describe.RdProvides a natural language description of a known totals data frame to be used for a calibration task.
pop.desc(pop.totals, ...) # S3 method for pop.totals pop.desc(pop.totals, ...) # S3 method for spc.pop pop.desc(pop.totals, verbose = FALSE, ...)
| pop.totals | An object of class |
|---|---|
| verbose | Fully describe the control totals of a special purpose calibration task? |
| ... | Parameters for future extensions (currently unused). |
Function pop.template generates a template (i.e. empty) data frame of class pop.totals, which is appropriate to store the known totals of a given calibration task. Afterwards, the template data frame must be filled with actual figures.
When the sampling frame of the survey is available and the actual population totals can be calculated from this source, function fill.template (i) automatically computes the totals of the auxiliary variables from the sampling frame, (ii) safely arranges and formats these values according to the template structure. Therefore, function fill.template avoids any need for the user to understand, comply with, or even be aware of, the structure of the template that is being filled.
On the contrary, when the population totals are available to the user as such, that is in the form of already computed aggregated values (e.g. because they come from an external source, like a Population Census), it is up to the user to correctly fill the template, that is to put the right values in the right slots of the prepared template.
Function pop.desc has been designed for users who cannot take advantage of function fill.template, to help them understand the structure of the known totals template, in order to safely fill it with actual figures.
Invoking pop.desc will print on screen a detailed natural language description of the structure of the input pop.totals object. Such description will clarify how known totals are organized inside the template slots.
The main purpose of the function is to print on screen, anyway it returns invisibly the input pop.totals object (as print would do).
e.calibrate for calibrating weights, pop.template for the definition of the class pop.totals and to build a template data frame for known population totals, fill.template to automatically fill the template when a sampling frame is available.
## First prepare some design objects to work with: # Load household data: data(data.examples) # Build a design object: exdes<-e.svydesign(data=example,ids=~towcod+famcod,strata=~SUPERSTRATUM, weights=~weight) # Load sbs data: data(sbs) # Build a design object: sbsdes<-e.svydesign(data=sbs,ids=~id,strata=~strata,weights=~weight,fpc=~fpc) ## Now build some known totals templates that (after having been filled by ## actual figures) could be used to calibrate the design objects above, ## and explore the corresponding natural language description: #################################### ## Some simple and small examples ## #################################### expop<-pop.template(exdes,calmodel=~1) expop#> (Intercept) #> 1 NApop.desc(expop)#> # Data frame of known totals for a *global* calibration task #> - Number of rows: 1 #> - Number of columns: 1 #> - Number of known totals: 1 #> #> # The data frame structure is as follows #> ## Column 1 identifies known totals organized into *1 BLOCK* #> #> - BLOCK 1 ---------------------------------------------------------------- #> - Benchmark: Count of elementary units #> - Known totals: 1 #> - Auxiliary variables: 1 #> - Column: 1 #> #> 1 #> aux "(Intercept)" #> #>#> (Intercept) sexm #> 1 NA NApop.desc(expop)#> # Data frame of known totals for a *global* calibration task #> - Number of rows: 1 #> - Number of columns: 2 #> - Number of known totals: 2 #> #> # The data frame structure is as follows #> ## Columns 1-2 identify known totals organized into *2 BLOCKS* #> #> - BLOCK 1 ---------------------------------------------------------------- #> - Benchmark: Count of elementary units #> - Known totals: 1 #> - Auxiliary variables: 1 #> - Column: 1 #> #> 1 #> aux "(Intercept)" #> #> #> - BLOCK 2 ---------------------------------------------------------------- #> - Benchmark: Counts of sex #> - Known totals: 1 #> - Auxiliary variables: 1 #> - Column: 2 #> #> 2 #> aux "m" #> #>#> sexf sexm #> 1 NA NApop.desc(expop)#> # Data frame of known totals for a *global* calibration task #> - Number of rows: 1 #> - Number of columns: 2 #> - Number of known totals: 2 #> #> # The data frame structure is as follows #> ## Columns 1-2 identify known totals organized into *1 BLOCK* #> #> - BLOCK 1 ---------------------------------------------------------------- #> - Benchmark: Counts of sex #> - Known totals: 2 #> - Auxiliary variables: 2 #> - Columns: 1-2 #> #> 1 2 #> aux "f" "m" #> #>#> procod (Intercept) x1 x2 x3 #> 1 8 NA NA NA NA #> 2 9 NA NA NA NA #> 3 10 NA NA NA NA #> 4 11 NA NA NA NA #> 5 30 NA NA NA NA #> 6 31 NA NA NA NA #> 7 32 NA NA NA NA #> 8 54 NA NA NA NA #> 9 55 NA NA NA NA #> 10 93 NA NA NA NApop.desc(expop)#> # Data frame of known totals for a *partitioned* calibration task #> - Number of rows: 10 #> - Number of columns: 5 #> - Number of known totals: 40 #> #> # The data frame structure is as follows #> ## Column 1 identifies 10 *calibration domains* (one for each row) #> #> procod #> 1 8 #> 2 9 #> 3 10 #> 4 11 #> 5 30 #> 6 31 #> 7 32 #> 8 54 #> 9 55 #> 10 93 #> #> ## Columns 2-5 identify known totals organized into *4 BLOCKS* #> #> - BLOCK 1 ---------------------------------------------------------------- #> - Benchmark: Count of elementary units #> - Known totals: 10 #> - Auxiliary variables: 1 #> - Column: 2 #> #> 2 #> aux "(Intercept)" #> #> #> - BLOCK 2 ---------------------------------------------------------------- #> - Benchmark: Total of x1 #> - Known totals: 10 #> - Auxiliary variables: 1 #> - Column: 3 #> #> 3 #> aux "x1" #> #> #> - BLOCK 3 ---------------------------------------------------------------- #> - Benchmark: Total of x2 #> - Known totals: 10 #> - Auxiliary variables: 1 #> - Column: 4 #> #> 4 #> aux "x2" #> #> #> - BLOCK 4 ---------------------------------------------------------------- #> - Benchmark: Total of x3 #> - Known totals: 10 #> - Auxiliary variables: 1 #> - Column: 5 #> #> 5 #> aux "x3" #> #>#> sexf:marstatmarried sexm:marstatmarried sexf:marstatunmarried #> 1 NA NA NA #> sexm:marstatunmarried sexf:marstatwidowed sexm:marstatwidowed #> 1 NA NA NApop.desc(expop)#> # Data frame of known totals for a *global* calibration task #> - Number of rows: 1 #> - Number of columns: 6 #> - Number of known totals: 6 #> #> # The data frame structure is as follows #> ## Columns 1-6 identify known totals organized into *1 BLOCK* #> #> - BLOCK 1 ---------------------------------------------------------------- #> - Benchmark: Counts of sex:marstat #> - Known totals: 6 #> - Auxiliary variables: 6 #> - Columns: 1-6 #> #> 1 2 3 4 5 6 #> aux "f:married" "m:married" "f:unmarried" "m:unmarried" "f:widowed" "m:widowed" #> #>#> sexf sexm marstatunmarried marstatwidowed sexm:marstatunmarried #> 1 NA NA NA NA NA #> sexm:marstatwidowed #> 1 NApop.desc(expop)#> # Data frame of known totals for a *global* calibration task #> - Number of rows: 1 #> - Number of columns: 6 #> - Number of known totals: 6 #> #> # The data frame structure is as follows #> ## Columns 1-6 identify known totals organized into *3 BLOCKS* #> #> - BLOCK 1 ---------------------------------------------------------------- #> - Benchmark: Counts of sex #> - Known totals: 2 #> - Auxiliary variables: 2 #> - Columns: 1-2 #> #> 1 2 #> aux "f" "m" #> #> #> - BLOCK 2 ---------------------------------------------------------------- #> - Benchmark: Counts of marstat #> - Known totals: 2 #> - Auxiliary variables: 2 #> - Columns: 3-4 #> #> 3 4 #> aux "unmarried" "widowed" #> #> #> - BLOCK 3 ---------------------------------------------------------------- #> - Benchmark: Counts of sex:marstat #> - Known totals: 2 #> - Auxiliary variables: 2 #> - Columns: 5-6 #> #> 5 6 #> aux "m:unmarried" "m:widowed" #> #>#> sex marstat (Intercept) #> 1 f married NA #> 2 f unmarried NA #> 3 f widowed NA #> 4 m married NA #> 5 m unmarried NA #> 6 m widowed NApop.desc(expop)#> # Data frame of known totals for a *partitioned* calibration task #> - Number of rows: 6 #> - Number of columns: 3 #> - Number of known totals: 6 #> #> # The data frame structure is as follows #> ## Columns 1-2 identify 6 *calibration domains* (one for each row) #> #> sex marstat #> 1 f married #> 2 f unmarried #> 3 f widowed #> 4 m married #> 5 m unmarried #> 6 m widowed #> #> ## Column 3 identifies known totals organized into *1 BLOCK* #> #> - BLOCK 1 ---------------------------------------------------------------- #> - Benchmark: Count of elementary units #> - Known totals: 6 #> - Auxiliary variables: 1 #> - Column: 3 #> #> 3 #> aux "(Intercept)" #> #>#> regcod sex x1 age5c1:marstatmarried age5c2:marstatmarried #> 1 6 f NA NA NA #> 2 6 m NA NA NA #> 3 7 f NA NA NA #> 4 7 m NA NA NA #> 5 10 f NA NA NA #> 6 10 m NA NA NA #> age5c3:marstatmarried age5c4:marstatmarried age5c5:marstatmarried #> 1 NA NA NA #> 2 NA NA NA #> 3 NA NA NA #> 4 NA NA NA #> 5 NA NA NA #> 6 NA NA NA #> age5c1:marstatunmarried age5c2:marstatunmarried age5c3:marstatunmarried #> 1 NA NA NA #> 2 NA NA NA #> 3 NA NA NA #> 4 NA NA NA #> 5 NA NA NA #> 6 NA NA NA #> age5c4:marstatunmarried age5c5:marstatunmarried age5c1:marstatwidowed #> 1 NA NA NA #> 2 NA NA NA #> 3 NA NA NA #> 4 NA NA NA #> 5 NA NA NA #> 6 NA NA NA #> age5c2:marstatwidowed age5c3:marstatwidowed age5c4:marstatwidowed #> 1 NA NA NA #> 2 NA NA NA #> 3 NA NA NA #> 4 NA NA NA #> 5 NA NA NA #> 6 NA NA NA #> age5c5:marstatwidowed #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NApop.desc(expop)#> # Data frame of known totals for a *partitioned* calibration task #> - Number of rows: 6 #> - Number of columns: 18 #> - Number of known totals: 96 #> #> # The data frame structure is as follows #> ## Columns 1-2 identify 6 *calibration domains* (one for each row) #> #> regcod sex #> 1 6 f #> 2 6 m #> 3 7 f #> 4 7 m #> 5 10 f #> 6 10 m #> #> ## Columns 3-18 identify known totals organized into *2 BLOCKS* #> #> - BLOCK 1 ---------------------------------------------------------------- #> - Benchmark: Total of x1 #> - Known totals: 6 #> - Auxiliary variables: 1 #> - Column: 3 #> #> 3 #> aux "x1" #> #> #> - BLOCK 2 ---------------------------------------------------------------- #> - Benchmark: Counts of age5c:marstat #> - Known totals: 90 #> - Auxiliary variables: 15 #> - Columns: 4-18 #> #> 4 5 6 7 8 9 #> aux "1:married" "2:married" "3:married" "4:married" "5:married" "1:unmarried" #> 10 11 12 13 14 #> aux "2:unmarried" "3:unmarried" "4:unmarried" "5:unmarried" "1:widowed" #> 15 16 17 18 #> aux "2:widowed" "3:widowed" "4:widowed" "5:widowed" #> #>############################################ ## Some more involved and bigger examples ## ############################################ expop<-pop.template(exdes,calmodel=~sex:age10c:regcod + sex:age5c:procod - 1) expop#> sexf:age10c1:regcod6 sexm:age10c1:regcod6 sexf:age10c2:regcod6 #> 1 NA NA NA #> sexm:age10c2:regcod6 sexf:age10c3:regcod6 sexm:age10c3:regcod6 #> 1 NA NA NA #> sexf:age10c4:regcod6 sexm:age10c4:regcod6 sexf:age10c5:regcod6 #> 1 NA NA NA #> sexm:age10c5:regcod6 sexf:age10c6:regcod6 sexm:age10c6:regcod6 #> 1 NA NA NA #> sexf:age10c7:regcod6 sexm:age10c7:regcod6 sexf:age10c8:regcod6 #> 1 NA NA NA #> sexm:age10c8:regcod6 sexf:age10c9:regcod6 sexm:age10c9:regcod6 #> 1 NA NA NA #> sexf:age10c10:regcod6 sexm:age10c10:regcod6 sexf:age10c1:regcod7 #> 1 NA NA NA #> sexm:age10c1:regcod7 sexf:age10c2:regcod7 sexm:age10c2:regcod7 #> 1 NA NA NA #> sexf:age10c3:regcod7 sexm:age10c3:regcod7 sexf:age10c4:regcod7 #> 1 NA NA NA #> sexm:age10c4:regcod7 sexf:age10c5:regcod7 sexm:age10c5:regcod7 #> 1 NA NA NA #> sexf:age10c6:regcod7 sexm:age10c6:regcod7 sexf:age10c7:regcod7 #> 1 NA NA NA #> sexm:age10c7:regcod7 sexf:age10c8:regcod7 sexm:age10c8:regcod7 #> 1 NA NA NA #> sexf:age10c9:regcod7 sexm:age10c9:regcod7 sexf:age10c10:regcod7 #> 1 NA NA NA #> sexm:age10c10:regcod7 sexf:age10c1:regcod10 sexm:age10c1:regcod10 #> 1 NA NA NA #> sexf:age10c2:regcod10 sexm:age10c2:regcod10 sexf:age10c3:regcod10 #> 1 NA NA NA #> sexm:age10c3:regcod10 sexf:age10c4:regcod10 sexm:age10c4:regcod10 #> 1 NA NA NA #> sexf:age10c5:regcod10 sexm:age10c5:regcod10 sexf:age10c6:regcod10 #> 1 NA NA NA #> sexm:age10c6:regcod10 sexf:age10c7:regcod10 sexm:age10c7:regcod10 #> 1 NA NA NA #> sexf:age10c8:regcod10 sexm:age10c8:regcod10 sexf:age10c9:regcod10 #> 1 NA NA NA #> sexm:age10c9:regcod10 sexf:age10c10:regcod10 sexm:age10c10:regcod10 #> 1 NA NA NA #> sexf:age5c1:procod8 sexm:age5c1:procod8 sexf:age5c2:procod8 #> 1 NA NA NA #> sexm:age5c2:procod8 sexf:age5c3:procod8 sexm:age5c3:procod8 #> 1 NA NA NA #> sexf:age5c4:procod8 sexm:age5c4:procod8 sexf:age5c5:procod8 #> 1 NA NA NA #> sexm:age5c5:procod8 sexf:age5c1:procod9 sexm:age5c1:procod9 #> 1 NA NA NA #> sexf:age5c2:procod9 sexm:age5c2:procod9 sexf:age5c3:procod9 #> 1 NA NA NA #> sexm:age5c3:procod9 sexf:age5c4:procod9 sexm:age5c4:procod9 #> 1 NA NA NA #> sexf:age5c5:procod9 sexm:age5c5:procod9 sexf:age5c1:procod10 #> 1 NA NA NA #> sexm:age5c1:procod10 sexf:age5c2:procod10 sexm:age5c2:procod10 #> 1 NA NA NA #> sexf:age5c3:procod10 sexm:age5c3:procod10 sexf:age5c4:procod10 #> 1 NA NA NA #> sexm:age5c4:procod10 sexf:age5c5:procod10 sexm:age5c5:procod10 #> 1 NA NA NA #> sexf:age5c1:procod11 sexm:age5c1:procod11 sexf:age5c2:procod11 #> 1 NA NA NA #> sexm:age5c2:procod11 sexf:age5c3:procod11 sexm:age5c3:procod11 #> 1 NA NA NA #> sexf:age5c4:procod11 sexm:age5c4:procod11 sexf:age5c5:procod11 #> 1 NA NA NA #> sexm:age5c5:procod11 sexf:age5c1:procod30 sexm:age5c1:procod30 #> 1 NA NA NA #> sexf:age5c2:procod30 sexm:age5c2:procod30 sexf:age5c3:procod30 #> 1 NA NA NA #> sexm:age5c3:procod30 sexf:age5c4:procod30 sexm:age5c4:procod30 #> 1 NA NA NA #> sexf:age5c5:procod30 sexm:age5c5:procod30 sexf:age5c1:procod31 #> 1 NA NA NA #> sexm:age5c1:procod31 sexf:age5c2:procod31 sexm:age5c2:procod31 #> 1 NA NA NA #> sexf:age5c3:procod31 sexm:age5c3:procod31 sexf:age5c4:procod31 #> 1 NA NA NA #> sexm:age5c4:procod31 sexf:age5c5:procod31 sexm:age5c5:procod31 #> 1 NA NA NA #> sexf:age5c1:procod32 sexm:age5c1:procod32 sexf:age5c2:procod32 #> 1 NA NA NA #> sexm:age5c2:procod32 sexf:age5c3:procod32 sexm:age5c3:procod32 #> 1 NA NA NA #> sexf:age5c4:procod32 sexm:age5c4:procod32 sexf:age5c5:procod32 #> 1 NA NA NA #> sexm:age5c5:procod32 sexf:age5c1:procod54 sexm:age5c1:procod54 #> 1 NA NA NA #> sexf:age5c2:procod54 sexm:age5c2:procod54 sexf:age5c3:procod54 #> 1 NA NA NA #> sexm:age5c3:procod54 sexf:age5c4:procod54 sexm:age5c4:procod54 #> 1 NA NA NA #> sexf:age5c5:procod54 sexm:age5c5:procod54 sexf:age5c1:procod55 #> 1 NA NA NA #> sexm:age5c1:procod55 sexf:age5c2:procod55 sexm:age5c2:procod55 #> 1 NA NA NA #> sexf:age5c3:procod55 sexm:age5c3:procod55 sexf:age5c4:procod55 #> 1 NA NA NA #> sexm:age5c4:procod55 sexf:age5c5:procod55 sexm:age5c5:procod55 #> 1 NA NA NA #> sexf:age5c1:procod93 sexm:age5c1:procod93 sexf:age5c2:procod93 #> 1 NA NA NA #> sexm:age5c2:procod93 sexf:age5c3:procod93 sexm:age5c3:procod93 #> 1 NA NA NA #> sexf:age5c4:procod93 sexm:age5c4:procod93 sexf:age5c5:procod93 #> 1 NA NA NA #> sexm:age5c5:procod93 #> 1 NApop.desc(expop)#> # Data frame of known totals for a *global* calibration task #> - Number of rows: 1 #> - Number of columns: 160 #> - Number of known totals: 160 #> #> # The data frame structure is as follows #> ## Columns 1-160 identify known totals organized into *2 BLOCKS* #> #> - BLOCK 1 ---------------------------------------------------------------- #> - Benchmark: Counts of sex:age10c:regcod #> - Known totals: 60 #> - Auxiliary variables: 60 #> - Columns: 1-60 #> #> 1 2 3 4 5 6 7 8 9 #> aux "f:1:6" "m:1:6" "f:2:6" "m:2:6" "f:3:6" "m:3:6" "f:4:6" "m:4:6" "f:5:6" #> 10 11 12 13 14 15 16 17 18 #> aux "m:5:6" "f:6:6" "m:6:6" "f:7:6" "m:7:6" "f:8:6" "m:8:6" "f:9:6" "m:9:6" #> 19 20 21 22 23 24 25 26 27 #> aux "f:10:6" "m:10:6" "f:1:7" "m:1:7" "f:2:7" "m:2:7" "f:3:7" "m:3:7" "f:4:7" #> 28 29 30 31 32 33 34 35 36 #> aux "m:4:7" "f:5:7" "m:5:7" "f:6:7" "m:6:7" "f:7:7" "m:7:7" "f:8:7" "m:8:7" #> 37 38 39 40 41 42 43 44 #> aux "f:9:7" "m:9:7" "f:10:7" "m:10:7" "f:1:10" "m:1:10" "f:2:10" "m:2:10" #> 45 46 47 48 49 50 51 52 #> aux "f:3:10" "m:3:10" "f:4:10" "m:4:10" "f:5:10" "m:5:10" "f:6:10" "m:6:10" #> 53 54 55 56 57 58 59 60 #> aux "f:7:10" "m:7:10" "f:8:10" "m:8:10" "f:9:10" "m:9:10" "f:10:10" "m:10:10" #> #> #> - BLOCK 2 ---------------------------------------------------------------- #> - Benchmark: Counts of sex:age5c:procod #> - Known totals: 100 #> - Auxiliary variables: 100 #> - Columns: 61-160 #> #> 61 62 63 64 65 66 67 68 69 #> aux "f:1:8" "m:1:8" "f:2:8" "m:2:8" "f:3:8" "m:3:8" "f:4:8" "m:4:8" "f:5:8" #> 70 71 72 73 74 75 76 77 78 #> aux "m:5:8" "f:1:9" "m:1:9" "f:2:9" "m:2:9" "f:3:9" "m:3:9" "f:4:9" "m:4:9" #> 79 80 81 82 83 84 85 86 #> aux "f:5:9" "m:5:9" "f:1:10" "m:1:10" "f:2:10" "m:2:10" "f:3:10" "m:3:10" #> 87 88 89 90 91 92 93 94 #> aux "f:4:10" "m:4:10" "f:5:10" "m:5:10" "f:1:11" "m:1:11" "f:2:11" "m:2:11" #> 95 96 97 98 99 100 101 102 #> aux "f:3:11" "m:3:11" "f:4:11" "m:4:11" "f:5:11" "m:5:11" "f:1:30" "m:1:30" #> 103 104 105 106 107 108 109 110 #> aux "f:2:30" "m:2:30" "f:3:30" "m:3:30" "f:4:30" "m:4:30" "f:5:30" "m:5:30" #> 111 112 113 114 115 116 117 118 #> aux "f:1:31" "m:1:31" "f:2:31" "m:2:31" "f:3:31" "m:3:31" "f:4:31" "m:4:31" #> 119 120 121 122 123 124 125 126 #> aux "f:5:31" "m:5:31" "f:1:32" "m:1:32" "f:2:32" "m:2:32" "f:3:32" "m:3:32" #> 127 128 129 130 131 132 133 134 #> aux "f:4:32" "m:4:32" "f:5:32" "m:5:32" "f:1:54" "m:1:54" "f:2:54" "m:2:54" #> 135 136 137 138 139 140 141 142 #> aux "f:3:54" "m:3:54" "f:4:54" "m:4:54" "f:5:54" "m:5:54" "f:1:55" "m:1:55" #> 143 144 145 146 147 148 149 150 #> aux "f:2:55" "m:2:55" "f:3:55" "m:3:55" "f:4:55" "m:4:55" "f:5:55" "m:5:55" #> 151 152 153 154 155 156 157 158 #> aux "f:1:93" "m:1:93" "f:2:93" "m:2:93" "f:3:93" "m:3:93" "f:4:93" "m:4:93" #> 159 160 #> aux "f:5:93" "m:5:93" #> #># equivalent to the one above (because procod is nested into regcod and # age10c is nested into age5c) expop<-pop.template(exdes,calmodel=~age10c+procod-1, partition=~regcod:sex:age5c) expop#> regcod sex age5c age10c1 age10c2 age10c3 age10c4 age10c5 age10c6 age10c7 #> 1 6 f 1 NA NA NA NA NA NA NA #> 2 6 f 2 NA NA NA NA NA NA NA #> 3 6 f 3 NA NA NA NA NA NA NA #> 4 6 f 4 NA NA NA NA NA NA NA #> 5 6 f 5 NA NA NA NA NA NA NA #> 6 6 m 1 NA NA NA NA NA NA NA #> 7 6 m 2 NA NA NA NA NA NA NA #> 8 6 m 3 NA NA NA NA NA NA NA #> 9 6 m 4 NA NA NA NA NA NA NA #> 10 6 m 5 NA NA NA NA NA NA NA #> 11 7 f 1 NA NA NA NA NA NA NA #> 12 7 f 2 NA NA NA NA NA NA NA #> 13 7 f 3 NA NA NA NA NA NA NA #> 14 7 f 4 NA NA NA NA NA NA NA #> 15 7 f 5 NA NA NA NA NA NA NA #> 16 7 m 1 NA NA NA NA NA NA NA #> 17 7 m 2 NA NA NA NA NA NA NA #> 18 7 m 3 NA NA NA NA NA NA NA #> 19 7 m 4 NA NA NA NA NA NA NA #> 20 7 m 5 NA NA NA NA NA NA NA #> 21 10 f 1 NA NA NA NA NA NA NA #> 22 10 f 2 NA NA NA NA NA NA NA #> 23 10 f 3 NA NA NA NA NA NA NA #> 24 10 f 4 NA NA NA NA NA NA NA #> 25 10 f 5 NA NA NA NA NA NA NA #> 26 10 m 1 NA NA NA NA NA NA NA #> 27 10 m 2 NA NA NA NA NA NA NA #> 28 10 m 3 NA NA NA NA NA NA NA #> 29 10 m 4 NA NA NA NA NA NA NA #> 30 10 m 5 NA NA NA NA NA NA NA #> age10c8 age10c9 age10c10 procod9 procod10 procod11 procod30 procod31 #> 1 NA NA NA NA NA NA NA NA #> 2 NA NA NA NA NA NA NA NA #> 3 NA NA NA NA NA NA NA NA #> 4 NA NA NA NA NA NA NA NA #> 5 NA NA NA NA NA NA NA NA #> 6 NA NA NA NA NA NA NA NA #> 7 NA NA NA NA NA NA NA NA #> 8 NA NA NA NA NA NA NA NA #> 9 NA NA NA NA NA NA NA NA #> 10 NA NA NA NA NA NA NA NA #> 11 NA NA NA NA NA NA NA NA #> 12 NA NA NA NA NA NA NA NA #> 13 NA NA NA NA NA NA NA NA #> 14 NA NA NA NA NA NA NA NA #> 15 NA NA NA NA NA NA NA NA #> 16 NA NA NA NA NA NA NA NA #> 17 NA NA NA NA NA NA NA NA #> 18 NA NA NA NA NA NA NA NA #> 19 NA NA NA NA NA NA NA NA #> 20 NA NA NA NA NA NA NA NA #> 21 NA NA NA NA NA NA NA NA #> 22 NA NA NA NA NA NA NA NA #> 23 NA NA NA NA NA NA NA NA #> 24 NA NA NA NA NA NA NA NA #> 25 NA NA NA NA NA NA NA NA #> 26 NA NA NA NA NA NA NA NA #> 27 NA NA NA NA NA NA NA NA #> 28 NA NA NA NA NA NA NA NA #> 29 NA NA NA NA NA NA NA NA #> 30 NA NA NA NA NA NA NA NA #> procod32 procod54 procod55 procod93 #> 1 NA NA NA NA #> 2 NA NA NA NA #> 3 NA NA NA NA #> 4 NA NA NA NA #> 5 NA NA NA NA #> 6 NA NA NA NA #> 7 NA NA NA NA #> 8 NA NA NA NA #> 9 NA NA NA NA #> 10 NA NA NA NA #> 11 NA NA NA NA #> 12 NA NA NA NA #> 13 NA NA NA NA #> 14 NA NA NA NA #> 15 NA NA NA NA #> 16 NA NA NA NA #> 17 NA NA NA NA #> 18 NA NA NA NA #> 19 NA NA NA NA #> 20 NA NA NA NA #> 21 NA NA NA NA #> 22 NA NA NA NA #> 23 NA NA NA NA #> 24 NA NA NA NA #> 25 NA NA NA NA #> 26 NA NA NA NA #> 27 NA NA NA NA #> 28 NA NA NA NA #> 29 NA NA NA NA #> 30 NA NA NA NApop.desc(expop)#> # Data frame of known totals for a *partitioned* calibration task #> - Number of rows: 30 #> - Number of columns: 22 #> - Number of known totals: 570 #> #> # The data frame structure is as follows #> ## Columns 1-3 identify 30 *calibration domains* (one for each row) #> #> regcod sex age5c #> 1 6 f 1 #> 2 6 f 2 #> 3 6 f 3 #> 4 6 f 4 #> 5 6 f 5 #> 6 6 m 1 #> 7 6 m 2 #> 8 6 m 3 #> 9 6 m 4 #> 10 6 m 5 #> 11 7 f 1 #> 12 7 f 2 #> 13 7 f 3 #> 14 7 f 4 #> 15 7 f 5 #> 16 7 m 1 #> 17 7 m 2 #> 18 7 m 3 #> 19 7 m 4 #> 20 7 m 5 #> 21 10 f 1 #> 22 10 f 2 #> 23 10 f 3 #> 24 10 f 4 #> 25 10 f 5 #> 26 10 m 1 #> 27 10 m 2 #> 28 10 m 3 #> 29 10 m 4 #> 30 10 m 5 #> #> ## Columns 4-22 identify known totals organized into *2 BLOCKS* #> #> - BLOCK 1 ---------------------------------------------------------------- #> - Benchmark: Counts of age10c #> - Known totals: 300 #> - Auxiliary variables: 10 #> - Columns: 4-13 #> #> 4 5 6 7 8 9 10 11 12 13 #> aux "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" #> #> #> - BLOCK 2 ---------------------------------------------------------------- #> - Benchmark: Counts of procod #> - Known totals: 270 #> - Auxiliary variables: 9 #> - Columns: 14-22 #> #> 14 15 16 17 18 19 20 21 22 #> aux "9" "10" "11" "30" "31" "32" "54" "55" "93" #> #># NOTE: Most of the entries of the template above will be structural zeros, # as can be seen in what follows: expop.HT<-aux.estimates(exdes, template=expop) expop.HT#> regcod sex age5c age10c1 age10c2 age10c3 age10c4 age10c5 age10c6 age10c7 #> 1 6 f 1 9062.1 13052.9 0.0 0.0 0.0 0.0 0.0 #> 2 6 f 2 0.0 0.0 21522.6 22953.6 0.0 0.0 0.0 #> 3 6 f 3 0.0 0.0 0.0 0.0 34440.6 25269.6 0.0 #> 4 6 f 4 0.0 0.0 0.0 0.0 0.0 0.0 14643.1 #> 5 6 f 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 6 6 m 1 6509.9 12278.5 0.0 0.0 0.0 0.0 0.0 #> 7 6 m 2 0.0 0.0 22109.4 26802.3 0.0 0.0 0.0 #> 8 6 m 3 0.0 0.0 0.0 0.0 30024.7 29153.2 0.0 #> 9 6 m 4 0.0 0.0 0.0 0.0 0.0 0.0 10770.9 #> 10 6 m 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 11 7 f 1 10649.0 21122.0 0.0 0.0 0.0 0.0 0.0 #> 12 7 f 2 0.0 0.0 27168.6 39377.6 0.0 0.0 0.0 #> 13 7 f 3 0.0 0.0 0.0 0.0 39409.9 35244.9 0.0 #> 14 7 f 4 0.0 0.0 0.0 0.0 0.0 0.0 26843.4 #> 15 7 f 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 16 7 m 1 9703.0 14359.8 0.0 0.0 0.0 0.0 0.0 #> 17 7 m 2 0.0 0.0 28829.8 36305.4 0.0 0.0 0.0 #> 18 7 m 3 0.0 0.0 0.0 0.0 40776.6 32918.7 0.0 #> 19 7 m 4 0.0 0.0 0.0 0.0 0.0 0.0 23581.9 #> 20 7 m 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 21 10 f 1 5187.2 11320.8 0.0 0.0 0.0 0.0 0.0 #> 22 10 f 2 0.0 0.0 18387.0 17595.0 0.0 0.0 0.0 #> 23 10 f 3 0.0 0.0 0.0 0.0 22660.5 20754.6 0.0 #> 24 10 f 4 0.0 0.0 0.0 0.0 0.0 0.0 9935.0 #> 25 10 f 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 26 10 m 1 4935.1 10748.1 0.0 0.0 0.0 0.0 0.0 #> 27 10 m 2 0.0 0.0 14986.3 18537.5 0.0 0.0 0.0 #> 28 10 m 3 0.0 0.0 0.0 0.0 26708.4 19102.2 0.0 #> 29 10 m 4 0.0 0.0 0.0 0.0 0.0 0.0 9070.0 #> 30 10 m 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> age10c8 age10c9 age10c10 procod9 procod10 procod11 procod30 procod31 #> 1 0.0 0.0 0.0 0.0 0.0 0.0 10122.8 2233.5 #> 2 0.0 0.0 0.0 0.0 0.0 0.0 17357.4 6018.2 #> 3 0.0 0.0 0.0 0.0 0.0 0.0 28542.2 5509.6 #> 4 4821.9 0.0 0.0 0.0 0.0 0.0 6722.2 2062.2 #> 5 0.0 1530.2 190.7 0.0 0.0 0.0 771.8 0.0 #> 6 0.0 0.0 0.0 0.0 0.0 0.0 9202.6 2589.7 #> 7 0.0 0.0 0.0 0.0 0.0 0.0 24684.7 5057.7 #> 8 0.0 0.0 0.0 0.0 0.0 0.0 22682.8 7310.3 #> 9 4728.5 0.0 0.0 0.0 0.0 0.0 5368.0 1838.3 #> 10 0.0 1897.1 1696.5 0.0 0.0 0.0 1174.4 285.4 #> 11 0.0 0.0 0.0 6958.8 15774.9 4654.3 0.0 0.0 #> 12 0.0 0.0 0.0 9655.0 37991.5 9589.7 0.0 0.0 #> 13 0.0 0.0 0.0 13794.9 42630.2 8800.1 0.0 0.0 #> 14 6463.5 0.0 0.0 5937.2 18940.5 4171.8 0.0 0.0 #> 15 0.0 5234.0 1024.4 1857.2 1606.5 2031.1 0.0 0.0 #> 16 0.0 0.0 0.0 4221.1 13607.4 2831.4 0.0 0.0 #> 17 0.0 0.0 0.0 10555.2 36314.1 13091.3 0.0 0.0 #> 18 0.0 0.0 0.0 14894.6 39557.1 13077.2 0.0 0.0 #> 19 6413.7 0.0 0.0 5096.1 17315.4 4499.4 0.0 0.0 #> 20 0.0 3586.0 1659.7 393.6 3532.3 452.2 0.0 0.0 #> 21 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 23 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 24 3075.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 25 0.0 1120.2 0.0 0.0 0.0 0.0 0.0 0.0 #> 26 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 27 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 28 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 29 2549.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 30 0.0 2869.7 427.8 0.0 0.0 0.0 0.0 0.0 #> procod32 procod54 procod55 procod93 #> 1 4175.6 0.0 0.0 5583.1 #> 2 8274.8 0.0 0.0 12825.8 #> 3 11793.7 0.0 0.0 13864.7 #> 4 5703.8 0.0 0.0 4976.8 #> 5 371.2 0.0 0.0 577.9 #> 6 2947.4 0.0 0.0 4048.7 #> 7 9759.6 0.0 0.0 9409.7 #> 8 12705.5 0.0 0.0 16479.3 #> 9 3938.1 0.0 0.0 4355.0 #> 10 968.5 0.0 0.0 1165.3 #> 11 0.0 0.0 0.0 0.0 #> 12 0.0 0.0 0.0 0.0 #> 13 0.0 0.0 0.0 0.0 #> 14 0.0 0.0 0.0 0.0 #> 15 0.0 0.0 0.0 0.0 #> 16 0.0 0.0 0.0 0.0 #> 17 0.0 0.0 0.0 0.0 #> 18 0.0 0.0 0.0 0.0 #> 19 0.0 0.0 0.0 0.0 #> 20 0.0 0.0 0.0 0.0 #> 21 0.0 10835.8 5672.2 0.0 #> 22 0.0 27406.7 8575.3 0.0 #> 23 0.0 30649.4 12765.7 0.0 #> 24 0.0 9839.9 3171.0 0.0 #> 25 0.0 444.7 675.5 0.0 #> 26 0.0 11708.5 3974.7 0.0 #> 27 0.0 25527.7 7996.1 0.0 #> 28 0.0 33164.3 12646.3 0.0 #> 29 0.0 7097.7 4522.1 0.0 #> 30 0.0 1818.3 1479.2 0.0#> [1] 422# Switch to sbs data sbspop<-pop.template(sbsdes, calmodel=~(emp.num + ent):(nace.macro + emp.cl) - 1, partition=~region) # Can fill the template using the sampling frame... sbspop<-fill.template(universe=sbs.frame,template=sbspop)#> #> # Coherence check between 'universe' and 'template': OK #>sbspop#> region emp.num:nace.macroAgriculture emp.num:nace.macroIndustry #> 1 North 13022 364710 #> 2 Center 1831 146333 #> 3 South 2315 68537 #> emp.num:nace.macroCommerce emp.num:nace.macroServices emp.num:emp.cl(9,19] #> 1 51591 257482 36549 #> 2 8285 29556 12146 #> 3 9649 31083 13984 #> emp.num:emp.cl(19,49] emp.num:emp.cl(49,99] emp.num:emp.cl(99,Inf] #> 1 66346 69029 492546 #> 2 22804 30069 113872 #> 3 20031 16279 52697 #> ent:nace.macroAgriculture ent:nace.macroIndustry ent:nace.macroCommerce #> 1 283 5080 1902 #> 2 61 2290 554 #> 3 114 1925 604 #> ent:nace.macroServices ent:emp.cl(9,19] ent:emp.cl(19,49] ent:emp.cl(49,99] #> 1 3109 2774 2201 999 #> 2 636 934 746 435 #> 3 760 1070 670 235 #> ent:emp.cl(99,Inf] #> 1 1287 #> 2 429 #> 3 228# ...and invoke function pop.desc on the filled known totals data frame: pop.desc(sbspop)#> # Data frame of known totals for a *partitioned* calibration task #> - Number of rows: 3 #> - Number of columns: 17 #> - Number of known totals: 48 #> #> # The data frame structure is as follows #> ## Column 1 identifies 3 *calibration domains* (one for each row) #> #> region #> 1 North #> 2 Center #> 3 South #> #> ## Columns 2-17 identify known totals organized into *4 BLOCKS* #> #> - BLOCK 1 ---------------------------------------------------------------- #> - Benchmark: Totals of emp.num by nace.macro #> - Known totals: 12 #> - Auxiliary variables: 4 #> - Columns: 2-5 #> #> 2 3 4 #> aux "emp.num:Agriculture" "emp.num:Industry" "emp.num:Commerce" #> 5 #> aux "emp.num:Services" #> #> #> - BLOCK 2 ---------------------------------------------------------------- #> - Benchmark: Totals of emp.num by emp.cl #> - Known totals: 12 #> - Auxiliary variables: 4 #> - Columns: 6-9 #> #> 6 7 8 9 #> aux "emp.num:(9,19]" "emp.num:(19,49]" "emp.num:(49,99]" "emp.num:(99,Inf]" #> #> #> - BLOCK 3 ---------------------------------------------------------------- #> - Benchmark: Totals of ent by nace.macro #> - Known totals: 12 #> - Auxiliary variables: 4 #> - Columns: 10-13 #> #> 10 11 12 13 #> aux "ent:Agriculture" "ent:Industry" "ent:Commerce" "ent:Services" #> #> #> - BLOCK 4 ---------------------------------------------------------------- #> - Benchmark: Totals of ent by emp.cl #> - Known totals: 12 #> - Auxiliary variables: 4 #> - Columns: 14-17 #> #> 14 15 16 17 #> aux "ent:(9,19]" "ent:(19,49]" "ent:(49,99]" "ent:(99,Inf]" #> #>sbspop <- pop.template(data=sbsdes, calmodel=~((emp.num + ent):(nace2 + emp.cl:nace.macro))-1, partition=~region:public) sbspop#> region public emp.num:nace21 emp.num:nace22 emp.num:nace25 emp.num:nace211 #> 1 North 0 NA NA NA NA #> 2 North 1 NA NA NA NA #> 3 Center 0 NA NA NA NA #> 4 Center 1 NA NA NA NA #> 5 South 0 NA NA NA NA #> 6 South 1 NA NA NA NA #> emp.num:nace213 emp.num:nace214 emp.num:nace215 emp.num:nace216 #> 1 NA NA NA NA #> 2 NA NA NA NA #> 3 NA NA NA NA #> 4 NA NA NA NA #> 5 NA NA NA NA #> 6 NA NA NA NA #> emp.num:nace217 emp.num:nace218 emp.num:nace219 emp.num:nace220 #> 1 NA NA NA NA #> 2 NA NA NA NA #> 3 NA NA NA NA #> 4 NA NA NA NA #> 5 NA NA NA NA #> 6 NA NA NA NA #> emp.num:nace221 emp.num:nace222 emp.num:nace223 emp.num:nace224 #> 1 NA NA NA NA #> 2 NA NA NA NA #> 3 NA NA NA NA #> 4 NA NA NA NA #> 5 NA NA NA NA #> 6 NA NA NA NA #> emp.num:nace225 emp.num:nace226 emp.num:nace227 emp.num:nace228 #> 1 NA NA NA NA #> 2 NA NA NA NA #> 3 NA NA NA NA #> 4 NA NA NA NA #> 5 NA NA NA NA #> 6 NA NA NA NA #> emp.num:nace229 emp.num:nace230 emp.num:nace231 emp.num:nace232 #> 1 NA NA NA NA #> 2 NA NA NA NA #> 3 NA NA NA NA #> 4 NA NA NA NA #> 5 NA NA NA NA #> 6 NA NA NA NA #> emp.num:nace233 emp.num:nace234 emp.num:nace235 emp.num:nace236 #> 1 NA NA NA NA #> 2 NA NA NA NA #> 3 NA NA NA NA #> 4 NA NA NA NA #> 5 NA NA NA NA #> 6 NA NA NA NA #> emp.num:nace237 emp.num:nace240 emp.num:nace241 emp.num:nace245 #> 1 NA NA NA NA #> 2 NA NA NA NA #> 3 NA NA NA NA #> 4 NA NA NA NA #> 5 NA NA NA NA #> 6 NA NA NA NA #> emp.num:nace250 emp.num:nace251 emp.num:nace252 emp.num:nace255 #> 1 NA NA NA NA #> 2 NA NA NA NA #> 3 NA NA NA NA #> 4 NA NA NA NA #> 5 NA NA NA NA #> 6 NA NA NA NA #> emp.num:nace260 emp.num:nace261 emp.num:nace262 emp.num:nace263 #> 1 NA NA NA NA #> 2 NA NA NA NA #> 3 NA NA NA NA #> 4 NA NA NA NA #> 5 NA NA NA NA #> 6 NA NA NA NA #> emp.num:nace264 emp.num:nace265 emp.num:nace266 emp.num:nace267 #> 1 NA NA NA NA #> 2 NA NA NA NA #> 3 NA NA NA NA #> 4 NA NA NA NA #> 5 NA NA NA NA #> 6 NA NA NA NA #> emp.num:nace270 emp.num:nace271 emp.num:nace272 emp.num:nace273 #> 1 NA NA NA NA #> 2 NA NA NA NA #> 3 NA NA NA NA #> 4 NA NA NA NA #> 5 NA NA NA NA #> 6 NA NA NA NA #> emp.num:nace274 emp.num:nace275 emp.num:nace280 emp.num:nace285 #> 1 NA NA NA NA #> 2 NA NA NA NA #> 3 NA NA NA NA #> 4 NA NA NA NA #> 5 NA NA NA NA #> 6 NA NA NA NA #> emp.num:nace290 emp.num:nace291 emp.num:nace292 emp.num:nace293 #> 1 NA NA NA NA #> 2 NA NA NA NA #> 3 NA NA NA NA #> 4 NA NA NA NA #> 5 NA NA NA NA #> 6 NA NA NA NA #> emp.num:nace299 ent:nace21 ent:nace22 ent:nace25 ent:nace211 ent:nace213 #> 1 NA NA NA NA NA NA #> 2 NA NA NA NA NA NA #> 3 NA NA NA NA NA NA #> 4 NA NA NA NA NA NA #> 5 NA NA NA NA NA NA #> 6 NA NA NA NA NA NA #> ent:nace214 ent:nace215 ent:nace216 ent:nace217 ent:nace218 ent:nace219 #> 1 NA NA NA NA NA NA #> 2 NA NA NA NA NA NA #> 3 NA NA NA NA NA NA #> 4 NA NA NA NA NA NA #> 5 NA NA NA NA NA NA #> 6 NA NA NA NA NA NA #> ent:nace220 ent:nace221 ent:nace222 ent:nace223 ent:nace224 ent:nace225 #> 1 NA NA NA NA NA NA #> 2 NA NA NA NA NA NA #> 3 NA NA NA NA NA NA #> 4 NA NA NA NA NA NA #> 5 NA NA NA NA NA NA #> 6 NA NA NA NA NA NA #> ent:nace226 ent:nace227 ent:nace228 ent:nace229 ent:nace230 ent:nace231 #> 1 NA NA NA NA NA NA #> 2 NA NA NA NA NA NA #> 3 NA NA NA NA NA NA #> 4 NA NA NA NA NA NA #> 5 NA NA NA NA NA NA #> 6 NA NA NA NA NA NA #> ent:nace232 ent:nace233 ent:nace234 ent:nace235 ent:nace236 ent:nace237 #> 1 NA NA NA NA NA NA #> 2 NA NA NA NA NA NA #> 3 NA NA NA NA NA NA #> 4 NA NA NA NA NA NA #> 5 NA NA NA NA NA NA #> 6 NA NA NA NA NA NA #> ent:nace240 ent:nace241 ent:nace245 ent:nace250 ent:nace251 ent:nace252 #> 1 NA NA NA NA NA NA #> 2 NA NA NA NA NA NA #> 3 NA NA NA NA NA NA #> 4 NA NA NA NA NA NA #> 5 NA NA NA NA NA NA #> 6 NA NA NA NA NA NA #> ent:nace255 ent:nace260 ent:nace261 ent:nace262 ent:nace263 ent:nace264 #> 1 NA NA NA NA NA NA #> 2 NA NA NA NA NA NA #> 3 NA NA NA NA NA NA #> 4 NA NA NA NA NA NA #> 5 NA NA NA NA NA NA #> 6 NA NA NA NA NA NA #> ent:nace265 ent:nace266 ent:nace267 ent:nace270 ent:nace271 ent:nace272 #> 1 NA NA NA NA NA NA #> 2 NA NA NA NA NA NA #> 3 NA NA NA NA NA NA #> 4 NA NA NA NA NA NA #> 5 NA NA NA NA NA NA #> 6 NA NA NA NA NA NA #> ent:nace273 ent:nace274 ent:nace275 ent:nace280 ent:nace285 ent:nace290 #> 1 NA NA NA NA NA NA #> 2 NA NA NA NA NA NA #> 3 NA NA NA NA NA NA #> 4 NA NA NA NA NA NA #> 5 NA NA NA NA NA NA #> 6 NA NA NA NA NA NA #> ent:nace291 ent:nace292 ent:nace293 ent:nace299 #> 1 NA NA NA NA #> 2 NA NA NA NA #> 3 NA NA NA NA #> 4 NA NA NA NA #> 5 NA NA NA NA #> 6 NA NA NA NA #> emp.num:emp.cl[6,9]:nace.macroAgriculture #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> emp.num:emp.cl(9,19]:nace.macroAgriculture #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> emp.num:emp.cl(19,49]:nace.macroAgriculture #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> emp.num:emp.cl(49,99]:nace.macroAgriculture #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> emp.num:emp.cl(99,Inf]:nace.macroAgriculture #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> emp.num:emp.cl[6,9]:nace.macroIndustry #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> emp.num:emp.cl(9,19]:nace.macroIndustry #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> emp.num:emp.cl(19,49]:nace.macroIndustry #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> emp.num:emp.cl(49,99]:nace.macroIndustry #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> emp.num:emp.cl(99,Inf]:nace.macroIndustry #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> emp.num:emp.cl[6,9]:nace.macroCommerce #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> emp.num:emp.cl(9,19]:nace.macroCommerce #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> emp.num:emp.cl(19,49]:nace.macroCommerce #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> emp.num:emp.cl(49,99]:nace.macroCommerce #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> emp.num:emp.cl(99,Inf]:nace.macroCommerce #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> emp.num:emp.cl[6,9]:nace.macroServices #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> emp.num:emp.cl(9,19]:nace.macroServices #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> emp.num:emp.cl(19,49]:nace.macroServices #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> emp.num:emp.cl(49,99]:nace.macroServices #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> emp.num:emp.cl(99,Inf]:nace.macroServices #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> ent:emp.cl[6,9]:nace.macroAgriculture ent:emp.cl(9,19]:nace.macroAgriculture #> 1 NA NA #> 2 NA NA #> 3 NA NA #> 4 NA NA #> 5 NA NA #> 6 NA NA #> ent:emp.cl(19,49]:nace.macroAgriculture #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> ent:emp.cl(49,99]:nace.macroAgriculture #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA #> ent:emp.cl(99,Inf]:nace.macroAgriculture ent:emp.cl[6,9]:nace.macroIndustry #> 1 NA NA #> 2 NA NA #> 3 NA NA #> 4 NA NA #> 5 NA NA #> 6 NA NA #> ent:emp.cl(9,19]:nace.macroIndustry ent:emp.cl(19,49]:nace.macroIndustry #> 1 NA NA #> 2 NA NA #> 3 NA NA #> 4 NA NA #> 5 NA NA #> 6 NA NA #> ent:emp.cl(49,99]:nace.macroIndustry ent:emp.cl(99,Inf]:nace.macroIndustry #> 1 NA NA #> 2 NA NA #> 3 NA NA #> 4 NA NA #> 5 NA NA #> 6 NA NA #> ent:emp.cl[6,9]:nace.macroCommerce ent:emp.cl(9,19]:nace.macroCommerce #> 1 NA NA #> 2 NA NA #> 3 NA NA #> 4 NA NA #> 5 NA NA #> 6 NA NA #> ent:emp.cl(19,49]:nace.macroCommerce ent:emp.cl(49,99]:nace.macroCommerce #> 1 NA NA #> 2 NA NA #> 3 NA NA #> 4 NA NA #> 5 NA NA #> 6 NA NA #> ent:emp.cl(99,Inf]:nace.macroCommerce ent:emp.cl[6,9]:nace.macroServices #> 1 NA NA #> 2 NA NA #> 3 NA NA #> 4 NA NA #> 5 NA NA #> 6 NA NA #> ent:emp.cl(9,19]:nace.macroServices ent:emp.cl(19,49]:nace.macroServices #> 1 NA NA #> 2 NA NA #> 3 NA NA #> 4 NA NA #> 5 NA NA #> 6 NA NA #> ent:emp.cl(49,99]:nace.macroServices ent:emp.cl(99,Inf]:nace.macroServices #> 1 NA NA #> 2 NA NA #> 3 NA NA #> 4 NA NA #> 5 NA NA #> 6 NA NApop.desc(sbspop)#> # Data frame of known totals for a *partitioned* calibration task #> - Number of rows: 6 #> - Number of columns: 156 #> - Number of known totals: 924 #> #> # The data frame structure is as follows #> ## Columns 1-2 identify 6 *calibration domains* (one for each row) #> #> region public #> 1 North 0 #> 2 North 1 #> 3 Center 0 #> 4 Center 1 #> 5 South 0 #> 6 South 1 #> #> ## Columns 3-156 identify known totals organized into *4 BLOCKS* #> #> - BLOCK 1 ---------------------------------------------------------------- #> - Benchmark: Totals of emp.num by nace2 #> - Known totals: 342 #> - Auxiliary variables: 57 #> - Columns: 3-59 #> #> 3 4 5 6 7 8 #> aux "emp.num:1" "emp.num:2" "emp.num:5" "emp.num:11" "emp.num:13" "emp.num:14" #> 9 10 11 12 13 #> aux "emp.num:15" "emp.num:16" "emp.num:17" "emp.num:18" "emp.num:19" #> 14 15 16 17 18 #> aux "emp.num:20" "emp.num:21" "emp.num:22" "emp.num:23" "emp.num:24" #> 19 20 21 22 23 #> aux "emp.num:25" "emp.num:26" "emp.num:27" "emp.num:28" "emp.num:29" #> 24 25 26 27 28 #> aux "emp.num:30" "emp.num:31" "emp.num:32" "emp.num:33" "emp.num:34" #> 29 30 31 32 33 #> aux "emp.num:35" "emp.num:36" "emp.num:37" "emp.num:40" "emp.num:41" #> 34 35 36 37 38 #> aux "emp.num:45" "emp.num:50" "emp.num:51" "emp.num:52" "emp.num:55" #> 39 40 41 42 43 #> aux "emp.num:60" "emp.num:61" "emp.num:62" "emp.num:63" "emp.num:64" #> 44 45 46 47 48 #> aux "emp.num:65" "emp.num:66" "emp.num:67" "emp.num:70" "emp.num:71" #> 49 50 51 52 53 #> aux "emp.num:72" "emp.num:73" "emp.num:74" "emp.num:75" "emp.num:80" #> 54 55 56 57 58 #> aux "emp.num:85" "emp.num:90" "emp.num:91" "emp.num:92" "emp.num:93" #> 59 #> aux "emp.num:99" #> #> #> - BLOCK 2 ---------------------------------------------------------------- #> - Benchmark: Totals of ent by nace2 #> - Known totals: 342 #> - Auxiliary variables: 57 #> - Columns: 60-116 #> #> 60 61 62 63 64 65 66 67 #> aux "ent:1" "ent:2" "ent:5" "ent:11" "ent:13" "ent:14" "ent:15" "ent:16" #> 68 69 70 71 72 73 74 75 #> aux "ent:17" "ent:18" "ent:19" "ent:20" "ent:21" "ent:22" "ent:23" "ent:24" #> 76 77 78 79 80 81 82 83 #> aux "ent:25" "ent:26" "ent:27" "ent:28" "ent:29" "ent:30" "ent:31" "ent:32" #> 84 85 86 87 88 89 90 91 #> aux "ent:33" "ent:34" "ent:35" "ent:36" "ent:37" "ent:40" "ent:41" "ent:45" #> 92 93 94 95 96 97 98 99 #> aux "ent:50" "ent:51" "ent:52" "ent:55" "ent:60" "ent:61" "ent:62" "ent:63" #> 100 101 102 103 104 105 106 107 #> aux "ent:64" "ent:65" "ent:66" "ent:67" "ent:70" "ent:71" "ent:72" "ent:73" #> 108 109 110 111 112 113 114 115 #> aux "ent:74" "ent:75" "ent:80" "ent:85" "ent:90" "ent:91" "ent:92" "ent:93" #> 116 #> aux "ent:99" #> #> #> - BLOCK 3 ---------------------------------------------------------------- #> - Benchmark: Totals of emp.num by emp.cl, nace.macro #> - Known totals: 120 #> - Auxiliary variables: 20 #> - Columns: 117-136 #> #> 117 118 #> aux "emp.num:[6,9]:Agriculture" "emp.num:(9,19]:Agriculture" #> 119 120 #> aux "emp.num:(19,49]:Agriculture" "emp.num:(49,99]:Agriculture" #> 121 122 #> aux "emp.num:(99,Inf]:Agriculture" "emp.num:[6,9]:Industry" #> 123 124 #> aux "emp.num:(9,19]:Industry" "emp.num:(19,49]:Industry" #> 125 126 #> aux "emp.num:(49,99]:Industry" "emp.num:(99,Inf]:Industry" #> 127 128 #> aux "emp.num:[6,9]:Commerce" "emp.num:(9,19]:Commerce" #> 129 130 #> aux "emp.num:(19,49]:Commerce" "emp.num:(49,99]:Commerce" #> 131 132 #> aux "emp.num:(99,Inf]:Commerce" "emp.num:[6,9]:Services" #> 133 134 #> aux "emp.num:(9,19]:Services" "emp.num:(19,49]:Services" #> 135 136 #> aux "emp.num:(49,99]:Services" "emp.num:(99,Inf]:Services" #> #> #> - BLOCK 4 ---------------------------------------------------------------- #> - Benchmark: Totals of ent by emp.cl, nace.macro #> - Known totals: 120 #> - Auxiliary variables: 20 #> - Columns: 137-156 #> #> 137 138 139 #> aux "ent:[6,9]:Agriculture" "ent:(9,19]:Agriculture" "ent:(19,49]:Agriculture" #> 140 141 142 #> aux "ent:(49,99]:Agriculture" "ent:(99,Inf]:Agriculture" "ent:[6,9]:Industry" #> 143 144 145 #> aux "ent:(9,19]:Industry" "ent:(19,49]:Industry" "ent:(49,99]:Industry" #> 146 147 148 #> aux "ent:(99,Inf]:Industry" "ent:[6,9]:Commerce" "ent:(9,19]:Commerce" #> 149 150 151 #> aux "ent:(19,49]:Commerce" "ent:(49,99]:Commerce" "ent:(99,Inf]:Commerce" #> 152 153 154 #> aux "ent:[6,9]:Services" "ent:(9,19]:Services" "ent:(19,49]:Services" #> 155 156 #> aux "ent:(49,99]:Services" "ent:(99,Inf]:Services" #> #>