Método para desagregar población agrupada en edades quinquenales
Aplicaciones del modelo SPRAGUE con R-project
Diego Meza
9 de julio de 2020
1 Librería simPop
#install.packages("simPop")
library(simPop)## Loading required package: lattice## Loading required package: vcd## Loading required package: grid## Loading required package: data.table## Package simPop 1.2.0 has been loaded!## Since simPop does explicit parallelization,
## the number of data.table threads is set to 1.2 Datos de la EPH
Cantidad de trabajadores por cuenta propia por tramos de edades quinquenales
2.1 Tramos de edades
age=as.factor(c("0-4","5-9","10-14","15-19","20-24","25-29","30-34","35-39","40-44","45-49","50-54","55-59","60-64","65-69","77-74","75-79","80+"))2.2 Población en cada tramo
popH=c(0,0,4180,38254,31738,44179,56517,56386,69933,60672,57664, 61748,46811,39420,23147,12631,5121)popM=c(0,0,1939,8776,24975,50271,48176,62959,48837,42720,42109,41444,31441,24151,9885,4692,2721)
x <-data.frame(age,popH,popM)
x## age popH popM
## 1 0-4 0 0
## 2 5-9 0 0
## 3 10-14 4180 1939
## 4 15-19 38254 8776
## 5 20-24 31738 24975
## 6 25-29 44179 50271
## 7 30-34 56517 48176
## 8 35-39 56386 62959
## 9 40-44 69933 48837
## 10 45-49 60672 42720
## 11 50-54 57664 42109
## 12 55-59 61748 41444
## 13 60-64 46811 31441
## 14 65-69 39420 24151
## 15 77-74 23147 9885
## 16 75-79 12631 4692
## 17 80+ 5121 27213 Resultados
sH<-sprague(x[,2])sM<-sprague(x[,3])result<-data.frame(sH,sM)
result## sH sM
## 0 -663.3504 -6.3504
## 1 -138.8320 7.3520
## 2 172.2720 8.1600
## 3 311.1040 0.8080
## 4 318.8064 -9.9696
## 5 236.5216 -19.4384
## 6 105.3920 -22.8640
## 7 -33.4400 -15.5120
## 8 -138.8320 7.3520
## 9 -169.6416 50.4624
## 10 -238.6432 120.9616
## 11 -458.6112 225.9920
## 12 -18.8192 358.2480
## 13 1429.7088 517.6480
## 14 3466.3648 716.1504
## 15 5416.8400 965.3712
## 16 7530.3264 1262.4784
## 17 8807.7184 1650.0384
## 18 8720.1424 2150.3264
## 19 7778.9728 2747.7856
## 20 6993.9616 3346.2032
## 21 6126.5632 3894.7648
## 22 5692.8592 4690.3968
## 23 6039.6672 5848.5888
## 24 6884.9488 7195.0464
## 25 7580.0624 8510.8160
## 26 8230.9936 9924.6848
## 27 8879.4656 10790.1728
## 28 9473.5616 10790.4768
## 29 10014.9168 10254.8496
## 30 10598.9184 9756.0944
## 31 11256.6912 9091.7472
## 32 11649.6352 8923.8432
## 33 11634.7232 9611.3792
## 34 11377.0320 10792.9360
## 35 11126.2880 11807.1536
## 36 10762.4928 12857.1712
## 37 10766.9248 13352.2752
## 38 11381.8448 12954.5952
## 39 12348.4496 11987.8048
## 40 13212.1728 11109.4496
## 41 14119.7248 10183.2224
## 42 14588.5648 9451.1424
## 43 14350.5648 9098.3744
## 44 13661.9728 8994.8112
## 45 13048.5072 8816.2848
## 46 12406.6352 8616.9536
## 47 11903.7552 8480.3616
## 48 11676.6752 8412.3056
## 49 11636.4272 8394.0944
## 50 11515.4544 8386.3856
## 51 11317.1312 8379.2224
## 52 11303.5712 8398.3984
## 53 11562.3632 8446.9344
## 54 11965.4800 8498.0592
## 55 12339.8128 8541.2624
## 56 12770.9920 8601.7840
## 57 12838.9200 8500.0480
## 58 12333.2400 8152.9280
## 59 11465.0352 7647.9776
## 60 10629.4608 7140.3744
## 61 9715.9440 6580.4800
## 62 9026.6640 6127.3600
## 63 8741.7120 5869.8640
## 64 8697.2192 5722.9216
## 65 8572.6480 5538.2912
## 66 8460.2128 5376.4352
## 67 8153.8848 5050.7312
## 68 7527.9648 4463.3152
## 69 6705.2896 3722.2272
## 70 5929.9936 3024.0096
## 71 5147.9680 2318.1200
## 72 4472.0480 1764.2560
## 73 3981.3040 1458.1760
## 74 3615.6864 1320.4384
## 75 3231.6736 1166.4816
## 76 2852.6880 1021.9840
## 77 2502.1520 912.6240
## 78 2175.6640 829.0400
## 79 1868.8224 761.8704
## 80+ 5121.0000 2721.0000