also defines the number of iterations (40) before ending the fitting process.
mysms=new("microsimulation", iterations=40,census=census, panel=survey,lexicon=in.lexicon)
The object mysms now has 5 slots which contain various objects and information. We can explore the slot names with the following command:
slotNames(mysms)
## [1] "census" "panel" "lexicon" "results" "iterations"
The census slot, contains the census data and the lpane slot the panel data of the simulation. The lexicon slot contains the data association information and the results slot contains the results of the simulation process which is currently empty. Finally the iterations slot contains the number of iterations before the selection of the best combination of individuals for each area.
Now that we have a microsimulation object, we use it to start the fitting process.
try01 = run_parallel_HC( mysms )
The object try01 is a new microsimulation object which holds the results of the fitting process as well as the initial data.
The results slot, inside the try01 object, holds the results of the fitting process.
try01
## An object of class "microsimulation"
## Slot "census":
## areaid population he females
## 1 8301 56 46 42
## 2 8302 73 42 15
## 3 8303 58 12 10
## 4 8304 78 43 21
## 5 8305 73 17 60
## 6 8306 77 15 11
## 7 8307 66 37 20
## 8 8308 78 41 42
## 9 8309 77 56 10
## 10 8310 78 55 26
## 11 8311 58 19 60
## 12 8312 68 20 60
## 13 8313 60 56 51
##
## Slot "panel":
## pid he female agemature car_owner house_owner working annualIncome
## 1 6001 0 1 1 1 0 1 16567
## 2 6002 1 1 1 1 1 1 2458
## 3 6003 0 1 0 0 1 0 9437
## 4 6004 1 0 0 1 0 0 22130
## 5 6005 1 0 0 0 1 1 3936
## 6 6006 1 0 0 1 0 1 16695
## 7 6007 0 0 1 1 1 0 9535
## 8 6008 0 0 1 1 0 1 1136
## 9 6009 1 1 1 1 0 0 24170
## 10 6010 0 1 1 0 1 0 10119
## 11 6011 1 1 1 1 0 1 14821
## 12 6012 1 0 1 0 1 1 7016
## 13 6013 1 0 1 1 0 1 20227
## 14 6014 1 1 0 1 1 1 6014
## 15 6015 0 1 1 0 0 0 19546
## 16 6016 1 1 0 0 0 1 21714
## 17 6017 1 0 0 1 1 1 18431
## 18 6018 0 0 0 1 1 1 23950
## 19 6019 0 1 1 1 0 0 4546
## 20 6020 1 0 0 1 0 1 1010
## 21 6021 0 1 1 1 1 0 16588
## 22 6022 0 1 0 1 0 1 2871
## 23 6023 1 1 0 0 1 1 18244
## 24 6024 1 1 0 0 0 0 24865
## 25 6025 1 0 1 0 0 0 14752
## 26 6026 0 0 0 1 0 0 22364
## 27 6027 1 0 1 1 0 1 1347
## 28 6028 1 0 0 0 0 0 12338
## 29 6029 0 1 1 1 0 0 22979
## 30 6030 0 0 0 1 1 0 2245
## 31 6031 0 0 1 0 1 1 10229
## 32 6032 0 0 1 1 1 0 9894
## 33 6033 0 1 1 0 0 0 15822
## 34 6034 1 0 0 1 1 0 8897
## 35 6035 1 1 1 0 1 0 23377
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## 37 6037 0 0 1 1 0 0 7093
## 38 6038 1 0 1 1 1 0 4508
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## 42 6042 0 0 1 1 0 1 19157
## 43 6043 0 0 0 1 0 1 3170
## 44 6044 1 0 1 0 0 0 1844
## 45 6045 0 0 0 0 1 1 13377
## 46 6046 1 0 0 1 1 0 24515
## 47 6047 1 1 0 0 0 0 14501
## 48 6048 0 1 1 0 0 1 4032
## 49 6049 0 1 1 1 0 1 7873
## 50 6050 0 1 1 1 1 0 12243
## 51 6051 0 0 1 1 0 1 6669
## 52 6052 0 1 0 1 1 1 21240
## 53 6053 1 0 1 0 0 0 8081
## 54 6054 1 0 0 1 1 1 20903
## 55 6055 0 0 1 1 0 0 9651
## 56 6056 0 1 0 1 1 0 19291
## 57 6057 1 0 1 1 1 1 24504
## 58 6058 1 0 0 0 0 0 22892
## 59 6059 1 0 1 0 0 1 18409
## 60 6060 0 0 0 1 0 0 7352
## 61 6061 0 0 0 1 1 0 1202
## 62 6062 0 0 0 0 0 0 5375
## 63 6063 0 0 1 1 0 0 22695
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## 70 6070 1 0 0 1 1 1 16285
## 71 6071 0 0 0 1 0 0 23451
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## 73 6073 0 0 1 0 0 0 23682
## 74 6074 1 0 0 0 1 1 16511
## 75 6075 0 0 0 0 0 0 5855
## 76 6076 1 1 1 0 0 0 14320
## 77 6077 1 1 1 0 1 1 3660
## 78 6078 0 1 0 0 0 1 12626
## 79 6079 1 1 0 0 1 1 16117
## 80 6080 1 0 0 0 1 0 14511
## 81 6081 0 1 0 0 0 0 19338
## 82 6082 0 0 1 1 1 0 6233
## 83 6083 1 1 0 0 1 1 15286
## 84 6084 0 1 0 1 0 0 24604
## 85 6085 0 1 1 1 1 1 14183
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## 87 6087 1 1 1 1 1 0 4265
## 88 6088 0 0 0 0 1 0 23350
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## 96 6096 1 0 0 0 1 0 15223
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## 109 6109 0 1 1 0 0 0 1120
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## 118 6118 1 1 1 0 0 1 5610
## 119 6119 1 1 1 0 0 0 24288
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## 121 6121 1 1 1 1 0 0 2352
## 122 6122 0 1 0 1 0 1 5706
## 123 6123 1 1 1 1 0 0 9458
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## 125 6125 0 1 0 1 0 1 16596
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## 127 6127 0 1 0 1 1 0 12893
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## 131 6131 1 0 1 1 1 0 4416
## 132 6132 1 1 0 1 1 1 12931
## 133 6133 1 0 0 1 1 1 16596
## 134 6134 0 0 0 1 1 0 13271
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## 145 6145 1 1 0 1 0 1 9894
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## 147 6147 1 0 1 0 1 0 8914
## 148 6148 1 0 1 1 0 1 4717
## 149 6149 0 0 0 0 0 1 21917
## 150 6150 0 1 0 1 1 0 23632
## 151 6151 0 0 1 0 0 0 18055
## 152 6152 1 1 0 0 1 0 11947
## 153 6153 0 0 0 1 0 1 13642
## 154 6154 0 0 0 1 0 0 24662
## 155 6155 1 0 0 1 0 0 13186
## 156 6156 0 1 1 0 1 0 12150
## 157 6157 0 0 1 1 1 1 18831
## 158 6158 1 0 0 1 0 0 755
## 159 6159 0 0 1 1 1 1 17199
## 160 6160 1 1 1 1 0 1 13746
## 161 6161 0 1 0 1 0 0 23118
## 162 6162 1 1 1 0 1 1 8955
## 163 6163 0 1 0 0 1 1 9332
## 164 6164 0 1 0 0 1 1 21865
## 165 6165 1 0 1 0 1 1 1125
## 166 6166 0 1 1 0 1 1 15312
## 167 6167 0 1 1 1 1 1 19045
## 168 6168 0 1 0 1 1 0 17495
## 169 6169 1 0 1 1 1 0 3719
## 170 6170 0 1 1 0 0 0 12948
## 171 6171 1 0 1 0 1 1 20752
## 172 6172 1 0 1 0 0 1 14346
## 173 6173 0 1 1 1 1 1 7025
## 174 6174 0 0 1 1 0 0 6798
## 175 6175 1 0 0 0 0 0 4067
## 176 6176 1 0 1 0 0 0 23390
## 177 6177 1 0 0 1 1 0 17102
## 178 6178 0 1 0 1 0 1 5271
## 179 6179 0 0 0 0 1 0 14058
## 180 6180 0 1 0 0 1 1 14699
## 181 6181 0 1 1 1 1 0 24356
## 182 6182 1 0 0 0 1 1 1369
## 183 6183 1 1 0 1 0 0 20091
## 184 6184 0 0 1 1 1 0 18460
## 185 6185 1 0 1 1 1 1 12862
## 186 6186 0 0 0 0 1 1 21279
## 187 6187 1 1 0 0 1 1 6766
## 188 6188 1 0 0 0 1 1 21100
## 189 6189 0 1 0 0 0 1 3754
## 190 6190 0 1 0 0 0 1 6535
## 191 6191 1 1 1 0 1 0 24296
## 192 6192 1 0 1 0 1 1 1649
## 193 6193 1 1 1 0 1 1 17777
## 194 6194 1 1 1 0 1 0 15198
## 195 6195 1 0 0 1 0 1 21223
## 196 6196 1 0 1 0 0 1 17683
## 197 6197 0 0 1 1 0 1 16944
## 198 6198 1 0 0 0 0 0 13894
## 199 6199 1 0 1 1 1 1 20173
## 200 6200 0 1 1 0 1 1 12623
##
## Slot "lexicon":
## con_1 con_2
## census_row he females
## survey_row he female
##
## Slot "results":
## [[1]]
## [[1]]$areaid
## [1] 8301
##
## [[1]]$selection
## pid he female agemature car_owner house_owner working annualIncome
## 42 6042 0 0 1 1 0 1 19157
## 118 6118 1 1 1 0 0 1 5610
## 148 6148 1 0 1 1 0 1 4717
## 2 6002 1 1 1 1 1 1 2458
## 108 6108 0 1 1 0 1 0 19083
## 14 6014 1 1 0 1 1 1 6014
## 123 6123 1 1 1 1 0 0 9458
## 125 6125 0 1 0 1 0 1 16596
## 129 6129 0 1 1 1 0 0 5539
## 72 6072 0 1 0 1 0 1 861
## 1 6001 0 1 1 1 0 1 16567
## 48 6048 0 1 1 0 0 1 4032
## 44 6044 1 0 1 0 0 0 1844
## 54 6054 1 0 0 1 1 1 20903
## 59 6059 1 0 1 0 0 1 18409
## 58 6058 1 0 0 0 0 0 22892
## 137 6137 1 1 0 0 0 0 11019
## 173 6173 0 1 1 1 1 1 7025
## 84 6084 0 1 0 1 0 0 24604
## 172 6172 1 0 1 0 0 1 14346
## 11 6011 1 1 1 1 0 1 14821
## 157 6157 0 0 1 1 1 1 18831
## 193 6193 1 1 1 0 1 1 17777
## 79 6079 1 1 0 0 1 1 16117
## 101 6101 0 1 1 1 0 1 21034
## 126 6126 1 1 1 0 1 1 12386
## 118.1 6118 1 1 1 0 0 1 5610
## 130 6130 1 1 1 1 1 1 7340
## 59.1 6059 1 0 1 0 0 1 18409
## 192 6192 1 0 1 0 1 1 1649
## 193.1 6193 1 1 1 0 1 1 17777
## 43 6043 0 0 0 1 0 1 3170
## 41 6041 1 1 1 1 1 1 997
## 16 6016 1 1 0 0 0 1 21714
## 137.1 6137 1 1 0 0 0 0 11019
## 21 6021 0 1 1 1 1 0 16588
## 28 6028 1 0 0 0 0 0 12338
## 154 6154 0 0 0 1 0 0 24662
## 44.1 6044 1 0 1 0 0 0 1844
## 44.2 6044 1 0 1 0 0 0 1844
## 84.1 6084 0 1 0 1 0 0 24604
## 5 6005 1 0 0 0 1 1 3936
## 160 6160 1 1 1 1 0 1 13746
## 190 6190 0 1 0 0 0 1 6535
## 82 6082 0 0 1 1 1 0 6233
## 153 6153 0 0 0 1 0 1 13642
## 119 6119 1 1 1 0 0 0 24288
## 121 6121 1 1 1 1 0 0 2352
## 75 6075 0 0 0 0 0 0 5855
## 122 6122 0 1 0 1 0 1 5706
## 14.1 6014 1 1 0 1 1 1 6014
## 103 6103 1 0 1 0 0 0 13352
## 198 6198 1 0 0 0 0 0 13894
## 54.1 6054 1 0 0 1 1 1 20903
## 132 6132 1 1 0 1 1 1 12931
## 26 6026 0 0 0 1 0 0 22364
##
## [[1]]$tae
## [1] 20
##
## [[1]]$tries
## [1] 35 29 25 26 32 33 36 38 33 42 32 28 41 41 36 29 33 25 20 27 32 32 33
## [24] 31 39 36 31 36 29 27 28 29 35 34 40 37 34 28 39 31
##
## [[1]]$error_states
## [1] 35 29 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 20 20 20 20 20
## [24] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
##
##
## [[2]]
## [[2]]$areaid
## [1] 8302
##
## [[2]]$selection
## pid he female agemature car_owner house_owner working annualIncome
## 137 6137 1 1 0 0 0 0 11019
## 176 6176 1 0 1 0 0 0 23390
## 152 6152 1 1 0 0 1 0 11947
## 146 6146 0 0 0 0 1 0 17654
## 191 6191 1 1 1 0 1 0 24296
## 63 6063 0 0 1 1 0 0 22695
## 199 6199 1 0 1 1 1 1 20173
## 2 6002 1 1 1 1 1 1 2458
## 94 6094 0 0 1 1 1 1 18017
## 148 6148 1 0 1 1 0 1 4717
## 137.1 6137 1 1 0 0 0 0 11019
## 4 6004 1 0 0 1 0 0 22130
## 13 6013 1 0 1 1 0 1 20227
## 57 6057 1 0 1 1 1 1 24504
## 23 6023 1 1 0 0 1 1 18244
## 27 6027 1 0 1 1 0 1 1347
## 17 6017 1 0 0 1 1 1 18431
## 92 6092 0 1 0 0 1 0 22133
## 51 6051 0 0 1 1 0 1 6669
## 163 6163 0 1 0 0 1 1 9332
## 66 6066 0 0 1 1 1 1 9266
## 107 6107 1 1 0 0 0 0 2070
## 41 6041 1 1 1 1 1 1 997
## 82 6082 0 0 1 1 1 0 6233
## 18 6018 0 0 0 1 1 1 23950
## 143 6143 0 0 1 0 0 0 4922
## 142 6142 0 1 1 1 0 0 24860
## 47 6047 1 1 0 0 0 0 14501
## 23.1 6023 1 1 0 0 1 1 18244
## 62 6062 0 0 0 0 0 0 5375
## 94.1 6094 0 0 1 1 1 1 18017
## 30 6030 0 0 0 1 1 0 2245
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## 80 6080 1 0 0 0 1 0 14511
## 185 6185 1 0 1 1 1 1 12862
## 34 6034 1 0 0 1 1 0 8897
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## 63.1 6063 0 0 1 1 0 0 22695
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## 61 6061 0 0 0 1 1 0 1202
## 143.2 6143 0 0 1 0 0 0 4922
## 173 6173 0 1 1 1 1 1 7025
## 188 6188 1 0 0 0 1 1 21100
## 106 6106 1 1 1 1 0 0 11994
## 177 6177 1 0 0 1 1 0 17102
## 121 6121 1 1 1 1 0 0 2352
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## 73 6073 0 0 1 0 0 0 23682
## 126 6126 1 1 1 0 1 1 12386
## 140 6140 0 1 0 1 0 1 17720
## 156 6156 0 1 1 0 1 0 12150
## 88 6088 0 0 0 0 1 0 23350
##
## [[2]]$tae
## [1] 18
##
## [[2]]$tries
## [1] 22 22 23 27 39 29 25 27 31 23 28 29 19 31 21 40 26 26 18 32 26 27 31
## [24] 23 34 32 39 31 23 26 26 32 38 23 30 18 26 30 34 31
##
## [[2]]$error_states
## [1] 22 22 22 22 22 22 22 22 22 22 22 22 19 19 19 19 19 19 18 18 18 18 18
## [24] 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18
##
##
## [[3]]
## [[3]]$areaid
## [1] 8303
##
## [[3]]$selection
## pid he female agemature car_owner house_owner working annualIncome
## 158 6158 1 0 0 1 0 0 755
## 35 6035 1 1 1 0 1 0 23377
## 94 6094 0 0 1 1 1 1 18017
## 150 6150 0 1 0 1 1 0 23632
## 177 6177 1 0 0 1 1 0 17102
## 45 6045 0 0 0 0 1 1 13377
## 94.1 6094 0 0 1 1 1 1 18017
## 96 6096 1 0 0 0 1 0 15223
## 151 6151 0 0 1 0 0 0 18055
## 174 6174 0 0 1 1 0 0 6798
## 174.1 6174 0 0 1 1 0 0 6798
## 164 6164 0 1 0 0 1 1 21865
## 139 6139 1 1 0 1 0 0 24333
## 143 6143 0 0 1 0 0 0 4922
## 2 6002 1 1 1 1 1 1 2458
## 170 6170 0 1 1 0 0 0 12948
## 98 6098 0 1 1 0 0 0 23423
## 188 6188 1 0 0 0 1 1 21100
## 141 6141 0 0 0 0 0 1 8217
## 184 6184 0 0 1 1 1 0 18460
## 59 6059 1 0 1 0 0 1 18409
## 122 6122 0 1 0 1 0 1 5706
## 45.1 6045 0 0 0 0 1 1 13377
## 59.1 6059 1 0 1 0 0 1 18409
## 195 6195 1 0 0 1 0 1 21223
## 71 6071 0 0 0 1 0 0 23451
## 60 6060 0 0 0 1 0 0 7352
## 15 6015 0 1 1 0 0 0 19546
## 108 6108 0 1 1 0 1 0 19083
## 130 6130 1 1 1 1 1 1 7340
## 103 6103 1 0 1 0 0 0 13352
## 84 6084 0 1 0 1 0 0 24604
## 178 6178 0 1 0 1 0 1 5271
## 153 6153 0 0 0 1 0 1 13642
## 147 6147 1 0 1 0 1 0 8914
## 48 6048 0 1 1 0 0 1 4032
## 27 6027 1 0 1 1 0 1 1347
## 153.1 6153 0 0 0 1 0 1 13642
## 161 6161 0 1 0 1 0 0 23118
## 130.1 6130 1 1 1 1 1 1 7340
## 33 6033 0 1 1 0 0 0 15822
## 184.1 6184 0 0 1 1 1 0 18460
## 71.1 6071 0 0 0 1 0 0 23451
## 90 6090 0 1 0 1 1 1 20528
## 18 6018 0 0 0 1 1 1 23950
## 33.1 6033 0 1 1 0 0 0 15822
## 154 6154 0 0 0 1 0 0 24662
## 188.1 6188 1 0 0 0 1 1 21100
## 32 6032 0 0 1 1 1 0 9894
## 13 6013 1 0 1 1 0 1 20227
## 144 6144 1 0 0 0 0 0 10261
## 16 6016 1 1 0 0 0 1 21714
## 159 6159 0 0 1 1 1 1 17199
## 112 6112 0 0 1 0 0 0 23382
## 187 6187 1 1 0 0 1 1 6766
## 167 6167 0 1 1 1 1 1 19045
## 161.1 6161 0 1 0 1 0 0 23118
## 78 6078 0 1 0 0 0 1 12626
##
## [[3]]$tae
## [1] 22
##
## [[3]]$tries
## [1] 42 35 22 39 39 32 41 28 27 43 29 36 32 39 33 39 31 36 38 36 36 39 35
## [24] 39 37 41 39 27 27 33 41 24 31 33 25 30 28 34 35 41
##
## [[3]]$error_states
## [1] 42 35 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22
## [24] 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22
##
##
## [[4]]
## [[4]]$areaid
## [1] 8304
##
## [[4]]$selection
## pid he female agemature car_owner house_owner working annualIncome
## 39 6039 0 0 0 0 1 1 3470
## 113 6113 0 0 0 1 1 1 18582
## 34 6034 1 0 0 1 1 0 8897
## 187 6187 1 1 0 0 1 1 6766
## 64 6064 1 1 0 0 1 0 19559
## 151 6151 0 0 1 0 0 0 18055
## 48 6048 0 1 1 0 0 1 4032
## 158 6158 1 0 0 1 0 0 755
## 167 6167 0 1 1 1 1 1 19045
## 133 6133 1 0 0 1 1 1 16596
## 2 6002 1 1 1 1 1 1 2458
## 191 6191 1 1 1 0 1 0 24296
## 192 6192 1 0 1 0 1 1 1649
## 177 6177 1 0 0 1 1 0 17102
## 171 6171 1 0 1 0 1 1 20752
## 38 6038 1 0 1 1 1 0 4508
## 96 6096 1 0 0 0 1 0 15223
## 157 6157 0 0 1 1 1 1 18831
## 173 6173 0 1 1 1 1 1 7025
## 79 6079 1 1 0 0 1 1 16117
## 26 6026 0 0 0 1 0 0 22364
## 165 6165 1 0 1 0 1 1 1125
## 43 6043 0 0 0 1 0 1 3170
## 78 6078 0 1 0 0 0 1 12626
## 91 6091 1 1 0 0 0 0 5695
## 174 6174 0 0 1 1 0 0 6798
## 94 6094 0 0 1 1 1 1 18017
## 23 6023 1 1 0 0 1 1 18244
## 198 6198 1 0 0 0 0 0 13894
## 57 6057 1 0 1 1 1 1 24504
## 165.1 6165 1 0 1 0 1 1 1125
## 93 6093 0 1 0 1 1 0 20926
## 109 6109 0 1 1 0 0 0 1120
## 140 6140 0 1 0 1 0 1 17720
## 2.1 6002 1 1 1 1 1 1 2458
## 38.1 6038 1 0 1 1 1 0 4508
## 85 6085 0 1 1 1 1 1 14183
## 50 6050 0 1 1 1 1 0 12243
## 183 6183 1 1 0 1 0 0 20091
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##
## [[5]]
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## [[5]]$tae
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##
##
## [[6]]
## [[6]]$areaid
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## [[6]]$selection
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## [[6]]$tae
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##
##
## [[7]]
## [[7]]$areaid
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##
## [[7]]$selection
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## [[7]]$tae
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## [[7]]$tries
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##
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##
##
## [[8]]
## [[8]]$areaid
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## [[8]]$selection
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## [[8]]$tae
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## [[8]]$tries
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##
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##
##
## [[9]]
## [[9]]$areaid
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## [[9]]$selection
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## [[9]]$tae
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##
## [[9]]$error_states
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##
##
## [[10]]
## [[10]]$areaid
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## [[10]]$selection
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## 7 6007 0 0 1 1 1 0 9535
## 103 6103 1 0 1 0 0 0 13352
## 29 6029 0 1 1 1 0 0 22979
## 105 6105 1 1 1 0 1 0 6742
## 21 6021 0 1 1 1 1 0 16588
## 171 6171 1 0 1 0 1 1 20752
## 28.1 6028 1 0 0 0 0 0 12338
##
## [[10]]$tae
## [1] 15
##
## [[10]]$tries
## [1] 34 31 31 28 30 22 25 35 22 34 34 39 30 28 15 25 28 40 31 25 28 24 30
## [24] 29 29 26 30 32 26 34 34 15 20 33 21 42 39 29 25 23
##
## [[10]]$error_states
## [1] 34 31 31 28 28 22 22 22 22 22 22 22 22 22 15 15 15 15 15 15 15 15 15
## [24] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##
##
## [[11]]
## [[11]]$areaid
## [1] 8311
##
## [[11]]$selection
## pid he female agemature car_owner house_owner working annualIncome
## 88 6088 0 0 0 0 1 0 23350
## 200 6200 0 1 1 0 1 1 12623
## 173 6173 0 1 1 1 1 1 7025
## 51 6051 0 0 1 1 0 1 6669
## 79 6079 1 1 0 0 1 1 16117
## 166 6166 0 1 1 0 1 1 15312
## 136 6136 0 1 1 0 0 0 1716
## 139 6139 1 1 0 1 0 0 24333
## 65 6065 1 0 0 1 0 0 11579
## 39 6039 0 0 0 0 1 1 3470
## 115 6115 1 0 1 1 1 0 17012
## 3 6003 0 1 0 0 1 0 9437
## 49 6049 0 1 1 1 0 1 7873
## 93 6093 0 1 0 1 1 0 20926
## 111 6111 0 0 1 0 1 1 16075
## 8 6008 0 0 1 1 0 1 1136
## 75 6075 0 0 0 0 0 0 5855
## 67 6067 1 1 1 1 0 1 19112
## 173.1 6173 0 1 1 1 1 1 7025
## 127 6127 0 1 0 1 1 0 12893
## 96 6096 1 0 0 0 1 0 15223
## 92 6092 0 1 0 0 1 0 22133
## 18 6018 0 0 0 1 1 1 23950
## 191 6191 1 1 1 0 1 0 24296
## 128 6128 0 1 0 0 1 1 1066
## 168 6168 0 1 0 1 1 0 17495
## 184 6184 0 0 1 1 1 0 18460
## 116 6116 0 1 0 0 1 0 4414
## 75.1 6075 0 0 0 0 0 0 5855
## 95 6095 1 1 0 0 0 1 4143
## 96.1 6096 1 0 0 0 1 0 15223
## 96.2 6096 1 0 0 0 1 0 15223
## 42 6042 0 0 1 1 0 1 19157
## 48 6048 0 1 1 0 0 1 4032
## 180 6180 0 1 0 0 1 1 14699
## 35 6035 1 1 1 0 1 0 23377
## 124 6124 0 0 0 1 0 1 15521
## 47 6047 1 1 0 0 0 0 14501
## 157 6157 0 0 1 1 1 1 18831
## 164 6164 0 1 0 0 1 1 21865
## 151 6151 0 0 1 0 0 0 18055
## 140 6140 0 1 0 1 0 1 17720
## 113 6113 0 0 0 1 1 1 18582
## 181 6181 0 1 1 1 1 0 24356
## 126 6126 1 1 1 0 1 1 12386
## 6 6006 1 0 0 1 0 1 16695
## 68 6068 0 1 0 1 1 0 20739
## 107 6107 1 1 0 0 0 0 2070
## 69 6069 1 1 1 1 1 1 15149
## 135 6135 1 0 1 0 0 1 14497
## 99 6099 1 0 1 0 1 0 15471
## 145 6145 1 1 0 1 0 1 9894
## 19 6019 0 1 1 1 0 0 4546
## 198 6198 1 0 0 0 0 0 13894
## 59 6059 1 0 1 0 0 1 18409
## 69.1 6069 1 1 1 1 1 1 15149
## 129 6129 0 1 1 1 0 0 5539
## 47.1 6047 1 1 0 0 0 0 14501
##
## [[11]]$tae
## [1] 30
##
## [[11]]$tries
## [1] 35 39 35 35 46 36 44 41 44 33 45 37 45 38 42 40 36 37 45 48 51 44 46
## [24] 33 36 44 43 38 39 40 36 30 36 42 39 41 35 32 44 44
##
## [[11]]$error_states
## [1] 35 35 35 35 35 35 35 35 35 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [24] 33 33 33 33 33 33 33 33 30 30 30 30 30 30 30 30 30
##
##
## [[12]]
## [[12]]$areaid
## [1] 8312
##
## [[12]]$selection
## pid he female agemature car_owner house_owner working annualIncome
## 174 6174 0 0 1 1 0 0 6798
## 43 6043 0 0 0 1 0 1 3170
## 118 6118 1 1 1 0 0 1 5610
## 10 6010 0 1 1 0 1 0 10119
## 129 6129 0 1 1 1 0 0 5539
## 37 6037 0 0 1 1 0 0 7093
## 172 6172 1 0 1 0 0 1 14346
## 142 6142 0 1 1 1 0 0 24860
## 9 6009 1 1 1 1 0 0 24170
## 101 6101 0 1 1 1 0 1 21034
## 183 6183 1 1 0 1 0 0 20091
## 100 6100 0 1 1 1 1 0 19540
## 67 6067 1 1 1 1 0 1 19112
## 58 6058 1 0 0 0 0 0 22892
## 6 6006 1 0 0 1 0 1 16695
## 87 6087 1 1 1 1 1 0 4265
## 93 6093 0 1 0 1 1 0 20926
## 164 6164 0 1 0 0 1 1 21865
## 111 6111 0 0 1 0 1 1 16075
## 31 6031 0 0 1 0 1 1 10229
## 45 6045 0 0 0 0 1 1 13377
## 154 6154 0 0 0 1 0 0 24662
## 92 6092 0 1 0 0 1 0 22133
## 189 6189 0 1 0 0 0 1 3754
## 195 6195 1 0 0 1 0 1 21223
## 123 6123 1 1 1 1 0 0 9458
## 80 6080 1 0 0 0 1 0 14511
## 147 6147 1 0 1 0 1 0 8914
## 132 6132 1 1 0 1 1 1 12931
## 95 6095 1 1 0 0 0 1 4143
## 43.1 6043 0 0 0 1 0 1 3170
## 7 6007 0 0 1 1 1 0 9535
## 88 6088 0 0 0 0 1 0 23350
## 18 6018 0 0 0 1 1 1 23950
## 45.1 6045 0 0 0 0 1 1 13377
## 158 6158 1 0 0 1 0 0 755
## 38 6038 1 0 1 1 1 0 4508
## 149 6149 0 0 0 0 0 1 21917
## 141 6141 0 0 0 0 0 1 8217
## 19 6019 0 1 1 1 0 0 4546
## 41 6041 1 1 1 1 1 1 997
## 130 6130 1 1 1 1 1 1 7340
## 150 6150 0 1 0 1 1 0 23632
## 187 6187 1 1 0 0 1 1 6766
## 161 6161 0 1 0 1 0 0 23118
## 135 6135 1 0 1 0 0 1 14497
## 183.1 6183 1 1 0 1 0 0 20091
## 68 6068 0 1 0 1 1 0 20739
## 26 6026 0 0 0 1 0 0 22364
## 31.1 6031 0 0 1 0 1 1 10229
## 57 6057 1 0 1 1 1 1 24504
## 200 6200 0 1 1 0 1 1 12623
## 106 6106 1 1 1 1 0 0 11994
## 92.1 6092 0 1 0 0 1 0 22133
## 56 6056 0 1 0 1 1 0 19291
## 3 6003 0 1 0 0 1 0 9437
## 87.1 6087 1 1 1 1 1 0 4265
## 162 6162 1 1 1 0 1 1 8955
## 62 6062 0 0 0 0 0 0 5375
## 188 6188 1 0 0 0 1 1 21100
## 45.2 6045 0 0 0 0 1 1 13377
## 137 6137 1 1 0 0 0 0 11019
## 120 6120 0 0 0 1 0 0 1025
## 10.1 6010 0 1 1 0 1 0 10119
## 16 6016 1 1 0 0 0 1 21714
## 122 6122 0 1 0 1 0 1 5706
## 19.1 6019 0 1 1 1 0 0 4546
## 196 6196 1 0 1 0 0 1 17683
##
## [[12]]$tae
## [1] 32
##
## [[12]]$tries
## [1] 55 33 42 40 42 42 42 46 39 41 39 43 38 49 34 41 48 41 40 33 57 44 40
## [24] 32 45 34 46 35 35 35 36 49 38 33 34 40 40 40 44 42
##
## [[12]]$error_states
## [1] 55 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [24] 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32
##
##
## [[13]]
## [[13]]$areaid
## [1] 8313
##
## [[13]]$selection
## pid he female agemature car_owner house_owner working annualIncome
## 99 6099 1 0 1 0 1 0 15471
## 164 6164 0 1 0 0 1 1 21865
## 197 6197 0 0 1 1 0 1 16944
## 81 6081 0 1 0 0 0 0 19338
## 41 6041 1 1 1 1 1 1 997
## 144 6144 1 0 0 0 0 0 10261
## 133 6133 1 0 0 1 1 1 16596
## 46 6046 1 0 0 1 1 0 24515
## 87 6087 1 1 1 1 1 0 4265
## 22 6022 0 1 0 1 0 1 2871
## 113 6113 0 0 0 1 1 1 18582
## 104 6104 0 0 1 1 1 1 14046
## 44 6044 1 0 1 0 0 0 1844
## 29 6029 0 1 1 1 0 0 22979
## 61 6061 0 0 0 1 1 0 1202
## 57 6057 1 0 1 1 1 1 24504
## 40 6040 1 1 0 0 0 0 13271
## 33 6033 0 1 1 0 0 0 15822
## 123 6123 1 1 1 1 0 0 9458
## 167 6167 0 1 1 1 1 1 19045
## 10 6010 0 1 1 0 1 0 10119
## 125 6125 0 1 0 1 0 1 16596
## 190 6190 0 1 0 0 0 1 6535
## 52 6052 0 1 0 1 1 1 21240
## 160 6160 1 1 1 1 0 1 13746
## 198 6198 1 0 0 0 0 0 13894
## 175 6175 1 0 0 0 0 0 4067
## 11 6011 1 1 1 1 0 1 14821
## 70 6070 1 0 0 1 1 1 16285
## 169 6169 1 0 1 1 1 0 3719
## 14 6014 1 1 0 1 1 1 6014
## 58 6058 1 0 0 0 0 0 22892
## 61.1 6061 0 0 0 1 1 0 1202
## 145 6145 1 1 0 1 0 1 9894
## 157 6157 0 0 1 1 1 1 18831
## 46.1 6046 1 0 0 1 1 0 24515
## 97 6097 1 1 1 0 1 1 21521
## 162 6162 1 1 1 0 1 1 8955
## 196 6196 1 0 1 0 0 1 17683
## 53 6053 1 0 1 0 0 0 8081
## 188 6188 1 0 0 0 1 1 21100
## 169.1 6169 1 0 1 1 1 0 3719
## 118 6118 1 1 1 0 0 1 5610
## 41.1 6041 1 1 1 1 1 1 997
## 134 6134 0 0 0 1 1 0 13271
## 178 6178 0 1 0 1 0 1 5271
## 140 6140 0 1 0 1 0 1 17720
## 162.1 6162 1 1 1 0 1 1 8955
## 100 6100 0 1 1 1 1 0 19540
## 79 6079 1 1 0 0 1 1 16117
## 59 6059 1 0 1 0 0 1 18409
## 178.1 6178 0 1 0 1 0 1 5271
## 163 6163 0 1 0 0 1 1 9332
## 123.1 6123 1 1 1 1 0 0 9458
## 151 6151 0 0 1 0 0 0 18055
## 185 6185 1 0 1 1 1 1 12862
## 11.1 6011 1 1 1 1 0 1 14821
## 4 6004 1 0 0 1 0 0 22130
## 41.2 6041 1 1 1 1 1 1 997
## 123.2 6123 1 1 1 1 0 0 9458
##
## [[13]]$tae
## [1] 37
##
## [[13]]$tries
## [1] 48 44 47 42 57 38 44 46 53 43 53 47 54 43 53 48 41 48 44 46 43 52 41
## [24] 47 51 59 43 38 48 55 55 52 51 51 58 37 43 39 40 49
##
## [[13]]$error_states
## [1] 48 44 44 42 42 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38
## [24] 38 38 38 38 38 38 38 38 38 38 38 38 37 37 37 37 37
##
##
##
## Slot "iterations":
## [1] 40
length( try01@results )
## [1] 13
The results object, holds the results for 4 areas. For each area, the microsimulation object holds a number of information. Let’s examine the third area of our microsimulation.
area03 = try01@results[[3]]
area03
$areaid
[1] 8303
$selection
pid he female agemature car_owner house_owner working annualIncome
158 6158 1 0 0 1 0 0 755
35 6035 1 1 1 0 1 0 23377
94 6094 0 0 1 1 1 1 18017
150 6150 0 1 0 1 1 0 23632
177 6177 1 0 0 1 1 0 17102
45 6045 0 0 0 0 1 1 13377
94.1 6094 0 0 1 1 1 1 18017
96 6096 1 0 0 0 1 0 15223
151 6151 0 0 1 0 0 0 18055
174 6174 0 0 1 1 0 0 6798
174.1 6174 0 0 1 1 0 0 6798
164 6164 0 1 0 0 1 1 21865
139 6139 1 1 0 1 0 0 24333
143 6143 0 0 1 0 0 0 4922
2 6002 1 1 1 1 1 1 2458
170 6170 0 1 1 0 0 0 12948
98 6098 0 1 1 0 0 0 23423
188 6188 1 0 0 0 1 1 21100
141 6141 0 0 0 0 0 1 8217
184 6184 0 0 1 1 1 0 18460
59 6059 1 0 1 0 0 1 18409
122 6122 0 1 0 1 0 1 5706
45.1 6045 0 0 0 0 1 1 13377
59.1 6059 1 0 1 0 0 1 18409
195 6195 1 0 0 1 0 1 21223
71 6071 0 0 0 1 0 0 23451
60 6060 0 0 0 1 0 0 7352
15 6015 0 1 1 0 0 0 19546
108 6108 0 1 1 0 1 0 19083
130 6130 1 1 1 1 1 1 7340
103 6103 1 0 1 0 0 0 13352
84 6084 0 1 0 1 0 0 24604
178 6178 0 1 0 1 0 1 5271
153 6153 0 0 0 1 0 1 13642
147 6147 1 0 1 0 1 0 8914
48 6048 0 1 1 0 0 1 4032
27 6027 1 0 1 1 0 1 1347
153.1 6153 0 0 0 1 0 1 13642
161 6161 0 1 0 1 0 0 23118
130.1 6130 1 1 1 1 1 1 7340
33 6033 0 1 1 0 0 0 15822
184.1 6184 0 0 1 1 1 0 18460
71.1 6071 0 0 0 1 0 0 23451
90 6090 0 1 0 1 1 1 20528
18 6018 0 0 0 1 1 1 23950
33.1 6033 0 1 1 0 0 0 15822
154 6154 0 0 0 1 0 0 24662
188.1 6188 1 0 0 0 1 1 21100
32 6032 0 0 1 1 1 0 9894
13 6013 1 0 1 1 0 1 20227
144 6144 1 0 0 0 0 0 10261
16 6016 1 1 0 0 0 1 21714
159 6159 0 0 1 1 1 1 17199
112 6112 0 0 1 0 0 0 23382
187 6187 1 1 0 0 1 1 6766
167 6167 0 1 1 1 1 1 19045
161.1 6161 0 1 0 1 0 0 23118
78 6078 0 1 0 0 0 1 12626
$tae
[1] 22
$tries
[1] 42 35 22 39 39 32 41 28 27 43 29 36 32 39 33 39 31 36 38 36 36 39 35
[24] 39 37 41 39 27 27 33 41 24 31 33 25 30 28 34 35 41
$error_states
[1] 42 35 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22
[24] 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22
As can be see, the area03\(areaid_ is the name of this area. The _area03\)selection object is the generated microdata of the area is the following:
area03$selection
pid he female agemature car_owner house_owner working annualIncome
158 6158 1 0 0 1 0 0 755
35 6035 1 1 1 0 1 0 23377
94 6094 0 0 1 1 1 1 18017
150 6150 0 1 0 1 1 0 23632
177 6177 1 0 0 1 1 0 17102
45 6045 0 0 0 0 1 1 13377
94.1 6094 0 0 1 1 1 1 18017
96 6096 1 0 0 0 1 0 15223
151 6151 0 0 1 0 0 0 18055
174 6174 0 0 1 1 0 0 6798
174.1 6174 0 0 1 1 0 0 6798
164 6164 0 1 0 0 1 1 21865
139 6139 1 1 0 1 0 0 24333
143 6143 0 0 1 0 0 0 4922
2 6002 1 1 1 1 1 1 2458
170 6170 0 1 1 0 0 0 12948
98 6098 0 1 1 0 0 0 23423
188 6188 1 0 0 0 1 1 21100
141 6141 0 0 0 0 0 1 8217
184 6184 0 0 1 1 1 0 18460
59 6059 1 0 1 0 0 1 18409
122 6122 0 1 0 1 0 1 5706
45.1 6045 0 0 0 0 1 1 13377
59.1 6059 1 0 1 0 0 1 18409
195 6195 1 0 0 1 0 1 21223
71 6071 0 0 0 1 0 0 23451
60 6060 0 0 0 1 0 0 7352
15 6015 0 1 1 0 0 0 19546
108 6108 0 1 1 0 1 0 19083
130 6130 1 1 1 1 1 1 7340
103 6103 1 0 1 0 0 0 13352
84 6084 0 1 0 1 0 0 24604
178 6178 0 1 0 1 0 1 5271
153 6153 0 0 0 1 0 1 13642
147 6147 1 0 1 0 1 0 8914
48 6048 0 1 1 0 0 1 4032
27 6027 1 0 1 1 0 1 1347
153.1 6153 0 0 0 1 0 1 13642
161 6161 0 1 0 1 0 0 23118
130.1 6130 1 1 1 1 1 1 7340
33 6033 0 1 1 0 0 0 15822
184.1 6184 0 0 1 1 1 0 18460
71.1 6071 0 0 0 1 0 0 23451
90 6090 0 1 0 1 1 1 20528
18 6018 0 0 0 1 1 1 23950
33.1 6033 0 1 1 0 0 0 15822
154 6154 0 0 0 1 0 0 24662
188.1 6188 1 0 0 0 1 1 21100
32 6032 0 0 1 1 1 0 9894
13 6013 1 0 1 1 0 1 20227
144 6144 1 0 0 0 0 0 10261
16 6016 1 1 0 0 0 1 21714
159 6159 0 0 1 1 1 1 17199
112 6112 0 0 1 0 0 0 23382
187 6187 1 1 0 0 1 1 6766
167 6167 0 1 1 1 1 1 19045
161.1 6161 0 1 0 1 0 0 23118
78 6078 0 1 0 0 0 1 12626
which can be exported to a csv file with the following command:
write.table(area03$selection, "area03Microdata.csv", sep=",")
The tae oject is the Total Absolute Error of the fitting process for this area. The tries object holds the TAE of the random selections during the fitting process. This is the error of each combination, even if the combination isn’t selected. Finally, the area03$error_states holds the progress of the TAE during the iterations. The length of this array, is the same as the number of iterations and it is used mainly for tracking and then plotting the progress of the TAE. The last two arrays, can be plotted in order to examine the progress of the fitting process:
plotTries(insms=try01, 3)
The above plot, shows the progress of TAE during the fitting process. The horizontal axis represents the iterations and the vertical the TAE. Each red circle, represents the TAE of a randomly selected combination of individuals, in each iteration. The dark filled dots, represent, the TAE of the currently selected best combination of individuals. This graph is very useful when the fitting algorithm is Simulated Annealing as it depicts the tolerance of the algorithm during the first iterations.
This was a small tutorial on preparing microdata with the use of R and the sms library. Please fill free to contact me regarding the sms library.