09/10/2020

Objectives

• Identify complex survey designs features

• Specify survey design

• Perform survey analysis: mean, cross-tabulations, and proportions

• Perform survey analysis: linear and logistic regressions

• Perform sub-population analysis (correctly)

• Recognise the importance of the design effect and estimate the design effect (DEFF) or design factor

Brief R intro

RStudio cloud

R y RStudio installed

RStudio layout

.R and .Rmd

  • Choose .R for a simpler code file

  • Choose .Rmd for a complete code file

.Rmd can be displayed as .doc, .pdf, .html

.Rmd structure

Paquetes/librerias R requeridos para el taller

  • library(rio) #To import and export data#

  • library(reshape2) #Modifiy data set shape design with grammar

  • library(dplyr) #dplyr: a grammar of data manipulation#

  • library(ggplot2) #plot your results

  • library(survey) #Analise using survey design

  • library(srvyr) #Analise using survey design with grammar

install.packages()

RStudio te ofrece installar los paquetes por ti. Guarda el archivo. No demora en aparecer un mensaje en amarillo ofreciendo instalar

install.packages()

# install.packages("survey")

  • Puedes activar el código borrando el # antes del texto

  • Puedes cambiar el “nombre del paquete” a instalar

  • Comillas en install.packages() son importantes

What is a complex survey design?

  • Basic statistical analyses assume that the data being analysed constitute a simple random sample (SRS) of the population. Remember: SRS = all members have an equal probability of selection.
  • In real-life, survey samples are often stratified by variables of interest and are frequently clustered
  • Stratification and clustering are two of the three features of a complex survey design

Dr Shaun Scholes, 2019

Typically in sampling we:

  • Stratify (partition the population into groups called strata), sampling independently from each and every stratum. Examples? Region; Faculty.

  • Sample from the set of clusters in each stratum (these are known as the Primary Sampling Units: PSUs)

  • Why? For reasons of cost and convenience!

  • But….there is a statistical price to pay. In this session, we treat the clustering of data as a nuisance (not as something of primary interest)

Chile is a complex country

Population strata and clustering

Common surveys

Precision of an estimate (assuming SRS)

Example: Precision for a proportion

\[SE=1.96~x~\sqrt\frac{P*(1-P)}{n}\]

What are the consequences of complex survey design?

  • Both stratification and clustering can influence the magnitude of the standard errors (SEs): and hence the value of test statistics and width of confidence intervals (CIs).

  • Because SEs affect significance levels (P-values), the conclusions drawn from an analysis that does not take these complex survey design features into account may be misleading.

  • Usually misleading in one direction: under-estimation of amount of uncertainty in the survey data (p-values too small; CIs too narrow)

  • Correct inference requires taking the features of the complex survey design into account.

To summarise

When using complex survey designs, the study might have any or all of the three following features:

• Stratification

• Clustering (selection of PSUs within strata)

• Weighting

In practice, these are variables in a complex survey dataset that can be used to enable you to use packages such as Stata and R to perform appropriate statistical inference (generalise from your sample to the underlying population)

Resources and requirements for this workshop

Databases

Databases

– ENS 2003 (SPSS)

– Base de datos ENS 2003 – Región y comuna (SPSS)

– ENS 2009-2010

  1. SPSS, STATA

(a.1) ENS 2009-2010 Comuna (SPSS)

  1. Base de medicamentos

– ENS 2016-17

  1. Base Formulario 1-Formulario 2 y exámenes

(a.1) Base Formulario 1-Formulario 2 y exámenes – comuna y variables complejas (SPSS)

(a.2) Base Formulario1-Formulario 2 y exámenes – Metales Pesados (SPSS)

  1. Base Formulario 4

  2. Manual de uso (Actualización 14/02/2018)

  3. Libro de códigos F1, F2 , F3 y F4

  4. Base de Medicamentos

  5. Base formuarios y libro de codigos F3

Databases

Questionnaires and technical reports

Open packages: library()

library(rio) #To import and export data#
library(reshape2) #Modifiy data set shape design with grammar
library(dplyr) #dplyr: a grammar of data manipulation#
library(ggplot2) #plot your results
library(survey) #Analise using survey design
library(srvyr) #Analise using survey design with grammar

Knitr (ctrl+shift+K)

Knit: Do you see that blue button with knitting sticks? Press it

Import and explore database

Import and explore database

  • Leave all the files you need in the same working directory
# Current wd
getwd()
## [1] "/cloud/project"
# Setting wd
# setwd('~/Dropbox/PhD_web/ENS/static/Referencias ENS')
# df0 <- import('F1_F2_EX_V9_20AUG18AP5.dta')
df0 <- rio::import('F1_F2_EX_V9_20AUG18AP5.dta')

rio:: lo utilizas cuando no has cargado la librería “rio” o para asegurar que usas la función “import” del paquete “rio” y no de “otro paquete”.

Explore database

# # View(df0)
# WARNING # View() de bases gigante 
# ENS no es gigante

# nombres de variables / columnas
# names(df0)
# total columnas
# length(names(df0)) 
# total filas
# length(df0$IdEncuesta)
# options:
# nrow(df0)
# ncol(df0)
dim(df0)
## [1] 6233 1160

Explore database

# A veces es mejor generar un elemento ("namesdf0")
namesdf0 <- names(df0)
namesdf0
##    [1] "IdEncuesta"                       "FechaInicioF1"                   
##    [3] "Region"                           "Zona"                            
##    [5] "IdSegmento"                       "IdPersona_1"                     
##    [7] "Ident7"                           "Edad"                            
##    [9] "Edad_Codificada"                  "Sexo"                            
##   [11] "c1"                               "c1_esp"                          
##   [13] "c2"                               "c2a"                             
##   [15] "c2b"                              "c3"                              
##   [17] "c3a"                              "c3b"                             
##   [19] "c3c"                              "c5"                              
##   [21] "c5_otro"                          "c5b"                             
##   [23] "c6"                               "c7_0_nino"                       
##   [25] "c7_1_nino"                        "c7_1_cuidador"                   
##   [27] "c7_2_nino"                        "c7_2_cuidador"                   
##   [29] "c7_3_nino"                        "c7_3_cuidador"                   
##   [31] "c7_4_nino"                        "c7_4_cuidador"                   
##   [33] "c7_5_nino"                        "c7_5_cuidador"                   
##   [35] "e1"                               "e2a"                             
##   [37] "e2b"                              "e2c"                             
##   [39] "e2d"                              "e3_1"                            
##   [41] "e3_2"                             "e3_3"                            
##   [43] "e4_1"                             "e4_2"                            
##   [45] "e4_3"                             "e4_4"                            
##   [47] "e4_5"                             "e5_1"                            
##   [49] "e5_2"                             "e5_3"                            
##   [51] "e6_1"                             "e6_2"                            
##   [53] "e6_3"                             "e7"                              
##   [55] "t9"                               "t9_2"                            
##   [57] "t10a"                             "t10b"                            
##   [59] "t10c"                             "t10d"                            
##   [61] "t10e"                             "t10f"                            
##   [63] "t10g"                             "t10h"                            
##   [65] "t10i"                             "t10j"                            
##   [67] "t10k"                             "cd1"                             
##   [69] "cd2"                              "cd3"                             
##   [71] "cd4"                              "cd5"                             
##   [73] "cd6"                              "cd7"                             
##   [75] "cd8"                              "cd8_corr"                        
##   [77] "cd9a_1"                           "cd9a_2"                          
##   [79] "cd9a_3"                           "cd9a_4"                          
##   [81] "cd9a_5"                           "cd9a_6"                          
##   [83] "cd9a_7"                           "cd9a_8"                          
##   [85] "cd9a_9"                           "cd9a_10"                         
##   [87] "cd9a_11"                          "cd9a_12"                         
##   [89] "cd9a_13"                          "cd9a_14"                         
##   [91] "cd9a_15"                          "cd9a_16"                         
##   [93] "cd9a_17"                          "cd9a_18"                         
##   [95] "cd9a_19"                          "cd9a_20"                         
##   [97] "cd9a_21"                          "cd9a_22"                         
##   [99] "cd9a_23"                          "cd9a_24"                         
##  [101] "a1"                               "a2"                              
##  [103] "a3a"                              "a3b"                             
##  [105] "a4"                               "a5"                              
##  [107] "a6a"                              "a6b"                             
##  [109] "a7"                               "a8"                              
##  [111] "a9a"                              "a9b"                             
##  [113] "a10"                              "a11"                             
##  [115] "a12a"                             "a12b"                            
##  [117] "a13"                              "a14"                             
##  [119] "a15a"                             "a15b"                            
##  [121] "a16a"                             "a16b"                            
##  [123] "a17"                              "a18"                             
##  [125] "a18_esp"                          "a18_esp_sinCodificar"            
##  [127] "a19"                              "d1_F1"                           
##  [129] "d2_F1"                            "d3_1"                            
##  [131] "d3_2"                             "d3_3"                            
##  [133] "d3_4"                             "d3_5"                            
##  [135] "d3_6"                             "d4"                              
##  [137] "d5"                               "d6"                              
##  [139] "d7"                               "v1"                              
##  [141] "v2"                               "v3"                              
##  [143] "v4"                               "v5"                              
##  [145] "v6"                               "v7"                              
##  [147] "v8_1"                             "v8_2"                            
##  [149] "v8_3"                             "v8_4"                            
##  [151] "v8_5"                             "v8_6"                            
##  [153] "v8_7"                             "au1_F1"                          
##  [155] "au2_F1"                           "au3_F1"                          
##  [157] "au4_F1"                           "au5_F1"                          
##  [159] "au6_F1"                           "au7_F1"                          
##  [161] "sm1"                              "sm2_1"                           
##  [163] "sm2_2"                            "sm2_3"                           
##  [165] "sm2_4"                            "sm2_5"                           
##  [167] "sm2_6"                            "sm2_7"                           
##  [169] "sm2_8"                            "sm2_9"                           
##  [171] "sm2_10"                           "sm2_11"                          
##  [173] "sm2_12"                           "sm2_13"                          
##  [175] "sm2_14"                           "sm2_15"                          
##  [177] "sm2_16"                           "sm3"                             
##  [179] "sm4"                              "sm5a"                            
##  [181] "sm5b"                             "sm5c"                            
##  [183] "sm6_1"                            "sm6_2"                           
##  [185] "sm6_3"                            "sm6_4"                           
##  [187] "sm6_5"                            "sm6_6"                           
##  [189] "sm6_7"                            "sm6_8"                           
##  [191] "sm6_9"                            "sm6_10"                          
##  [193] "sm6_11"                           "sm6_12"                          
##  [195] "sm7"                              "sm8"                             
##  [197] "sm9"                              "sd1_F1"                          
##  [199] "sd2_F1"                           "sd3_F1"                          
##  [201] "sd4_F1"                           "sd5_F1"                          
##  [203] "sd6_F1"                           "sd7_F1"                          
##  [205] "sd8_F1"                           "sd9_F1"                          
##  [207] "sd10_F1"                          "sd11_F1"                         
##  [209] "sd12_F1"                          "sd13_F1"                         
##  [211] "sd14_F1"                          "sd15_F1"                         
##  [213] "sd16_F1"                          "sd17_F1"                         
##  [215] "sd18_F1"                          "sd19_F1"                         
##  [217] "sd20_F1"                          "sd21_F1"                         
##  [219] "sd22_F1"                          "sd23_F1"                         
##  [221] "sd24_F1"                          "sd25_F1"                         
##  [223] "sd26a"                            "sd26b"                           
##  [225] "sd27_F1"                          "sd28_F1"                         
##  [227] "sd29_F1"                          "sd30"                            
##  [229] "sd31"                             "o1"                              
##  [231] "o2"                               "o2_esp"                          
##  [233] "o3a_1"                            "o3b_1"                           
##  [235] "o3b_1_esp"                        "o3b_1_esp_sinCodificar"          
##  [237] "o3c_1"                            "o3c_1_esp"                       
##  [239] "o3c_1_esp_sinCodificar"           "o3a_2"                           
##  [241] "o3b_2"                            "o3b_2_esp"                       
##  [243] "o3b_2_esp_sinCodificar"           "o3c_2"                           
##  [245] "o3c_2_esp"                        "o3c_2_esp_sinCodificar"          
##  [247] "o3a_3"                            "o3b_3"                           
##  [249] "o3b_3_esp"                        "o3b_3_esp_sinCodificar"          
##  [251] "o3c_3"                            "o3c_3_esp"                       
##  [253] "o3c_3_esp_sinCodificar"           "o3a_4"                           
##  [255] "o3b_4"                            "o3b_4_esp"                       
##  [257] "o3b_4_esp_sinCodificar"           "o3c_4"                           
##  [259] "o3c_4_esp"                        "o3c_4_esp_sinCodificar"          
##  [261] "o3a_5"                            "o3b_5"                           
##  [263] "o3b_5_esp"                        "o3c_5"                           
##  [265] "o3c_5_esp"                        "o3c_5_esp_sinCodificar"          
##  [267] "o3a_6"                            "o3b_6"                           
##  [269] "o3b_6_esp"                        "o3b_6_esp_sinCodificar"          
##  [271] "o3c_6"                            "o3c_6_esp"                       
##  [273] "o3c_6_esp_sinCodificar"           "o3a_7"                           
##  [275] "o3b_7"                            "o3b_7_esp"                       
##  [277] "o3b_7_esp_sinCodificar"           "o3c_7"                           
##  [279] "o3c_7_esp"                        "o3c_7_esp_sinCodificar"          
##  [281] "o3a_8"                            "o3b_8"                           
##  [283] "o3c_8"                            "o3c_8_esp"                       
##  [285] "o3c_8_esp_sinCodificar"           "o3a_9"                           
##  [287] "o3b_9"                            "o3c_9"                           
##  [289] "o3c_9_esp"                        "o3c_9_esp_sinCodificar"          
##  [291] "o3a_10"                           "o3b_10"                          
##  [293] "o3c_10"                           "o3c_10_esp"                      
##  [295] "o4"                               "o5"                              
##  [297] "o6"                               "o7"                              
##  [299] "o7_esp"                           "o7_esp_sinCodificar"             
##  [301] "n1"                               "n2"                              
##  [303] "n3"                               "n4_1"                            
##  [305] "n4_2"                             "n4_3"                            
##  [307] "n4_4"                             "n4_5"                            
##  [309] "n4_6"                             "n4_6_esp"                        
##  [311] "n4_6_esp_sinCodificar"            "n4_7"                            
##  [313] "die1a"                            "die1aesp"                        
##  [315] "die1b"                            "die2"                            
##  [317] "die3"                             "die4"                            
##  [319] "die5"                             "die6"                            
##  [321] "die7"                             "die8"                            
##  [323] "die9"                             "die10_a"                         
##  [325] "die10_b"                          "die10_c"                         
##  [327] "die10_d"                          "die10_e"                         
##  [329] "die10_f"                          "die11"                           
##  [331] "die12_cantidad"                   "die12_unidad"                    
##  [333] "die12e"                           "die13_cantidad"                  
##  [335] "die13_unidad"                     "die13e"                          
##  [337] "die14"                            "die14_esp"                       
##  [339] "die14_esp_sinCodificar"           "ts1"                             
##  [341] "ts2"                              "ts3"                             
##  [343] "ts4"                              "ts5"                             
##  [345] "ts5_aux"                          "ts6"                             
##  [347] "ts6_aux"                          "ts7"                             
##  [349] "ts8"                              "ts9_1"                           
##  [351] "ts9_2"                            "ts9_3"                           
##  [353] "ts9_4"                            "ts9_5"                           
##  [355] "ts9_6"                            "ts9_7"                           
##  [357] "ts9_8"                            "ts10"                            
##  [359] "ta1"                              "ta2"                             
##  [361] "ta3"                              "ta4"                             
##  [363] "ta5"                              "ta6"                             
##  [365] "ta7"                              "ta8"                             
##  [367] "ta9"                              "ta10_1"                          
##  [369] "ta10_1_esp"                       "ta10_1_esp_sinCodificar"         
##  [371] "ta10_2"                           "ta10_2_esp"                      
##  [373] "ta10_2_esp_sinCodificar"          "ta10_3"                          
##  [375] "ta10_3_esp"                       "ta10_3_esp_sinCodificar"         
##  [377] "ta11_1"                           "ta11_2"                          
##  [379] "ta12_1"                           "ta12_2"                          
##  [381] "ta13_1"                           "ta13_2"                          
##  [383] "ta14_1a"                          "ta14_1b"                         
##  [385] "ta14_2a"                          "ta14_2b"                         
##  [387] "ta14_3a"                          "ta14_3b"                         
##  [389] "ta14_4a"                          "ta14_4b"                         
##  [391] "ta14_5"                           "ta14_6a"                         
##  [393] "ta14_6b"                          "ta14_7a"                         
##  [395] "ta14_7b"                          "ta14_7c"                         
##  [397] "ta14_7c_sinCodificar"             "ta14a"                           
##  [399] "ta14b"                            "ta15"                            
##  [401] "ta16_1"                           "ta16_2"                          
##  [403] "ta16_3"                           "ta16_4"                          
##  [405] "ta16_5"                           "ta16_6"                          
##  [407] "ta16_7"                           "ta17"                            
##  [409] "ta18"                             "ta18_esp"                        
##  [411] "sb1"                              "sb2"                             
##  [413] "sb3"                              "sb4"                             
##  [415] "sb5_1"                            "sb5_2"                           
##  [417] "sb5_3"                            "sb5_4"                           
##  [419] "sb5_5"                            "h1a"                             
##  [421] "h1b"                              "h2"                              
##  [423] "h3"                               "h4"                              
##  [425] "h5"                               "h6"                              
##  [427] "h7"                               "h8"                              
##  [429] "h8_esp"                           "h8_esp_sinCodificar"             
##  [431] "h9_1"                             "h9_2"                            
##  [433] "h9_3"                             "h10_1"                           
##  [435] "h10_1_esp"                        "h10_1_esp_sinCodificar"          
##  [437] "h10a_1"                           "h10b_1"                          
##  [439] "h10b_1_esp"                       "h10b_1_esp_sinCodificar"         
##  [441] "h10_2"                            "h10_2_esp"                       
##  [443] "h10_2_esp_sinCodificar"           "h10a_2"                          
##  [445] "h10b_2"                           "h10b_2_esp"                      
##  [447] "h10b_2_esp_sinCodificar"          "h10_3"                           
##  [449] "h10_3_esp"                        "h10_3_esp_sinCodificar"          
##  [451] "h10a_3"                           "h10b_3"                          
##  [453] "h10b_3_esp"                       "h10b_3_esp_sinCodificar"         
##  [455] "h11"                              "di1"                             
##  [457] "di2a"                             "di2b"                            
##  [459] "di3"                              "di3a"                            
##  [461] "di4"                              "di5"                             
##  [463] "di6"                              "di7_1"                           
##  [465] "di7_2"                            "di7_3"                           
##  [467] "di8"                              "di9"                             
##  [469] "di10"                             "di11"                            
##  [471] "di12"                             "di13"                            
##  [473] "di14"                             "di14_esp"                        
##  [475] "di14_esp_sinCodificar"            "di15_1"                          
##  [477] "di15_2"                           "di15_3"                          
##  [479] "di16_1"                           "di16_1_esp"                      
##  [481] "di16_1_esp_sinCodificar"          "di16a_1"                         
##  [483] "di16b_1"                          "di16b_1_esp"                     
##  [485] "di16b_1_esp_sinCodificar"         "di16_2"                          
##  [487] "di16_2_esp"                       "di16_2_esp_sinCodificar"         
##  [489] "di16a_2"                          "di16b_2"                         
##  [491] "di16b_2_esp"                      "di16b_2_esp_sinCodificar"        
##  [493] "di16_3"                           "di16_3_esp"                      
##  [495] "di16_3_esp_sinCodificar"          "di16a_3"                         
##  [497] "di16b_3"                          "di16b_3_esp"                     
##  [499] "di16b_3_esp_sinCodificar"         "dis2"                            
##  [501] "dis3"                             "dis4"                            
##  [503] "dis5"                             "dis6"                            
##  [505] "af1a"                             "af1b"                            
##  [507] "af1c"                             "af1d"                            
##  [509] "af1e"                             "af1f"                            
##  [511] "af1g"                             "af1h"                            
##  [513] "af1i"                             "ps1"                             
##  [515] "ps2_F1"                           "ps3_F1"                          
##  [517] "ps4_F1"                           "ps5_F1"                          
##  [519] "ps6_F1"                           "ps6a"                            
##  [521] "ps7_F1"                           "ps8"                             
##  [523] "ps9_F1"                           "ps10_F1"                         
##  [525] "as3_1"                            "as4_1"                           
##  [527] "as5_1"                            "as5_esp_1"                       
##  [529] "as5_esp_sinCodificar_1"           "as6_1"                           
##  [531] "as7_1"                            "as7_corr_1"                      
##  [533] "as8_1"                            "as8_corr_1"                      
##  [535] "NEDU1"                            "as9_1"                           
##  [537] "as9_corr_1"                       "as10_1"                          
##  [539] "as10_esp_1"                       "as10_esp_sinCodificar_1"         
##  [541] "as11_1"                           "as13_1"                          
##  [543] "as14_1"                           "as15_1"                          
##  [545] "as16_1"                           "as17_1"                          
##  [547] "as17_sinCodificar_1"              "as18_1"                          
##  [549] "as18_sinCodificar_1"              "as19_1"                          
##  [551] "as19_esp_1"                       "as19_esp_sinCodificar_1"         
##  [553] "as20_1"                           "as21_1"                          
##  [555] "as22_1"                           "as22_esp_1"                      
##  [557] "as22_esp_sinCodificar_1"          "IdPersona_2"                     
##  [559] "IdSexo_2"                         "Edad_2"                          
##  [561] "as3_2"                            "as4_2"                           
##  [563] "as5_2"                            "as5_esp_2"                       
##  [565] "as5_esp_sinCodificar_2"           "as6_2"                           
##  [567] "as7_2"                            "as7_corr_2"                      
##  [569] "as8_2"                            "as8_corr_2"                      
##  [571] "as9_2"                            "as9_corr_2"                      
##  [573] "as10_2"                           "as10_esp_2"                      
##  [575] "as10_esp_sinCodificar_2"          "as11_2"                          
##  [577] "as13_2"                           "as14_2"                          
##  [579] "as15_2"                           "as16_2"                          
##  [581] "as17_2"                           "as17_sinCodificar_2"             
##  [583] "as18_2"                           "as18_sinCodificar_2"             
##  [585] "as19_2"                           "as19_esp_2"                      
##  [587] "as19_esp_sinCodificar_2"          "as20_2"                          
##  [589] "as21_2"                           "as22_2"                          
##  [591] "as22_esp_2"                       "as22_esp_sinCodificar_2"         
##  [593] "IdPersona_3"                      "IdSexo_3"                        
##  [595] "Edad_3"                           "as3_3"                           
##  [597] "as4_3"                            "as5_3"                           
##  [599] "as5_esp_3"                        "as5_esp_sinCodificar_3"          
##  [601] "as6_3"                            "as7_3"                           
##  [603] "as7_corr_3"                       "as8_3"                           
##  [605] "as8_corr_3"                       "as9_3"                           
##  [607] "as9_corr_3"                       "as10_3"                          
##  [609] "as10_esp_3"                       "as10_esp_sinCodificar_3"         
##  [611] "as11_3"                           "as13_3"                          
##  [613] "as14_3"                           "as15_3"                          
##  [615] "as16_3"                           "as17_3"                          
##  [617] "as17_sinCodificar_3"              "as18_3"                          
##  [619] "as18_sinCodificar_3"              "as19_3"                          
##  [621] "as19_esp_3"                       "as19_esp_sinCodificar_3"         
##  [623] "as20_3"                           "as21_3"                          
##  [625] "as22_3"                           "as27"                            
##  [627] "as28"                             "as29"                            
##  [629] "as29_esp"                         "as29_esp_sinCodificar"           
##  [631] "as30"                             "as31"                            
##  [633] "as32"                             "as32_esp"                        
##  [635] "as32_esp_sinCodificar"            "as33"                            
##  [637] "as34"                             "as35_1"                          
##  [639] "as35_2"                           "as36"                            
##  [641] "as37"                             "as38"                            
##  [643] "as39"                             "as22_esp_3"                      
##  [645] "as22_esp_sinCodificar_3"          "n_per"                           
##  [647] "h6_v7"                            "h7_v7"                           
##  [649] "h8_v7"                            "h8_esp_v7"                       
##  [651] "h8_esp_sinCodificar_v7"           "h9_1_v7"                         
##  [653] "h9_2_v7"                          "h9_3_v7"                         
##  [655] "h10_1_v7"                         "h10_1_esp_v7"                    
##  [657] "h10_1_esp_sinCodificar_v7"        "h10a_1_v7"                       
##  [659] "h10b_1_v7"                        "h10b_1_esp_v7"                   
##  [661] "h10b_1_esp_sinCodificar_v7"       "h10_2_v7"                        
##  [663] "h10_2_esp_v7"                     "h10_2_esp_sinCodificar_v7"       
##  [665] "h10a_2_v7"                        "h10b_2_v7"                       
##  [667] "h10b_2_esp_v7"                    "h10b_2_esp_sinCodificar_v7"      
##  [669] "h10_3_v7"                         "h10_3_esp_v7"                    
##  [671] "h10_3_esp_sinCodificar_v7"        "h10a_3_v7"                       
##  [673] "h10b_3_v7"                        "h10b_3_esp_v7"                   
##  [675] "h10b_3_esp_sinCodificar_v7"       "FechaInicioF2"                   
##  [677] "m1p0"                             "m1p1"                            
##  [679] "m1p2"                             "m1p3_1"                          
##  [681] "m1p3_2"                           "m1p3_2_esp"                      
##  [683] "m1p3_2_esp_sinCodificar"          "m1p4"                            
##  [685] "m1p5"                             "m1p6"                            
##  [687] "m1p7"                             "m1p8"                            
##  [689] "m2p1"                             "m2p2"                            
##  [691] "m2p3"                             "m2p3_1"                          
##  [693] "m2p3_2"                           "m2p4"                            
##  [695] "m2p5_1"                           "m2p5_2"                          
##  [697] "m2p5_3"                           "m2p6"                            
##  [699] "m2p7"                             "m2p7_1"                          
##  [701] "m2p7_2"                           "m2p7a"                           
##  [703] "m2p8_1"                           "m2p8_2"                          
##  [705] "m2p8_3"                           "m2p9_1"                          
##  [707] "m2p9_2"                           "m2p9_3"                          
##  [709] "m2p10_1"                          "m2p10_2"                         
##  [711] "m2p10_3"                          "m2p11a_PAS"                      
##  [713] "m2p11a_PAD"                       "mgstp1"                          
##  [715] "mgstp1_esp"                       "mgstp1_esp_sinCodificar"         
##  [717] "mgstp2"                           "mgstp2_esp"                      
##  [719] "mgstp3"                           "mgstp4"                          
##  [721] "mgstp5"                           "mgstp6"                          
##  [723] "mgstp7"                           "mgstp8"                          
##  [725] "mgstp9"                           "m3p1"                            
##  [727] "m3p2"                             "m3p3"                            
##  [729] "m3p3_esp"                         "m3p3_esp_sinCodificar"           
##  [731] "m3p4"                             "m3p4a"                           
##  [733] "m3p5"                             "m3p5_esp"                        
##  [735] "m3p5_esp_sinCodificar"            "m3p6"                            
##  [737] "m3p6_esp"                         "m3p6_esp_sinCodificar"           
##  [739] "m3p6_1_esp"                       "m3p6_1_esp_sinCodificar"         
##  [741] "m3p6_1"                           "m3p7"                            
##  [743] "m3p7_esp"                         "m3p7_esp_sinCodificar"           
##  [745] "m3p8"                             "m3p8_esp"                        
##  [747] "m3p8_esp_sinCodificar"            "m3p9"                            
##  [749] "m3p9_esp"                         "m3p9_esp_sinCodificar"           
##  [751] "m3p10"                            "m3p10_esp"                       
##  [753] "m3p10_esp_sinCodificar"           "m3p11"                           
##  [755] "m3p11_esp"                        "m3p11_esp_sinCodificar"          
##  [757] "m3p12"                            "m3p12_esp"                       
##  [759] "m3p12_esp_sinCodificar"           "m3p13"                           
##  [761] "m3p13_esp"                        "m3p13_esp_sinCodificar"          
##  [763] "m4p1"                             "m4p1_1"                          
##  [765] "m4p1_2"                           "m4p2"                            
##  [767] "m4p2_1"                           "m4p2_2"                          
##  [769] "m4p3"                             "m4p3_1"                          
##  [771] "m4p3_2"                           "m4p4"                            
##  [773] "m4p4_1"                           "m4p4_2"                          
##  [775] "m5p1"                             "m5p3"                            
##  [777] "m5p4"                             "m5p5"                            
##  [779] "m5p6"                             "m5p7"                            
##  [781] "m5p8"                             "m5p9"                            
##  [783] "m5p10"                            "m6p1"                            
##  [785] "m6p2"                             "m6p3"                            
##  [787] "m6p4"                             "m6p5"                            
##  [789] "m6p6"                             "m6p7"                            
##  [791] "m6p8"                             "m6p9"                            
##  [793] "m6p10"                            "m6p11"                           
##  [795] "m6p11a"                           "m6p12"                           
##  [797] "m6p12_1"                          "m6p12_2"                         
##  [799] "m6p13"                            "m6p14"                           
##  [801] "m6p15"                            "m6p15_esp"                       
##  [803] "m6p15_esp_sinCodificar"           "m6p16"                           
##  [805] "m6p17"                            "m7p1"                            
##  [807] "m7p2"                             "m7p3"                            
##  [809] "m7p4"                             "m7p4_esp"                        
##  [811] "m7p4_esp_sinCodificar"            "m7p5"                            
##  [813] "m7p6"                             "m7p7a"                           
##  [815] "m7p7b"                            "m7p8a"                           
##  [817] "m7p8b"                            "m7p9"                            
##  [819] "m7p10a"                           "m7p10b"                          
##  [821] "m7p11a"                           "m7p11b"                          
##  [823] "m7p11c"                           "m7p12"                           
##  [825] "m7p13"                            "m7p14"                           
##  [827] "m7p15"                            "m7p16"                           
##  [829] "m7p17"                            "m7p18"                           
##  [831] "m8p1"                             "m8p2"                            
##  [833] "m8p3"                             "m8p4"                            
##  [835] "m8p5"                             "m8p6"                            
##  [837] "m8p7"                             "m8p8"                            
##  [839] "m8p9"                             "m8p10"                           
##  [841] "m8p11"                            "m8p12_1"                         
##  [843] "m8p12_2"                          "m8p12_3"                         
##  [845] "m8p12_4"                          "m8p12_5"                         
##  [847] "m8p13"                            "m8p14"                           
##  [849] "m9p1A"                            "m9p1B"                           
##  [851] "m9p1C"                            "m9p1D"                           
##  [853] "m9p1E"                            "m9p2A"                           
##  [855] "m9p2B"                            "m9p2C"                           
##  [857] "m9p2D"                            "m9p2E"                           
##  [859] "m9p3A"                            "m9p3B"                           
##  [861] "m9p3C"                            "m9p3D"                           
##  [863] "m9p3E"                            "m9p4A"                           
##  [865] "m9p4B"                            "m9p4C"                           
##  [867] "m9p4D"                            "m9p4E"                           
##  [869] "m9p5A"                            "m9p5B"                           
##  [871] "m9p5C"                            "m9p5D"                           
##  [873] "m9p5E"                            "m9p6A"                           
##  [875] "m9p6B"                            "m9p6C"                           
##  [877] "m9p6D"                            "m9p6E"                           
##  [879] "m9p7A"                            "m9p7B"                           
##  [881] "m9p7C"                            "m9p7D"                           
##  [883] "m9p7E"                            "m9p8A"                           
##  [885] "m9p8A_esp"                        "m9p8A_esp_sinCodificar"          
##  [887] "m9p8B"                            "m9p8C"                           
##  [889] "m9p8D"                            "m9p8E"                           
##  [891] "m9p9A"                            "m9p9B"                           
##  [893] "m9p9C"                            "m9p9E"                           
##  [895] "m9p10A"                           "m9p10B"                          
##  [897] "m9p10C"                           "m9p10D"                          
##  [899] "m9p10E"                           "m9p11A"                          
##  [901] "m9p11B"                           "m9p11C"                          
##  [903] "m9p11E"                           "m9p12A"                          
##  [905] "m9p12B"                           "m9p12C"                          
##  [907] "m9p12D"                           "m9p12E"                          
##  [909] "m9p13A_esp"                       "m9p13A_esp_sinCodificar"         
##  [911] "m9p13A"                           "m9p13A_A"                        
##  [913] "m9p13A_A_esp"                     "m9p13A_A_esp_sinCodificar"       
##  [915] "m9p13B"                           "m9p13C"                          
##  [917] "m9p13E"                           "m9p13B_A"                        
##  [919] "m9p13C_A"                         "m9p13E_A"                        
##  [921] "m9p14A"                           "m9p14B"                          
##  [923] "m9p14C"                           "m9p14D"                          
##  [925] "m9p14E"                           "m9p15A"                          
##  [927] "m9p15B"                           "m9p15C"                          
##  [929] "m9p15D"                           "m9p15E"                          
##  [931] "m9p16A"                           "m9p16B"                          
##  [933] "m9p16C"                           "m9p16D"                          
##  [935] "m9p16E"                           "m9p17A"                          
##  [937] "m9p17B"                           "m9p17C"                          
##  [939] "m9p17E"                           "m9p18A"                          
##  [941] "m9p18B"                           "m9p18C"                          
##  [943] "m9p18E"                           "m9p19A"                          
##  [945] "m9p19B"                           "m9p19C"                          
##  [947] "m9p19D"                           "m9p19E"                          
##  [949] "m9p20A"                           "m9p20B"                          
##  [951] "m9p20C"                           "m9p20D"                          
##  [953] "m9p20E"                           "m9p21A"                          
##  [955] "m9p21B"                           "m9p21C"                          
##  [957] "m9p21D"                           "m9p21E"                          
##  [959] "m9p22A"                           "m9p22B"                          
##  [961] "m9p22C"                           "m9p22E"                          
##  [963] "m9p23A"                           "m9p23B"                          
##  [965] "m9p23C"                           "m9p23D"                          
##  [967] "m9p23E"                           "m9p24A"                          
##  [969] "m9p24B"                           "m9p24C"                          
##  [971] "m9p24D"                           "m9p24E"                          
##  [973] "m10p1"                            "m10p2"                           
##  [975] "m10p3"                            "m11p1_1_a"                       
##  [977] "m11p1_1_b"                        "m11p1_1_c"                       
##  [979] "m11p1_1_d"                        "m11p1_1_e"                       
##  [981] "m11p1_2_a"                        "m11p1_2_b"                       
##  [983] "m11p1_2_c"                        "m11p1_2_d"                       
##  [985] "m11p1_2_e"                        "m11p1_3_a"                       
##  [987] "m11p1_3_b"                        "m11p1_3_c"                       
##  [989] "m11p1_3_d"                        "m11p1_3_e"                       
##  [991] "m11p1_4_a"                        "m11p1_4_b"                       
##  [993] "m11p1_4_c"                        "m11p1_4_d"                       
##  [995] "m11p1_4_e"                        "m11p1_5_a"                       
##  [997] "m11p1_5_b"                        "m11p1_5_c"                       
##  [999] "m11p1_5_d"                        "m11p1_5_e"                       
## [1001] "m11p1_6_a"                        "m11p1_6_b"                       
## [1003] "m11p1_6_c"                        "m11p1_6_d"                       
## [1005] "m11p1_6_e"                        "m11p1_7_a_esp1"                  
## [1007] "m11p1_7_a_esp2"                   "m11p1_7_a_esp3"                  
## [1009] "m11p1_7_a_esp_sinCodificar"       "m11p1_7_a"                       
## [1011] "m11p1_7_b"                        "m11p1_7_c"                       
## [1013] "m11p1_7_d"                        "m11p1_7_e"                       
## [1015] "m13p1"                            "m13p1b"                          
## [1017] "m13p2"                            "m13p3"                           
## [1019] "m13p4"                            "m13p5"                           
## [1021] "m13p6"                            "m13p7"                           
## [1023] "m13p8"                            "m13p9"                           
## [1025] "m13p9b"                           "m13p10_esp_sinCodificar"         
## [1027] "m13p10"                           "m13p10_esp"                      
## [1029] "m13p11a"                          "m13p11b"                         
## [1031] "m13p11c"                          "m13p11d"                         
## [1033] "m13p11e"                          "m13p12"                          
## [1035] "m13p12_esp"                       "m13p12_esp_sinCodificar"         
## [1037] "m13p13_esp"                       "m13p13_esp_sinCodificar"         
## [1039] "m13p13"                           "m12p1"                           
## [1041] "m12p2"                            "m12p2_esp"                       
## [1043] "m12p2_esp_sinCodificar"           "m12p3"                           
## [1045] "m12p3_esp"                        "m12p3_esp_sinCodificar"          
## [1047] "m12p4"                            "m12p4_esp"                       
## [1049] "m12p4_esp_sinCodificar"           "m12p5_esp_sinCodificar"          
## [1051] "m12p5"                            "m12p5_esp"                       
## [1053] "m12p6_esp_sinCodificar"           "m12p6"                           
## [1055] "m12p6_esp"                        "m12p7"                           
## [1057] "m12p7_esp"                        "m12p7_esp_sinCodificar"          
## [1059] "INT53"                            "Recepcion_Laboratorio"           
## [1061] "Fecha_de_cierre"                  "v_25_OH_Vitamina_D2"             
## [1063] "aux_25_OH_Vitamina_D2"            "v_25_OH_Vitamina_D2_D3"          
## [1065] "v_25_OH_Vitamina_D3"              "Anticuerpos_Anti_Peptido_C"      
## [1067] "aux_Anticuerpos_Anti_Peptido_C"   "Anticuerpos_Anti_Peroxidasa_T"   
## [1069] "aux_Anticuerpos_Anti_Peroxidasa"  "Anticuerpos_anti_Trypanosoma_c"  
## [1071] "aux_A_Anticuerpos_anti_Tryp_c_"   "aux_B_Anticuerpos_anti_Tryp_c_"  
## [1073] "Arsenico_ENS"                     "Cadmio_ENS"                      
## [1075] "Colesterol_HDL"                   "Colesterol_LDL_Calculado"        
## [1077] "Colesterol_No_HDL_Calculado"      "Colesterol_Total"                
## [1079] "Colesterol_VLDL_Calculado"        "Creatinina_en_Orina"             
## [1081] "Creatinina_en_Sangre"             "Factor_Reumatoideo"              
## [1083] "aux_Factor_Reumatoideo"           "Folato_Serico"                   
## [1085] "GGT_Gamma_Glutamil_Transferasa"   "Glucosa"                         
## [1087] "Hemoglobina"                      "Hemoglobina_A1C"                 
## [1089] "aux_Hemoglobina_A1C"              "Hormona_Estimulante_Tiroides_TSH"
## [1091] "aux_Hormona_Estimulante_TSH"      "Mercurio_ENS"                    
## [1093] "Microalbuminuria"                 "aux_Microalbuminuria"            
## [1095] "Microalbuminuria_Creatinina"      "Plomo_ENS"                       
## [1097] "Potasio_K_en_Orina"               "Potasio_Creatinina"              
## [1099] "Proteina_C_Reactiva_cuantitativa" "aux_Proteina_C_Reactiva_cuant"   
## [1101] "Sangre_deposiciones_1_muestra"    "Sarampion_IgG"                   
## [1103] "Sodio_Na_en_Orina"                "Sodio_Creatinina"                
## [1105] "Tiroxina_Libre_FT4"               "Transaminasa_Glutamico_Piruvica" 
## [1107] "Trigliceridos"                    "Conf_ISP_Trypanosoma_cruzi"      
## [1109] "SMAv2"                            "SMA2v2"                          
## [1111] "Conglomerado"                     "Estrato"                         
## [1113] "Fexp_F1p_Corr"                    "Fexp_F2p_Corr"                   
## [1115] "Fexp_F1F2p_Corr"                  "Fexp_EX1p_Corr"                  
## [1117] "Fexp_F1F2EX1p_Corr"               "Fexp_EX2p_Corr"                  
## [1119] "Fexp_F1F2EX2p_Corr"               "Fexp_EX3p_Corr"                  
## [1121] "Fexp_F1F2EX3p_Corr"               "Ident_MAU_Reu"                   
## [1123] "MENOPAUSIA"                       "GPAQ"                            
## [1125] "sedent_tiempo_libre"              "transporte_activo"               
## [1127] "marcha_lenta"                     "SINDROME_METABOLICO"             
## [1129] "RCV_CHILENO_RECODIFICADO"         "v_25_OH_Vitamina_D2_corr"        
## [1131] "v_25_OH_Vitamina_D3_corr"         "v_25_OH_Vitamina_D2_D3_corr"     
## [1133] "VITAD_en4"                        "Def_severo_VITAD"                
## [1135] "Deficit_VITAD"                    "Insuficiencia_VITAD"             
## [1137] "Edad_AM2"                         "IMC"                             
## [1139] "fg_CKDschwartz_diminuido_60"      "fg_CKDschwartz_diminuido_30"     
## [1141] "EXP_SOLAR"                        "Consumo_lacteos"                 
## [1143] "CC_Talla"                         "ExtremoSur"                      
## [1145] "filter__"                         "af2a"                            
## [1147] "af2a_esp"                         "af2b"                            
## [1149] "af2b_esp"                         "af2c"                            
## [1151] "af2c_esp"                         "af2d"                            
## [1153] "af2d_esp"                         "af2e"                            
## [1155] "af2e_esp"                         "dis1a_corr"                      
## [1157] "dis1b_corr"                       "CONTROL_CT_AGNO_corr"            
## [1159] "yoduria_c"                        "Yoduria_mas_Creatinina_c"
# y abrirlo en otra ventana
# # View(namesdf0)
namesdf0[1:40]
##  [1] "IdEncuesta"      "FechaInicioF1"   "Region"          "Zona"           
##  [5] "IdSegmento"      "IdPersona_1"     "Ident7"          "Edad"           
##  [9] "Edad_Codificada" "Sexo"            "c1"              "c1_esp"         
## [13] "c2"              "c2a"             "c2b"             "c3"             
## [17] "c3a"             "c3b"             "c3c"             "c5"             
## [21] "c5_otro"         "c5b"             "c6"              "c7_0_nino"      
## [25] "c7_1_nino"       "c7_1_cuidador"   "c7_2_nino"       "c7_2_cuidador"  
## [29] "c7_3_nino"       "c7_3_cuidador"   "c7_4_nino"       "c7_4_cuidador"  
## [33] "c7_5_nino"       "c7_5_cuidador"   "e1"              "e2a"            
## [37] "e2b"             "e2c"             "e2d"             "e3_1"
names(df0)
##    [1] "IdEncuesta"                       "FechaInicioF1"                   
##    [3] "Region"                           "Zona"                            
##    [5] "IdSegmento"                       "IdPersona_1"                     
##    [7] "Ident7"                           "Edad"                            
##    [9] "Edad_Codificada"                  "Sexo"                            
##   [11] "c1"                               "c1_esp"                          
##   [13] "c2"                               "c2a"                             
##   [15] "c2b"                              "c3"                              
##   [17] "c3a"                              "c3b"                             
##   [19] "c3c"                              "c5"                              
##   [21] "c5_otro"                          "c5b"                             
##   [23] "c6"                               "c7_0_nino"                       
##   [25] "c7_1_nino"                        "c7_1_cuidador"                   
##   [27] "c7_2_nino"                        "c7_2_cuidador"                   
##   [29] "c7_3_nino"                        "c7_3_cuidador"                   
##   [31] "c7_4_nino"                        "c7_4_cuidador"                   
##   [33] "c7_5_nino"                        "c7_5_cuidador"                   
##   [35] "e1"                               "e2a"                             
##   [37] "e2b"                              "e2c"                             
##   [39] "e2d"                              "e3_1"                            
##   [41] "e3_2"                             "e3_3"                            
##   [43] "e4_1"                             "e4_2"                            
##   [45] "e4_3"                             "e4_4"                            
##   [47] "e4_5"                             "e5_1"                            
##   [49] "e5_2"                             "e5_3"                            
##   [51] "e6_1"                             "e6_2"                            
##   [53] "e6_3"                             "e7"                              
##   [55] "t9"                               "t9_2"                            
##   [57] "t10a"                             "t10b"                            
##   [59] "t10c"                             "t10d"                            
##   [61] "t10e"                             "t10f"                            
##   [63] "t10g"                             "t10h"                            
##   [65] "t10i"                             "t10j"                            
##   [67] "t10k"                             "cd1"                             
##   [69] "cd2"                              "cd3"                             
##   [71] "cd4"                              "cd5"                             
##   [73] "cd6"                              "cd7"                             
##   [75] "cd8"                              "cd8_corr"                        
##   [77] "cd9a_1"                           "cd9a_2"                          
##   [79] "cd9a_3"                           "cd9a_4"                          
##   [81] "cd9a_5"                           "cd9a_6"                          
##   [83] "cd9a_7"                           "cd9a_8"                          
##   [85] "cd9a_9"                           "cd9a_10"                         
##   [87] "cd9a_11"                          "cd9a_12"                         
##   [89] "cd9a_13"                          "cd9a_14"                         
##   [91] "cd9a_15"                          "cd9a_16"                         
##   [93] "cd9a_17"                          "cd9a_18"                         
##   [95] "cd9a_19"                          "cd9a_20"                         
##   [97] "cd9a_21"                          "cd9a_22"                         
##   [99] "cd9a_23"                          "cd9a_24"                         
##  [101] "a1"                               "a2"                              
##  [103] "a3a"                              "a3b"                             
##  [105] "a4"                               "a5"                              
##  [107] "a6a"                              "a6b"                             
##  [109] "a7"                               "a8"                              
##  [111] "a9a"                              "a9b"                             
##  [113] "a10"                              "a11"                             
##  [115] "a12a"                             "a12b"                            
##  [117] "a13"                              "a14"                             
##  [119] "a15a"                             "a15b"                            
##  [121] "a16a"                             "a16b"                            
##  [123] "a17"                              "a18"                             
##  [125] "a18_esp"                          "a18_esp_sinCodificar"            
##  [127] "a19"                              "d1_F1"                           
##  [129] "d2_F1"                            "d3_1"                            
##  [131] "d3_2"                             "d3_3"                            
##  [133] "d3_4"                             "d3_5"                            
##  [135] "d3_6"                             "d4"                              
##  [137] "d5"                               "d6"                              
##  [139] "d7"                               "v1"                              
##  [141] "v2"                               "v3"                              
##  [143] "v4"                               "v5"                              
##  [145] "v6"                               "v7"                              
##  [147] "v8_1"                             "v8_2"                            
##  [149] "v8_3"                             "v8_4"                            
##  [151] "v8_5"                             "v8_6"                            
##  [153] "v8_7"                             "au1_F1"                          
##  [155] "au2_F1"                           "au3_F1"                          
##  [157] "au4_F1"                           "au5_F1"                          
##  [159] "au6_F1"                           "au7_F1"                          
##  [161] "sm1"                              "sm2_1"                           
##  [163] "sm2_2"                            "sm2_3"                           
##  [165] "sm2_4"                            "sm2_5"                           
##  [167] "sm2_6"                            "sm2_7"                           
##  [169] "sm2_8"                            "sm2_9"                           
##  [171] "sm2_10"                           "sm2_11"                          
##  [173] "sm2_12"                           "sm2_13"                          
##  [175] "sm2_14"                           "sm2_15"                          
##  [177] "sm2_16"                           "sm3"                             
##  [179] "sm4"                              "sm5a"                            
##  [181] "sm5b"                             "sm5c"                            
##  [183] "sm6_1"                            "sm6_2"                           
##  [185] "sm6_3"                            "sm6_4"                           
##  [187] "sm6_5"                            "sm6_6"                           
##  [189] "sm6_7"                            "sm6_8"                           
##  [191] "sm6_9"                            "sm6_10"                          
##  [193] "sm6_11"                           "sm6_12"                          
##  [195] "sm7"                              "sm8"                             
##  [197] "sm9"                              "sd1_F1"                          
##  [199] "sd2_F1"                           "sd3_F1"                          
##  [201] "sd4_F1"                           "sd5_F1"                          
##  [203] "sd6_F1"                           "sd7_F1"                          
##  [205] "sd8_F1"                           "sd9_F1"                          
##  [207] "sd10_F1"                          "sd11_F1"                         
##  [209] "sd12_F1"                          "sd13_F1"                         
##  [211] "sd14_F1"                          "sd15_F1"                         
##  [213] "sd16_F1"                          "sd17_F1"                         
##  [215] "sd18_F1"                          "sd19_F1"                         
##  [217] "sd20_F1"                          "sd21_F1"                         
##  [219] "sd22_F1"                          "sd23_F1"                         
##  [221] "sd24_F1"                          "sd25_F1"                         
##  [223] "sd26a"                            "sd26b"                           
##  [225] "sd27_F1"                          "sd28_F1"                         
##  [227] "sd29_F1"                          "sd30"                            
##  [229] "sd31"                             "o1"                              
##  [231] "o2"                               "o2_esp"                          
##  [233] "o3a_1"                            "o3b_1"                           
##  [235] "o3b_1_esp"                        "o3b_1_esp_sinCodificar"          
##  [237] "o3c_1"                            "o3c_1_esp"                       
##  [239] "o3c_1_esp_sinCodificar"           "o3a_2"                           
##  [241] "o3b_2"                            "o3b_2_esp"                       
##  [243] "o3b_2_esp_sinCodificar"           "o3c_2"                           
##  [245] "o3c_2_esp"                        "o3c_2_esp_sinCodificar"          
##  [247] "o3a_3"                            "o3b_3"                           
##  [249] "o3b_3_esp"                        "o3b_3_esp_sinCodificar"          
##  [251] "o3c_3"                            "o3c_3_esp"                       
##  [253] "o3c_3_esp_sinCodificar"           "o3a_4"                           
##  [255] "o3b_4"                            "o3b_4_esp"                       
##  [257] "o3b_4_esp_sinCodificar"           "o3c_4"                           
##  [259] "o3c_4_esp"                        "o3c_4_esp_sinCodificar"          
##  [261] "o3a_5"                            "o3b_5"                           
##  [263] "o3b_5_esp"                        "o3c_5"                           
##  [265] "o3c_5_esp"                        "o3c_5_esp_sinCodificar"          
##  [267] "o3a_6"                            "o3b_6"                           
##  [269] "o3b_6_esp"                        "o3b_6_esp_sinCodificar"          
##  [271] "o3c_6"                            "o3c_6_esp"                       
##  [273] "o3c_6_esp_sinCodificar"           "o3a_7"                           
##  [275] "o3b_7"                            "o3b_7_esp"                       
##  [277] "o3b_7_esp_sinCodificar"           "o3c_7"                           
##  [279] "o3c_7_esp"                        "o3c_7_esp_sinCodificar"          
##  [281] "o3a_8"                            "o3b_8"                           
##  [283] "o3c_8"                            "o3c_8_esp"                       
##  [285] "o3c_8_esp_sinCodificar"           "o3a_9"                           
##  [287] "o3b_9"                            "o3c_9"                           
##  [289] "o3c_9_esp"                        "o3c_9_esp_sinCodificar"          
##  [291] "o3a_10"                           "o3b_10"                          
##  [293] "o3c_10"                           "o3c_10_esp"                      
##  [295] "o4"                               "o5"                              
##  [297] "o6"                               "o7"                              
##  [299] "o7_esp"                           "o7_esp_sinCodificar"             
##  [301] "n1"                               "n2"                              
##  [303] "n3"                               "n4_1"                            
##  [305] "n4_2"                             "n4_3"                            
##  [307] "n4_4"                             "n4_5"                            
##  [309] "n4_6"                             "n4_6_esp"                        
##  [311] "n4_6_esp_sinCodificar"            "n4_7"                            
##  [313] "die1a"                            "die1aesp"                        
##  [315] "die1b"                            "die2"                            
##  [317] "die3"                             "die4"                            
##  [319] "die5"                             "die6"                            
##  [321] "die7"                             "die8"                            
##  [323] "die9"                             "die10_a"                         
##  [325] "die10_b"                          "die10_c"                         
##  [327] "die10_d"                          "die10_e"                         
##  [329] "die10_f"                          "die11"                           
##  [331] "die12_cantidad"                   "die12_unidad"                    
##  [333] "die12e"                           "die13_cantidad"                  
##  [335] "die13_unidad"                     "die13e"                          
##  [337] "die14"                            "die14_esp"                       
##  [339] "die14_esp_sinCodificar"           "ts1"                             
##  [341] "ts2"                              "ts3"                             
##  [343] "ts4"                              "ts5"                             
##  [345] "ts5_aux"                          "ts6"                             
##  [347] "ts6_aux"                          "ts7"                             
##  [349] "ts8"                              "ts9_1"                           
##  [351] "ts9_2"                            "ts9_3"                           
##  [353] "ts9_4"                            "ts9_5"                           
##  [355] "ts9_6"                            "ts9_7"                           
##  [357] "ts9_8"                            "ts10"                            
##  [359] "ta1"                              "ta2"                             
##  [361] "ta3"                              "ta4"                             
##  [363] "ta5"                              "ta6"                             
##  [365] "ta7"                              "ta8"                             
##  [367] "ta9"                              "ta10_1"                          
##  [369] "ta10_1_esp"                       "ta10_1_esp_sinCodificar"         
##  [371] "ta10_2"                           "ta10_2_esp"                      
##  [373] "ta10_2_esp_sinCodificar"          "ta10_3"                          
##  [375] "ta10_3_esp"                       "ta10_3_esp_sinCodificar"         
##  [377] "ta11_1"                           "ta11_2"                          
##  [379] "ta12_1"                           "ta12_2"                          
##  [381] "ta13_1"                           "ta13_2"                          
##  [383] "ta14_1a"                          "ta14_1b"                         
##  [385] "ta14_2a"                          "ta14_2b"                         
##  [387] "ta14_3a"                          "ta14_3b"                         
##  [389] "ta14_4a"                          "ta14_4b"                         
##  [391] "ta14_5"                           "ta14_6a"                         
##  [393] "ta14_6b"                          "ta14_7a"                         
##  [395] "ta14_7b"                          "ta14_7c"                         
##  [397] "ta14_7c_sinCodificar"             "ta14a"                           
##  [399] "ta14b"                            "ta15"                            
##  [401] "ta16_1"                           "ta16_2"                          
##  [403] "ta16_3"                           "ta16_4"                          
##  [405] "ta16_5"                           "ta16_6"                          
##  [407] "ta16_7"                           "ta17"                            
##  [409] "ta18"                             "ta18_esp"                        
##  [411] "sb1"                              "sb2"                             
##  [413] "sb3"                              "sb4"                             
##  [415] "sb5_1"                            "sb5_2"                           
##  [417] "sb5_3"                            "sb5_4"                           
##  [419] "sb5_5"                            "h1a"                             
##  [421] "h1b"                              "h2"                              
##  [423] "h3"                               "h4"                              
##  [425] "h5"                               "h6"                              
##  [427] "h7"                               "h8"                              
##  [429] "h8_esp"                           "h8_esp_sinCodificar"             
##  [431] "h9_1"                             "h9_2"                            
##  [433] "h9_3"                             "h10_1"                           
##  [435] "h10_1_esp"                        "h10_1_esp_sinCodificar"          
##  [437] "h10a_1"                           "h10b_1"                          
##  [439] "h10b_1_esp"                       "h10b_1_esp_sinCodificar"         
##  [441] "h10_2"                            "h10_2_esp"                       
##  [443] "h10_2_esp_sinCodificar"           "h10a_2"                          
##  [445] "h10b_2"                           "h10b_2_esp"                      
##  [447] "h10b_2_esp_sinCodificar"          "h10_3"                           
##  [449] "h10_3_esp"                        "h10_3_esp_sinCodificar"          
##  [451] "h10a_3"                           "h10b_3"                          
##  [453] "h10b_3_esp"                       "h10b_3_esp_sinCodificar"         
##  [455] "h11"                              "di1"                             
##  [457] "di2a"                             "di2b"                            
##  [459] "di3"                              "di3a"                            
##  [461] "di4"                              "di5"                             
##  [463] "di6"                              "di7_1"                           
##  [465] "di7_2"                            "di7_3"                           
##  [467] "di8"                              "di9"                             
##  [469] "di10"                             "di11"                            
##  [471] "di12"                             "di13"                            
##  [473] "di14"                             "di14_esp"                        
##  [475] "di14_esp_sinCodificar"            "di15_1"                          
##  [477] "di15_2"                           "di15_3"                          
##  [479] "di16_1"                           "di16_1_esp"                      
##  [481] "di16_1_esp_sinCodificar"          "di16a_1"                         
##  [483] "di16b_1"                          "di16b_1_esp"                     
##  [485] "di16b_1_esp_sinCodificar"         "di16_2"                          
##  [487] "di16_2_esp"                       "di16_2_esp_sinCodificar"         
##  [489] "di16a_2"                          "di16b_2"                         
##  [491] "di16b_2_esp"                      "di16b_2_esp_sinCodificar"        
##  [493] "di16_3"                           "di16_3_esp"                      
##  [495] "di16_3_esp_sinCodificar"          "di16a_3"                         
##  [497] "di16b_3"                          "di16b_3_esp"                     
##  [499] "di16b_3_esp_sinCodificar"         "dis2"                            
##  [501] "dis3"                             "dis4"                            
##  [503] "dis5"                             "dis6"                            
##  [505] "af1a"                             "af1b"                            
##  [507] "af1c"                             "af1d"                            
##  [509] "af1e"                             "af1f"                            
##  [511] "af1g"                             "af1h"                            
##  [513] "af1i"                             "ps1"                             
##  [515] "ps2_F1"                           "ps3_F1"                          
##  [517] "ps4_F1"                           "ps5_F1"                          
##  [519] "ps6_F1"                           "ps6a"                            
##  [521] "ps7_F1"                           "ps8"                             
##  [523] "ps9_F1"                           "ps10_F1"                         
##  [525] "as3_1"                            "as4_1"                           
##  [527] "as5_1"                            "as5_esp_1"                       
##  [529] "as5_esp_sinCodificar_1"           "as6_1"                           
##  [531] "as7_1"                            "as7_corr_1"                      
##  [533] "as8_1"                            "as8_corr_1"                      
##  [535] "NEDU1"                            "as9_1"                           
##  [537] "as9_corr_1"                       "as10_1"                          
##  [539] "as10_esp_1"                       "as10_esp_sinCodificar_1"         
##  [541] "as11_1"                           "as13_1"                          
##  [543] "as14_1"                           "as15_1"                          
##  [545] "as16_1"                           "as17_1"                          
##  [547] "as17_sinCodificar_1"              "as18_1"                          
##  [549] "as18_sinCodificar_1"              "as19_1"                          
##  [551] "as19_esp_1"                       "as19_esp_sinCodificar_1"         
##  [553] "as20_1"                           "as21_1"                          
##  [555] "as22_1"                           "as22_esp_1"                      
##  [557] "as22_esp_sinCodificar_1"          "IdPersona_2"                     
##  [559] "IdSexo_2"                         "Edad_2"                          
##  [561] "as3_2"                            "as4_2"                           
##  [563] "as5_2"                            "as5_esp_2"                       
##  [565] "as5_esp_sinCodificar_2"           "as6_2"                           
##  [567] "as7_2"                            "as7_corr_2"                      
##  [569] "as8_2"                            "as8_corr_2"                      
##  [571] "as9_2"                            "as9_corr_2"                      
##  [573] "as10_2"                           "as10_esp_2"                      
##  [575] "as10_esp_sinCodificar_2"          "as11_2"                          
##  [577] "as13_2"                           "as14_2"                          
##  [579] "as15_2"                           "as16_2"                          
##  [581] "as17_2"                           "as17_sinCodificar_2"             
##  [583] "as18_2"                           "as18_sinCodificar_2"             
##  [585] "as19_2"                           "as19_esp_2"                      
##  [587] "as19_esp_sinCodificar_2"          "as20_2"                          
##  [589] "as21_2"                           "as22_2"                          
##  [591] "as22_esp_2"                       "as22_esp_sinCodificar_2"         
##  [593] "IdPersona_3"                      "IdSexo_3"                        
##  [595] "Edad_3"                           "as3_3"                           
##  [597] "as4_3"                            "as5_3"                           
##  [599] "as5_esp_3"                        "as5_esp_sinCodificar_3"          
##  [601] "as6_3"                            "as7_3"                           
##  [603] "as7_corr_3"                       "as8_3"                           
##  [605] "as8_corr_3"                       "as9_3"                           
##  [607] "as9_corr_3"                       "as10_3"                          
##  [609] "as10_esp_3"                       "as10_esp_sinCodificar_3"         
##  [611] "as11_3"                           "as13_3"                          
##  [613] "as14_3"                           "as15_3"                          
##  [615] "as16_3"                           "as17_3"                          
##  [617] "as17_sinCodificar_3"              "as18_3"                          
##  [619] "as18_sinCodificar_3"              "as19_3"                          
##  [621] "as19_esp_3"                       "as19_esp_sinCodificar_3"         
##  [623] "as20_3"                           "as21_3"                          
##  [625] "as22_3"                           "as27"                            
##  [627] "as28"                             "as29"                            
##  [629] "as29_esp"                         "as29_esp_sinCodificar"           
##  [631] "as30"                             "as31"                            
##  [633] "as32"                             "as32_esp"                        
##  [635] "as32_esp_sinCodificar"            "as33"                            
##  [637] "as34"                             "as35_1"                          
##  [639] "as35_2"                           "as36"                            
##  [641] "as37"                             "as38"                            
##  [643] "as39"                             "as22_esp_3"                      
##  [645] "as22_esp_sinCodificar_3"          "n_per"                           
##  [647] "h6_v7"                            "h7_v7"                           
##  [649] "h8_v7"                            "h8_esp_v7"                       
##  [651] "h8_esp_sinCodificar_v7"           "h9_1_v7"                         
##  [653] "h9_2_v7"                          "h9_3_v7"                         
##  [655] "h10_1_v7"                         "h10_1_esp_v7"                    
##  [657] "h10_1_esp_sinCodificar_v7"        "h10a_1_v7"                       
##  [659] "h10b_1_v7"                        "h10b_1_esp_v7"                   
##  [661] "h10b_1_esp_sinCodificar_v7"       "h10_2_v7"                        
##  [663] "h10_2_esp_v7"                     "h10_2_esp_sinCodificar_v7"       
##  [665] "h10a_2_v7"                        "h10b_2_v7"                       
##  [667] "h10b_2_esp_v7"                    "h10b_2_esp_sinCodificar_v7"      
##  [669] "h10_3_v7"                         "h10_3_esp_v7"                    
##  [671] "h10_3_esp_sinCodificar_v7"        "h10a_3_v7"                       
##  [673] "h10b_3_v7"                        "h10b_3_esp_v7"                   
##  [675] "h10b_3_esp_sinCodificar_v7"       "FechaInicioF2"                   
##  [677] "m1p0"                             "m1p1"                            
##  [679] "m1p2"                             "m1p3_1"                          
##  [681] "m1p3_2"                           "m1p3_2_esp"                      
##  [683] "m1p3_2_esp_sinCodificar"          "m1p4"                            
##  [685] "m1p5"                             "m1p6"                            
##  [687] "m1p7"                             "m1p8"                            
##  [689] "m2p1"                             "m2p2"                            
##  [691] "m2p3"                             "m2p3_1"                          
##  [693] "m2p3_2"                           "m2p4"                            
##  [695] "m2p5_1"                           "m2p5_2"                          
##  [697] "m2p5_3"                           "m2p6"                            
##  [699] "m2p7"                             "m2p7_1"                          
##  [701] "m2p7_2"                           "m2p7a"                           
##  [703] "m2p8_1"                           "m2p8_2"                          
##  [705] "m2p8_3"                           "m2p9_1"                          
##  [707] "m2p9_2"                           "m2p9_3"                          
##  [709] "m2p10_1"                          "m2p10_2"                         
##  [711] "m2p10_3"                          "m2p11a_PAS"                      
##  [713] "m2p11a_PAD"                       "mgstp1"                          
##  [715] "mgstp1_esp"                       "mgstp1_esp_sinCodificar"         
##  [717] "mgstp2"                           "mgstp2_esp"                      
##  [719] "mgstp3"                           "mgstp4"                          
##  [721] "mgstp5"                           "mgstp6"                          
##  [723] "mgstp7"                           "mgstp8"                          
##  [725] "mgstp9"                           "m3p1"                            
##  [727] "m3p2"                             "m3p3"                            
##  [729] "m3p3_esp"                         "m3p3_esp_sinCodificar"           
##  [731] "m3p4"                             "m3p4a"                           
##  [733] "m3p5"                             "m3p5_esp"                        
##  [735] "m3p5_esp_sinCodificar"            "m3p6"                            
##  [737] "m3p6_esp"                         "m3p6_esp_sinCodificar"           
##  [739] "m3p6_1_esp"                       "m3p6_1_esp_sinCodificar"         
##  [741] "m3p6_1"                           "m3p7"                            
##  [743] "m3p7_esp"                         "m3p7_esp_sinCodificar"           
##  [745] "m3p8"                             "m3p8_esp"                        
##  [747] "m3p8_esp_sinCodificar"            "m3p9"                            
##  [749] "m3p9_esp"                         "m3p9_esp_sinCodificar"           
##  [751] "m3p10"                            "m3p10_esp"                       
##  [753] "m3p10_esp_sinCodificar"           "m3p11"                           
##  [755] "m3p11_esp"                        "m3p11_esp_sinCodificar"          
##  [757] "m3p12"                            "m3p12_esp"                       
##  [759] "m3p12_esp_sinCodificar"           "m3p13"                           
##  [761] "m3p13_esp"                        "m3p13_esp_sinCodificar"          
##  [763] "m4p1"                             "m4p1_1"                          
##  [765] "m4p1_2"                           "m4p2"                            
##  [767] "m4p2_1"                           "m4p2_2"                          
##  [769] "m4p3"                             "m4p3_1"                          
##  [771] "m4p3_2"                           "m4p4"                            
##  [773] "m4p4_1"                           "m4p4_2"                          
##  [775] "m5p1"                             "m5p3"                            
##  [777] "m5p4"                             "m5p5"                            
##  [779] "m5p6"                             "m5p7"                            
##  [781] "m5p8"                             "m5p9"                            
##  [783] "m5p10"                            "m6p1"                            
##  [785] "m6p2"                             "m6p3"                            
##  [787] "m6p4"                             "m6p5"                            
##  [789] "m6p6"                             "m6p7"                            
##  [791] "m6p8"                             "m6p9"                            
##  [793] "m6p10"                            "m6p11"                           
##  [795] "m6p11a"                           "m6p12"                           
##  [797] "m6p12_1"                          "m6p12_2"                         
##  [799] "m6p13"                            "m6p14"                           
##  [801] "m6p15"                            "m6p15_esp"                       
##  [803] "m6p15_esp_sinCodificar"           "m6p16"                           
##  [805] "m6p17"                            "m7p1"                            
##  [807] "m7p2"                             "m7p3"                            
##  [809] "m7p4"                             "m7p4_esp"                        
##  [811] "m7p4_esp_sinCodificar"            "m7p5"                            
##  [813] "m7p6"                             "m7p7a"                           
##  [815] "m7p7b"                            "m7p8a"                           
##  [817] "m7p8b"                            "m7p9"                            
##  [819] "m7p10a"                           "m7p10b"                          
##  [821] "m7p11a"                           "m7p11b"                          
##  [823] "m7p11c"                           "m7p12"                           
##  [825] "m7p13"                            "m7p14"                           
##  [827] "m7p15"                            "m7p16"                           
##  [829] "m7p17"                            "m7p18"                           
##  [831] "m8p1"                             "m8p2"                            
##  [833] "m8p3"                             "m8p4"                            
##  [835] "m8p5"                             "m8p6"                            
##  [837] "m8p7"                             "m8p8"                            
##  [839] "m8p9"                             "m8p10"                           
##  [841] "m8p11"                            "m8p12_1"                         
##  [843] "m8p12_2"                          "m8p12_3"                         
##  [845] "m8p12_4"                          "m8p12_5"                         
##  [847] "m8p13"                            "m8p14"                           
##  [849] "m9p1A"                            "m9p1B"                           
##  [851] "m9p1C"                            "m9p1D"                           
##  [853] "m9p1E"                            "m9p2A"                           
##  [855] "m9p2B"                            "m9p2C"                           
##  [857] "m9p2D"                            "m9p2E"                           
##  [859] "m9p3A"                            "m9p3B"                           
##  [861] "m9p3C"                            "m9p3D"                           
##  [863] "m9p3E"                            "m9p4A"                           
##  [865] "m9p4B"                            "m9p4C"                           
##  [867] "m9p4D"                            "m9p4E"                           
##  [869] "m9p5A"                            "m9p5B"                           
##  [871] "m9p5C"                            "m9p5D"                           
##  [873] "m9p5E"                            "m9p6A"                           
##  [875] "m9p6B"                            "m9p6C"                           
##  [877] "m9p6D"                            "m9p6E"                           
##  [879] "m9p7A"                            "m9p7B"                           
##  [881] "m9p7C"                            "m9p7D"                           
##  [883] "m9p7E"                            "m9p8A"                           
##  [885] "m9p8A_esp"                        "m9p8A_esp_sinCodificar"          
##  [887] "m9p8B"                            "m9p8C"                           
##  [889] "m9p8D"                            "m9p8E"                           
##  [891] "m9p9A"                            "m9p9B"                           
##  [893] "m9p9C"                            "m9p9E"                           
##  [895] "m9p10A"                           "m9p10B"                          
##  [897] "m9p10C"                           "m9p10D"                          
##  [899] "m9p10E"                           "m9p11A"                          
##  [901] "m9p11B"                           "m9p11C"                          
##  [903] "m9p11E"                           "m9p12A"                          
##  [905] "m9p12B"                           "m9p12C"                          
##  [907] "m9p12D"                           "m9p12E"                          
##  [909] "m9p13A_esp"                       "m9p13A_esp_sinCodificar"         
##  [911] "m9p13A"                           "m9p13A_A"                        
##  [913] "m9p13A_A_esp"                     "m9p13A_A_esp_sinCodificar"       
##  [915] "m9p13B"                           "m9p13C"                          
##  [917] "m9p13E"                           "m9p13B_A"                        
##  [919] "m9p13C_A"                         "m9p13E_A"                        
##  [921] "m9p14A"                           "m9p14B"                          
##  [923] "m9p14C"                           "m9p14D"                          
##  [925] "m9p14E"                           "m9p15A"                          
##  [927] "m9p15B"                           "m9p15C"                          
##  [929] "m9p15D"                           "m9p15E"                          
##  [931] "m9p16A"                           "m9p16B"                          
##  [933] "m9p16C"                           "m9p16D"                          
##  [935] "m9p16E"                           "m9p17A"                          
##  [937] "m9p17B"                           "m9p17C"                          
##  [939] "m9p17E"                           "m9p18A"                          
##  [941] "m9p18B"                           "m9p18C"                          
##  [943] "m9p18E"                           "m9p19A"                          
##  [945] "m9p19B"                           "m9p19C"                          
##  [947] "m9p19D"                           "m9p19E"                          
##  [949] "m9p20A"                           "m9p20B"                          
##  [951] "m9p20C"                           "m9p20D"                          
##  [953] "m9p20E"                           "m9p21A"                          
##  [955] "m9p21B"                           "m9p21C"                          
##  [957] "m9p21D"                           "m9p21E"                          
##  [959] "m9p22A"                           "m9p22B"                          
##  [961] "m9p22C"                           "m9p22E"                          
##  [963] "m9p23A"                           "m9p23B"                          
##  [965] "m9p23C"                           "m9p23D"                          
##  [967] "m9p23E"                           "m9p24A"                          
##  [969] "m9p24B"                           "m9p24C"                          
##  [971] "m9p24D"                           "m9p24E"                          
##  [973] "m10p1"                            "m10p2"                           
##  [975] "m10p3"                            "m11p1_1_a"                       
##  [977] "m11p1_1_b"                        "m11p1_1_c"                       
##  [979] "m11p1_1_d"                        "m11p1_1_e"                       
##  [981] "m11p1_2_a"                        "m11p1_2_b"                       
##  [983] "m11p1_2_c"                        "m11p1_2_d"                       
##  [985] "m11p1_2_e"                        "m11p1_3_a"                       
##  [987] "m11p1_3_b"                        "m11p1_3_c"                       
##  [989] "m11p1_3_d"                        "m11p1_3_e"                       
##  [991] "m11p1_4_a"                        "m11p1_4_b"                       
##  [993] "m11p1_4_c"                        "m11p1_4_d"                       
##  [995] "m11p1_4_e"                        "m11p1_5_a"                       
##  [997] "m11p1_5_b"                        "m11p1_5_c"                       
##  [999] "m11p1_5_d"                        "m11p1_5_e"                       
## [1001] "m11p1_6_a"                        "m11p1_6_b"                       
## [1003] "m11p1_6_c"                        "m11p1_6_d"                       
## [1005] "m11p1_6_e"                        "m11p1_7_a_esp1"                  
## [1007] "m11p1_7_a_esp2"                   "m11p1_7_a_esp3"                  
## [1009] "m11p1_7_a_esp_sinCodificar"       "m11p1_7_a"                       
## [1011] "m11p1_7_b"                        "m11p1_7_c"                       
## [1013] "m11p1_7_d"                        "m11p1_7_e"                       
## [1015] "m13p1"                            "m13p1b"                          
## [1017] "m13p2"                            "m13p3"                           
## [1019] "m13p4"                            "m13p5"                           
## [1021] "m13p6"                            "m13p7"                           
## [1023] "m13p8"                            "m13p9"                           
## [1025] "m13p9b"                           "m13p10_esp_sinCodificar"         
## [1027] "m13p10"                           "m13p10_esp"                      
## [1029] "m13p11a"                          "m13p11b"                         
## [1031] "m13p11c"                          "m13p11d"                         
## [1033] "m13p11e"                          "m13p12"                          
## [1035] "m13p12_esp"                       "m13p12_esp_sinCodificar"         
## [1037] "m13p13_esp"                       "m13p13_esp_sinCodificar"         
## [1039] "m13p13"                           "m12p1"                           
## [1041] "m12p2"                            "m12p2_esp"                       
## [1043] "m12p2_esp_sinCodificar"           "m12p3"                           
## [1045] "m12p3_esp"                        "m12p3_esp_sinCodificar"          
## [1047] "m12p4"                            "m12p4_esp"                       
## [1049] "m12p4_esp_sinCodificar"           "m12p5_esp_sinCodificar"          
## [1051] "m12p5"                            "m12p5_esp"                       
## [1053] "m12p6_esp_sinCodificar"           "m12p6"                           
## [1055] "m12p6_esp"                        "m12p7"                           
## [1057] "m12p7_esp"                        "m12p7_esp_sinCodificar"          
## [1059] "INT53"                            "Recepcion_Laboratorio"           
## [1061] "Fecha_de_cierre"                  "v_25_OH_Vitamina_D2"             
## [1063] "aux_25_OH_Vitamina_D2"            "v_25_OH_Vitamina_D2_D3"          
## [1065] "v_25_OH_Vitamina_D3"              "Anticuerpos_Anti_Peptido_C"      
## [1067] "aux_Anticuerpos_Anti_Peptido_C"   "Anticuerpos_Anti_Peroxidasa_T"   
## [1069] "aux_Anticuerpos_Anti_Peroxidasa"  "Anticuerpos_anti_Trypanosoma_c"  
## [1071] "aux_A_Anticuerpos_anti_Tryp_c_"   "aux_B_Anticuerpos_anti_Tryp_c_"  
## [1073] "Arsenico_ENS"                     "Cadmio_ENS"                      
## [1075] "Colesterol_HDL"                   "Colesterol_LDL_Calculado"        
## [1077] "Colesterol_No_HDL_Calculado"      "Colesterol_Total"                
## [1079] "Colesterol_VLDL_Calculado"        "Creatinina_en_Orina"             
## [1081] "Creatinina_en_Sangre"             "Factor_Reumatoideo"              
## [1083] "aux_Factor_Reumatoideo"           "Folato_Serico"                   
## [1085] "GGT_Gamma_Glutamil_Transferasa"   "Glucosa"                         
## [1087] "Hemoglobina"                      "Hemoglobina_A1C"                 
## [1089] "aux_Hemoglobina_A1C"              "Hormona_Estimulante_Tiroides_TSH"
## [1091] "aux_Hormona_Estimulante_TSH"      "Mercurio_ENS"                    
## [1093] "Microalbuminuria"                 "aux_Microalbuminuria"            
## [1095] "Microalbuminuria_Creatinina"      "Plomo_ENS"                       
## [1097] "Potasio_K_en_Orina"               "Potasio_Creatinina"              
## [1099] "Proteina_C_Reactiva_cuantitativa" "aux_Proteina_C_Reactiva_cuant"   
## [1101] "Sangre_deposiciones_1_muestra"    "Sarampion_IgG"                   
## [1103] "Sodio_Na_en_Orina"                "Sodio_Creatinina"                
## [1105] "Tiroxina_Libre_FT4"               "Transaminasa_Glutamico_Piruvica" 
## [1107] "Trigliceridos"                    "Conf_ISP_Trypanosoma_cruzi"      
## [1109] "SMAv2"                            "SMA2v2"                          
## [1111] "Conglomerado"                     "Estrato"                         
## [1113] "Fexp_F1p_Corr"                    "Fexp_F2p_Corr"                   
## [1115] "Fexp_F1F2p_Corr"                  "Fexp_EX1p_Corr"                  
## [1117] "Fexp_F1F2EX1p_Corr"               "Fexp_EX2p_Corr"                  
## [1119] "Fexp_F1F2EX2p_Corr"               "Fexp_EX3p_Corr"                  
## [1121] "Fexp_F1F2EX3p_Corr"               "Ident_MAU_Reu"                   
## [1123] "MENOPAUSIA"                       "GPAQ"                            
## [1125] "sedent_tiempo_libre"              "transporte_activo"               
## [1127] "marcha_lenta"                     "SINDROME_METABOLICO"             
## [1129] "RCV_CHILENO_RECODIFICADO"         "v_25_OH_Vitamina_D2_corr"        
## [1131] "v_25_OH_Vitamina_D3_corr"         "v_25_OH_Vitamina_D2_D3_corr"     
## [1133] "VITAD_en4"                        "Def_severo_VITAD"                
## [1135] "Deficit_VITAD"                    "Insuficiencia_VITAD"             
## [1137] "Edad_AM2"                         "IMC"                             
## [1139] "fg_CKDschwartz_diminuido_60"      "fg_CKDschwartz_diminuido_30"     
## [1141] "EXP_SOLAR"                        "Consumo_lacteos"                 
## [1143] "CC_Talla"                         "ExtremoSur"                      
## [1145] "filter__"                         "af2a"                            
## [1147] "af2a_esp"                         "af2b"                            
## [1149] "af2b_esp"                         "af2c"                            
## [1151] "af2c_esp"                         "af2d"                            
## [1153] "af2d_esp"                         "af2e"                            
## [1155] "af2e_esp"                         "dis1a_corr"                      
## [1157] "dis1b_corr"                       "CONTROL_CT_AGNO_corr"            
## [1159] "yoduria_c"                        "Yoduria_mas_Creatinina_c"

Explore database

# summary(df0)
summary(df0[1:10])
##    IdEncuesta    FechaInicioF1            Region            Zona      
##  Min.   :20006   Min.   :2016-08-04   Min.   : 1.000   Min.   :1.000  
##  1st Qu.:22684   1st Qu.:2016-10-14   1st Qu.: 5.000   1st Qu.:1.000  
##  Median :25698   Median :2016-11-06   Median : 7.000   Median :1.000  
##  Mean   :25764   Mean   :2016-11-07   Mean   : 7.851   Mean   :1.159  
##  3rd Qu.:28501   3rd Qu.:2016-11-28   3rd Qu.:11.000   3rd Qu.:1.000  
##  Max.   :70000   Max.   :2017-02-23   Max.   :15.000   Max.   :2.000  
##    IdSegmento        IdPersona_1        Ident7                Edad      
##  Min.   : 1101101   Min.   :  1.0   Min.   :1918-11-26   Min.   :15.00  
##  1st Qu.: 5109123   1st Qu.: 40.0   1st Qu.:1952-04-23   1st Qu.:33.00  
##  Median : 8301107   Median : 82.0   Median :1966-10-30   Median :50.00  
##  Mean   : 8503764   Mean   :106.6   Mean   :1967-06-20   Mean   :48.91  
##  3rd Qu.:13101108   3rd Qu.:146.0   3rd Qu.:1983-07-25   3rd Qu.:64.00  
##  Max.   :15101204   Max.   :590.0   Max.   :2002-01-19   Max.   :98.00  
##  Edad_Codificada      Sexo      
##  Min.   :1.000   Min.   :1.000  
##  1st Qu.:2.000   1st Qu.:1.000  
##  Median :3.000   Median :2.000  
##  Mean   :2.684   Mean   :1.629  
##  3rd Qu.:3.000   3rd Qu.:2.000  
##  Max.   :4.000   Max.   :2.000

Explore database

names(df0)[grepl("Vitamina",names(df0))]
## [1] "v_25_OH_Vitamina_D2"         "aux_25_OH_Vitamina_D2"      
## [3] "v_25_OH_Vitamina_D2_D3"      "v_25_OH_Vitamina_D3"        
## [5] "v_25_OH_Vitamina_D2_corr"    "v_25_OH_Vitamina_D3_corr"   
## [7] "v_25_OH_Vitamina_D2_D3_corr"
names(df0)[grepl("Fexp",names(df0))]
## [1] "Fexp_F1p_Corr"      "Fexp_F2p_Corr"      "Fexp_F1F2p_Corr"   
## [4] "Fexp_EX1p_Corr"     "Fexp_F1F2EX1p_Corr" "Fexp_EX2p_Corr"    
## [7] "Fexp_F1F2EX2p_Corr" "Fexp_EX3p_Corr"     "Fexp_F1F2EX3p_Corr"
names(df0)[grepl("Congl",names(df0))]
## [1] "Conglomerado"
names(df0)[grepl("Estr",names(df0))]
## [1] "Estrato"
names(df0)[grepl("m4p3",names(df0))]
## [1] "m4p3"   "m4p3_1" "m4p3_2"

Extract and describe selected variables

df0_ext <- df0[c( "IdEncuesta","FechaInicioF1","Region","Zona", "Edad",
"Edad_Codificada","Sexo","NEDU1",
"Fexp_F1p_Corr","Fexp_F2p_Corr","Fexp_F1F2p_Corr",
"Fexp_EX1p_Corr","Fexp_F1F2EX1p_Corr", "Fexp_EX2p_Corr",
"Fexp_F1F2EX2p_Corr" ,"Fexp_EX3p_Corr","Fexp_F1F2EX3p_Corr",
"Conglomerado","Estrato","v_25_OH_Vitamina_D2_D3_corr")]
summary(df0_ext)
##    IdEncuesta    FechaInicioF1            Region            Zona      
##  Min.   :20006   Min.   :2016-08-04   Min.   : 1.000   Min.   :1.000  
##  1st Qu.:22684   1st Qu.:2016-10-14   1st Qu.: 5.000   1st Qu.:1.000  
##  Median :25698   Median :2016-11-06   Median : 7.000   Median :1.000  
##  Mean   :25764   Mean   :2016-11-07   Mean   : 7.851   Mean   :1.159  
##  3rd Qu.:28501   3rd Qu.:2016-11-28   3rd Qu.:11.000   3rd Qu.:1.000  
##  Max.   :70000   Max.   :2017-02-23   Max.   :15.000   Max.   :2.000  
##                                                                       
##       Edad       Edad_Codificada      Sexo           NEDU1      
##  Min.   :15.00   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:33.00   1st Qu.:2.000   1st Qu.:1.000   1st Qu.:2.000  
##  Median :50.00   Median :3.000   Median :2.000   Median :2.000  
##  Mean   :48.91   Mean   :2.684   Mean   :1.629   Mean   :1.983  
##  3rd Qu.:64.00   3rd Qu.:3.000   3rd Qu.:2.000   3rd Qu.:2.000  
##  Max.   :98.00   Max.   :4.000   Max.   :2.000   Max.   :3.000  
##                                                  NA's   :59     
##  Fexp_F1p_Corr       Fexp_F2p_Corr       Fexp_F1F2p_Corr    
##  Min.   :    2.594   Min.   :    3.131   Min.   :    3.131  
##  1st Qu.:  414.818   1st Qu.:  454.585   1st Qu.:  454.585  
##  Median : 1084.403   Median : 1209.348   Median : 1209.348  
##  Mean   : 2329.371   Mean   : 2630.248   Mean   : 2630.248  
##  3rd Qu.: 2633.405   3rd Qu.: 3017.059   3rd Qu.: 3017.059  
##  Max.   :24000.000   Max.   :24000.000   Max.   :24000.000  
##                      NA's   :713         NA's   :713        
##  Fexp_EX1p_Corr      Fexp_F1F2EX1p_Corr  Fexp_EX2p_Corr     Fexp_F1F2EX2p_Corr
##  Min.   :    3.163   Min.   :    3.163   Min.   :    4.32   Min.   :    4.34  
##  1st Qu.:  464.486   1st Qu.:  465.550   1st Qu.:  726.64   1st Qu.:  728.37  
##  Median : 1232.532   Median : 1241.019   Median : 1781.00   Median : 1791.02  
##  Mean   : 2666.477   Mean   : 2674.828   Mean   : 3765.63   Mean   : 3774.10  
##  3rd Qu.: 3071.311   3rd Qu.: 3071.912   3rd Qu.: 4341.99   3rd Qu.: 4349.47  
##  Max.   :24000.000   Max.   :24000.000   Max.   :34000.00   Max.   :34000.00  
##  NA's   :788         NA's   :805         NA's   :2386       NA's   :2386      
##  Fexp_EX3p_Corr     Fexp_F1F2EX3p_Corr  Conglomerado         Estrato      
##  Min.   :   10.27   Min.   :   10.41   Min.   : 1101101   Min.   : 11.00  
##  1st Qu.: 1808.85   1st Qu.: 1821.60   1st Qu.: 5109123   1st Qu.: 51.00  
##  Median : 4823.59   Median : 4832.53   Median : 8301107   Median : 81.00  
##  Mean   :10532.52   Mean   :10551.58   Mean   : 8503691   Mean   : 84.27  
##  3rd Qu.:12958.39   3rd Qu.:12962.88   3rd Qu.:13101108   3rd Qu.:131.00  
##  Max.   :98000.00   Max.   :98000.00   Max.   :15101204   Max.   :152.00  
##  NA's   :4857       NA's   :4857                                          
##  v_25_OH_Vitamina_D2_D3_corr
##  Min.   : 1.30              
##  1st Qu.:13.50              
##  Median :18.90              
##  Mean   :19.74              
##  3rd Qu.:24.90              
##  Max.   :59.70              
##  NA's   :3347
by(df0_ext, df0_ext$Sexo, summary)
## df0_ext$Sexo: 1
##    IdEncuesta    FechaInicioF1            Region            Zona      
##  Min.   :20013   Min.   :2016-09-03   Min.   : 1.000   Min.   :1.000  
##  1st Qu.:22616   1st Qu.:2016-10-14   1st Qu.: 5.000   1st Qu.:1.000  
##  Median :25585   Median :2016-11-04   Median : 7.000   Median :1.000  
##  Mean   :25608   Mean   :2016-11-06   Mean   : 7.803   Mean   :1.143  
##  3rd Qu.:28369   3rd Qu.:2016-11-27   3rd Qu.:11.000   3rd Qu.:1.000  
##  Max.   :31839   Max.   :2017-02-23   Max.   :15.000   Max.   :2.000  
##                                                                       
##       Edad       Edad_Codificada      Sexo       NEDU1      
##  Min.   :15.00   Min.   :1.000   Min.   :1   Min.   :1.000  
##  1st Qu.:31.00   1st Qu.:2.000   1st Qu.:1   1st Qu.:2.000  
##  Median :49.00   Median :3.000   Median :1   Median :2.000  
##  Mean   :47.93   Mean   :2.634   Mean   :1   Mean   :2.043  
##  3rd Qu.:63.00   3rd Qu.:3.000   3rd Qu.:1   3rd Qu.:2.000  
##  Max.   :95.00   Max.   :4.000   Max.   :1   Max.   :3.000  
##                                              NA's   :18     
##  Fexp_F1p_Corr       Fexp_F2p_Corr       Fexp_F1F2p_Corr    
##  Min.   :    6.424   Min.   :    8.888   Min.   :    8.888  
##  1st Qu.:  568.766   1st Qu.:  639.303   1st Qu.:  639.303  
##  Median : 1507.263   Median : 1786.075   Median : 1786.075  
##  Mean   : 3080.486   Mean   : 3532.108   Mean   : 3532.108  
##  3rd Qu.: 3768.953   3rd Qu.: 4395.049   3rd Qu.: 4395.049  
##  Max.   :24000.000   Max.   :24000.000   Max.   :24000.000  
##                      NA's   :296         NA's   :296        
##  Fexp_EX1p_Corr      Fexp_F1F2EX1p_Corr  Fexp_EX2p_Corr     Fexp_F1F2EX2p_Corr
##  Min.   :    8.888   Min.   :    8.888   Min.   :    9.11   Min.   :    9.11  
##  1st Qu.:  662.982   1st Qu.:  665.465   1st Qu.: 1038.81   1st Qu.: 1048.51  
##  Median : 1827.887   Median : 1837.309   Median : 2561.75   Median : 2579.03  
##  Mean   : 3567.447   Mean   : 3583.581   Mean   : 5053.01   Mean   : 5068.46  
##  3rd Qu.: 4470.406   3rd Qu.: 4514.441   3rd Qu.: 6300.13   3rd Qu.: 6323.65  
##  Max.   :24000.000   Max.   :24000.000   Max.   :34000.00   Max.   :34000.00  
##  NA's   :316         NA's   :325         NA's   :908        NA's   :908       
##  Fexp_EX3p_Corr     Fexp_F1F2EX3p_Corr  Conglomerado         Estrato      
##  Min.   :   64.65   Min.   :   64.65   Min.   : 1101101   Min.   : 11.00  
##  1st Qu.: 2810.20   1st Qu.: 2813.99   1st Qu.: 5301101   1st Qu.: 51.00  
##  Median : 6550.06   Median : 6554.51   Median : 8301106   Median : 81.00  
##  Mean   :14537.63   Mean   :14553.73   Mean   : 8501529   Mean   : 84.21  
##  3rd Qu.:20210.62   3rd Qu.:20237.63   3rd Qu.:13101108   3rd Qu.:131.00  
##  Max.   :98000.00   Max.   :98000.00   Max.   :15101204   Max.   :152.00  
##  NA's   :1825       NA's   :1825                                          
##  v_25_OH_Vitamina_D2_D3_corr
##  Min.   : 1.30              
##  1st Qu.:14.60              
##  Median :20.45              
##  Mean   :21.14              
##  3rd Qu.:26.75              
##  Max.   :50.10              
##  NA's   :1851               
## ------------------------------------------------------------ 
## df0_ext$Sexo: 2
##    IdEncuesta    FechaInicioF1            Region           Zona      
##  Min.   :20006   Min.   :2016-08-04   Min.   : 1.00   Min.   :1.000  
##  1st Qu.:22707   1st Qu.:2016-10-14   1st Qu.: 5.00   1st Qu.:1.000  
##  Median :25778   Median :2016-11-07   Median : 7.00   Median :1.000  
##  Mean   :25856   Mean   :2016-11-08   Mean   : 7.88   Mean   :1.168  
##  3rd Qu.:28584   3rd Qu.:2016-11-28   3rd Qu.:11.00   3rd Qu.:1.000  
##  Max.   :70000   Max.   :2017-02-22   Max.   :15.00   Max.   :2.000  
##                                                                      
##       Edad       Edad_Codificada      Sexo       NEDU1      
##  Min.   :15.00   Min.   :1.000   Min.   :2   Min.   :1.000  
##  1st Qu.:34.00   1st Qu.:2.000   1st Qu.:2   1st Qu.:1.000  
##  Median :50.00   Median :3.000   Median :2   Median :2.000  
##  Mean   :49.49   Mean   :2.713   Mean   :2   Mean   :1.948  
##  3rd Qu.:64.00   3rd Qu.:3.000   3rd Qu.:2   3rd Qu.:2.000  
##  Max.   :98.00   Max.   :4.000   Max.   :2   Max.   :3.000  
##                                              NA's   :41     
##  Fexp_F1p_Corr       Fexp_F2p_Corr       Fexp_F1F2p_Corr    
##  Min.   :    2.594   Min.   :    3.131   Min.   :    3.131  
##  1st Qu.:  346.014   1st Qu.:  387.065   1st Qu.:  387.065  
##  Median :  876.152   Median :  965.907   Median :  965.907  
##  Mean   : 1885.565   Mean   : 2110.152   Mean   : 2110.152  
##  3rd Qu.: 2138.313   3rd Qu.: 2402.234   3rd Qu.: 2402.234  
##  Max.   :24000.000   Max.   :24000.000   Max.   :24000.000  
##                      NA's   :417         NA's   :417        
##  Fexp_EX1p_Corr      Fexp_F1F2EX1p_Corr  Fexp_EX2p_Corr     Fexp_F1F2EX2p_Corr
##  Min.   :    3.163   Min.   :    3.163   Min.   :    4.32   Min.   :    4.34  
##  1st Qu.:  393.154   1st Qu.:  393.644   1st Qu.:  590.95   1st Qu.:  591.24  
##  Median :  983.557   Median :  984.762   Median : 1446.51   Median : 1448.54  
##  Mean   : 2143.831   Mean   : 2148.820   Mean   : 3023.28   Mean   : 3027.72  
##  3rd Qu.: 2458.421   3rd Qu.: 2461.515   3rd Qu.: 3504.26   3rd Qu.: 3511.23  
##  Max.   :24000.000   Max.   :24000.000   Max.   :34000.00   Max.   :34000.00  
##  NA's   :472         NA's   :480         NA's   :1478       NA's   :1478      
##  Fexp_EX3p_Corr     Fexp_F1F2EX3p_Corr  Conglomerado         Estrato      
##  Min.   :   10.27   Min.   :   10.41   Min.   : 1101101   Min.   : 11.00  
##  1st Qu.: 1473.66   1st Qu.: 1482.27   1st Qu.: 5109122   1st Qu.: 51.00  
##  Median : 3715.37   Median : 3722.69   Median : 8301108   Median : 81.00  
##  Mean   : 8317.50   Mean   : 8338.20   Mean   : 8504969   Mean   : 84.32  
##  3rd Qu.: 9521.39   3rd Qu.: 9521.39   3rd Qu.:13101109   3rd Qu.:131.00  
##  Max.   :98000.00   Max.   :98000.00   Max.   :15101204   Max.   :152.00  
##  NA's   :3032       NA's   :3032                                          
##  v_25_OH_Vitamina_D2_D3_corr
##  Min.   : 2.00              
##  1st Qu.:13.20              
##  Median :18.60              
##  Mean   :19.47              
##  3rd Qu.:24.60              
##  Max.   :59.70              
##  NA's   :1496

PSU, strata and weights

df0_ext <- df0[c( "IdEncuesta","Fexp_F1p_Corr","Fexp_F1F2EX1p_Corr", 
                  "Conglomerado","Estrato","v_25_OH_Vitamina_D2_D3_corr")]
df0_ext[1:10,]
##    IdEncuesta Fexp_F1p_Corr Fexp_F1F2EX1p_Corr Conglomerado Estrato
## 1       20006      98.31527                 NA     13101101     131
## 2       20008      49.15763           52.35046     13101101     131
## 3       20011     222.45004          275.63080     13101102     131
## 4       20012     124.96051          133.12834     13101102     131
## 5       20013     162.73767                 NA     13101102     131
## 6       20017     132.35255          140.94896     13101102     131
## 7       20018      86.41464           97.63442     13101102     131
## 8       20025      60.19316           64.12758     13101104     131
## 9       20026     156.78064          180.03816     13101104     131
## 10      20031     165.95195          190.56998     13101105     131
##    v_25_OH_Vitamina_D2_D3_corr
## 1                           NA
## 2                         13.4
## 3                         12.6
## 4                          4.3
## 5                           NA
## 6                         20.0
## 7                           NA
## 8                         23.5
## 9                           NA
## 10                          NA

Weights

• Weights provided with complex datasets are positive values associated with each (response) unit in the sample. There may be different weights for different stages of a survey (e.g. interview; nurse examination; collection of blood samples)

• Sampling weights: “inverse probability weights” are the inverse / reciprocal of the probability of selection

• The main purpose is to reduce bias (systematic error) in population estimates by up-weighting population subgroups that are under-represented in the sample and down-weighting subgroups that are over-represented in the sample

Weights compensate for:

• Unequal probability of selection

• Differences in response rates

• Post-stratification: adjust the sample distribution for key variables of interest (e.g. age, sex, region) so that the weighted sample conforms to a known population distribution in analysis (i.e. an adjustment after data has been collected)

• Does this sound familiar to you?

• Weights for analysis can include all 3 components: selection × (non-response × poststratification)

Weights (continued)…

We won’t worry today about how weights are calculated, but note that subjects aged 65 y or more were deliberately oversampled to ensure adequate numbers for subgroup analysis (and bearing in mind their response rates); and men (especially young men) are less likely to respond to surveys than women.

To achieve nationally-representative estimates (avoid bias) you would expect the weighting scheme to show: (1) lower weights for people aged 65 or more (compensate for over-sampling); and (2) higher weights for young men (adjust for lower response)

Dealing with survey data

  • SEs (estimate of variability) attached to estimates from complex surveys tend to be larger than those from a Simple Random Sample (of the same size)…this is the statistical price to be paid for the cost and convenience of complex designs.

  • Typically Stratification reduces SEs; clustering increases SEs

  • The difference in precision of estimates produced by a complex design relative to a SRS is estimated by the Design Effect (DEFF)

Design effect (DEFF)

\[DEFF=\frac{variance~estimate~(complex~design)}{variance~estimate~(SRS)}\] * Is the ratio of the actual variance of an estimateunder the sampling method used (e.g. stratification & clustering) to the variance calculated under the assumption of SRS.

  • The deff increases both as cluster size increases and as rho increases (fewer clusters and large homogeneity within them: intraclass correlation)

  • Deff = 1 implies similar precision for the complex design and SRS

  • Magnitude of the design effect is estimate-specific

Design effect

For a simple one-stage cluster sample: deff = 1 + rho (n-1) deff = 1 + (ρ × (n-1))

deff1 <- 1 + 0.05*(10-1) 
n1<-c(5,10,15,100)
p1<-c(0.05,0.10,0.15,0.1)
df1<-cbind(n1,p1)
df1<-as.data.frame(df1)
# View(df1)
df1$deff<-NA
df1$deff<-1+df1$p1*(df1$n1-1)
df1
##    n1   p1 deff
## 1   5 0.05  1.2
## 2  10 0.10  1.9
## 3  15 0.15  3.1
## 4 100 0.10 10.9

Where:

• n is the size of each cluster

• rho (ρ) is the intraclass correlation: which reflects the homogeneity of the sample:

  • ρ = 0 means elements of clusters are completely heterogeneous (members of the same cluster are diverse)

  • ρ = 1 means elements of clusters are completely homogeneous (members are completely alike)

Design effect

  • deff increases both as cluster size increases and as ρ increases (sample more people who are alike)

  • Interpretation? A deff = 2.0 is that the sampling variance for that estimate is twice as large than it would be if the survey was based on the same size sample but was SRS (hypothetical). A guide to the larger imprecision in the estimates.

  • Alternative interpretation: only a half as many sample cases would be needed to infer estimates with the same precision if you had used a SRS (effective sample size = ncomplex / deff)

  • But remember the costs and convenience!

Design effect

  • The SE is the √ variance.

  • Similarly, the design factor (deft) is the √ deff

  • A deft of 2.0 indicates that the SEs are twice as large as they would have been had the design been SRS.

Precision of an estimate

deft is typically greater than 1: so SEs for complex surveys are larger than those assuming SRS.

Complex survey design

  1. Specify survey design features

  2. Compute estimates of interest: means; crosstabulations; proportions; regression coefficients taking into account those design features

  3. Possible post-estimation commands giving more information: e.g. design effects; design factors; appropriate statistical testing for complex survey data

Extract and describe selected variables

Extract and describe selected variables

mydata <- descr(df0_ext)
mydata
## Descriptive Statistics  
## df0_ext$IdEncuesta  
## Label: IdEncuesta  
## N: 6233  
## 
##                     Conglomerado   Estrato   Fexp_F1F2EX1p_Corr   Fexp_F1p_Corr   IdEncuesta
## ----------------- -------------- --------- -------------------- --------------- ------------
##              Mean     8503691.47     84.27              2674.83         2329.37     25763.61
##           Std.Dev     4187166.26     42.10              3933.49         3526.21      3651.47
##               Min     1101101.00     11.00                 3.16            2.59     20006.00
##                Q1     5109123.00     51.00               465.53          414.82     22684.00
##            Median     8301107.00     81.00              1241.02         1084.40     25698.00
##                Q3    13101108.00    131.00              3072.37         2633.41     28501.00
##               Max    15101204.00    152.00             24000.00        24000.00     70000.00
##               MAD     5633900.76     59.30              1424.99         1223.40      4308.44
##               IQR     7991985.00     80.00              2606.36         2218.59      5817.00
##                CV           0.49      0.50                 1.47            1.51         0.14
##          Skewness          -0.10     -0.08                 3.06            3.34         1.36
##       SE.Skewness           0.03      0.03                 0.03            0.03         0.03
##          Kurtosis          -1.16     -1.19                10.99           13.68        13.25
##           N.Valid        6233.00   6233.00              5428.00         6233.00      6233.00
##         Pct.Valid         100.00    100.00                87.08          100.00       100.00
## 
## Table: Table continues below
## 
##  
## 
##                     v_25_OH_Vitamina_D2_D3_corr
## ----------------- -----------------------------
##              Mean                         19.74
##           Std.Dev                          8.37
##               Min                          1.30
##                Q1                         13.50
##            Median                         18.90
##                Q3                         24.90
##               Max                         59.70
##               MAD                          8.45
##               IQR                         11.40
##                CV                          0.42
##          Skewness                          0.59
##       SE.Skewness                          0.05
##          Kurtosis                          0.38
##           N.Valid                       2886.00
##         Pct.Valid                         46.30
# # View(mydata)

Identify weight to use

FactoresExp1 <- rio::import('ENS2017_FactoresExp.xlsx', sheet=1)
FactoresExp3 <- rio::import('ENS2017_FactoresExp.xlsx', sheet=3)
FactoresExp2 <- rio::import('ENS2017_FactoresExp.xlsx', sheet=2)
FactoresExp2
##                              Examen  FactorExp F1-Examen  FactorExp, F2-Examen 
## 1               v_25_OH_Vitamina_D2       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 2             aux_25_OH_Vitamina_D2       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 3            v_25_OH_Vitamina_D2_D3       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 4               v_25_OH_Vitamina_D3       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 5        Anticuerpos_Anti_Peptido_C       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 6    aux_Anticuerpos_Anti_Peptido_C       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 7     Anticuerpos_Anti_Peroxidasa_T       Fexp_EX3p_Corr    Fexp_F1F2EX3p_Corr 
## 8   aux_Anticuerpos_Anti_Peroxidasa       Fexp_EX3p_Corr    Fexp_F1F2EX3p_Corr 
## 9    Anticuerpos_anti_Trypanosoma_c       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 10   aux_A_Anticuerpos_anti_Tryp_c_       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 11   aux_B_Anticuerpos_anti_Tryp_c_       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 12                     Arsenico_ENS       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 13                       Cadmio_ENS       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 14                   Colesterol_HDL       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 15         Colesterol_LDL_Calculado       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 16      Colesterol_No_HDL_Calculado       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 17                 Colesterol_Total       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 18        Colesterol_VLDL_Calculado       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 19              Creatinina_en_Orina       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 20             Creatinina_en_Sangre       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 21               Factor_Reumatoideo       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 22           aux_Factor_Reumatoideo       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 23      Filtrado_Glomerular_CKD_EPI       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 24                    Folato_Serico       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 25   GGT_Gamma_Glutamil_Transferasa       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 26                          Glucosa       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 27                      Hemoglobina       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 28                  Hemoglobina_A1C       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 29              aux_Hemoglobina_A1C       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 30 Hormona_Estimulante_Tiroides_TSH       Fexp_EX3p_Corr    Fexp_F1F2EX3p_Corr 
## 31      aux_Hormona_Estimulante_TSH       Fexp_EX3p_Corr    Fexp_F1F2EX3p_Corr 
## 32                     Mercurio_ENS       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 33                 Microalbuminuria       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 34             aux_Microalbuminuria       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 35      Microalbuminuria_Creatinina       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 36                        Plomo_ENS       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 37               Potasio_K_en_Orina       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 38               Potasio_Creatinina       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 39 Proteina_C_Reactiva_cuantitativa       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 40    aux_Proteina_C_Reactiva_cuant       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 41    Sangre_deposiciones_1_muestra       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 42                    Sarampion_IgG       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 43                Sodio_Na_en_Orina       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 44                 Sodio_Creatinina       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 45               Tiroxina_Libre_FT4       Fexp_EX3p_Corr    Fexp_F1F2EX3p_Corr 
## 46  Transaminasa_Glutamico_Piruvica       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 47                    Trigliceridos       Fexp_EX2p_Corr    Fexp_F1F2EX2p_Corr 
## 48                  VPH_alto_riesgo       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 49                  VPH_genotipo_16       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 50                  VPH_genotipo_18       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 51                    VPH_resultado       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 
## 52                          Yoduria       Fexp_EX3p_Corr    Fexp_F1F2EX3p_Corr 
## 53                      aux_Yoduria       Fexp_EX3p_Corr    Fexp_F1F2EX3p_Corr 
## 54           Yoduria_mas_Creatinina       Fexp_EX3p_Corr    Fexp_F1F2EX3p_Corr 
## 55       Conf_ISP_Trypanosoma_cruzi       Fexp_EX1p_Corr    Fexp_F1F2EX1p_Corr 

Describe ENS as Simple Random Sample

table(df0$NEDU1)
## 
##    1    2    3 
## 1477 3323 1374
table(df0$NEDU1, useNA = "ifany")
## 
##    1    2    3 <NA> 
## 1477 3323 1374   59
prop.table(table(df0$NEDU1))
## 
##         1         2         3 
## 0.2392290 0.5382248 0.2225462
# Explorar paquetes
# library(epiDisplay)
# tab1(df0$NEDU1, sort.group = "decreasing", cum.percent = TRUE)
# library(janitor)
# tabyl(df0$NEDU1, sort = TRUE)
# library(summarytools)
# summarytools::freq(df0$NEDU1, order = "freq")
# library(questionr)
# questionr::freq(df0$NEDU1, cum = TRUE, sort = "dec", total = TRUE)

Describe ENS as Simple Random Sample

hist(df0$v_25_OH_Vitamina_D2_D3_corr)

Create/identify variables

df0$Gender <- factor(df0$Sexo,
                   levels=c("1","2"),
                   labels=c("Male",
                            "Female"))
df0$Educational_level <- factor(df0$NEDU1,
                              levels=c("1","2","3"),
                              labels=c("Low",
                                       "Mid",
                                       "High"))
df0$Area <- factor(df0$Zona,
                 levels=c("1","2"),
                 labels=c("Urban",
                          "Rural"))
df0$Age <- factor(df0$Edad_Codificada,
                levels=c("1","2","3","4"),
                labels=c("17-24",
                         "25-44",
                         "45-64",
                         "65+"))

df0$Region_n <- as.numeric(df0$Region)

df0$RM <- NA
df0$RM[(df0$Region_n==7)] <- 1
df0$RM[(df0$Region_n!=7)] <- 2

df0$ENS <- 2017
df0$person <- 1

Survey design

df0$Conglomerado_ <- NA
df0$Conglomerado_ <- df0$Conglomerado
df0$strata_ <- NA
df0$strata_ <- df0$Estrato
df0$fexp <- df0$Fexp_F1F2EX1p_Corr

Survey design

Explore

res_0b <- df0 %>%
  group_by(Region,Area,strata_) %>%
  summarize(Conglomerado_l = length(unique(Conglomerado_)))
res_0b
## # A tibble: 30 x 4
## # Groups:   Region, Area [30]
##    Region Area  strata_ Conglomerado_l
##     <dbl> <fct>   <dbl>          <int>
##  1      1 Urban     151             55
##  2      1 Rural     152              3
##  3      2 Urban      11             52
##  4      2 Rural      12              3
##  5      3 Urban      21             58
##  6      3 Rural      22              1
##  7      4 Urban      31             58
##  8      4 Rural      32              3
##  9      5 Urban      41             49
## 10      5 Rural      42              6
## # … with 20 more rows
res_0b <- df0 %>%
  group_by(Region,Area,strata_) %>%
  summarize(
    is.naFexp_F1F2EX1p_Corr = sum(is.na(Fexp_F1F2EX1p_Corr)),
    Fexp_F1F2EX1p_Corr_valid = sum(!is.na(Fexp_F1F2EX1p_Corr)),
    mean = mean(Fexp_F1F2EX1p_Corr, na.rm=TRUE)
  )
res_0b
## # A tibble: 30 x 6
## # Groups:   Region, Area [30]
##    Region Area  strata_ is.naFexp_F1F2EX1p_Corr Fexp_F1F2EX1p_Corr_valid  mean
##     <dbl> <fct>   <dbl>                   <int>                    <int> <dbl>
##  1      1 Urban     151                      58                      261  699.
##  2      1 Rural     152                       1                       39  185.
##  3      2 Urban      11                      68                      241 1061.
##  4      2 Rural      12                       8                       24  245.
##  5      3 Urban      21                      74                      254 1920.
##  6      3 Rural      22                       0                       10  280.
##  7      4 Urban      31                      26                      250  938.
##  8      4 Rural      32                       4                       24  433.
##  9      5 Urban      41                      34                      236 2062.
## 10      5 Rural      42                       9                       51 2604.
## # … with 20 more rows
res_0b <- df0 %>%
  group_by(Age) %>%
  summarize(
    is.naFexp_F1F2EX1p_Corr = sum(is.na(Fexp_F1F2EX1p_Corr)),
    Fexp_F1F2EX1p_Corr_valid = sum(!is.na(Fexp_F1F2EX1p_Corr)),
    mean_ = mean(Fexp_F1F2EX1p_Corr, na.rm=TRUE),
    sum_ = sum(Fexp_F1F2EX1p_Corr, na.rm=TRUE),
    n_ = n()
)
res_0b
## # A tibble: 4 x 6
##   Age   is.naFexp_F1F2EX1p_Corr Fexp_F1F2EX1p_Corr_valid mean_     sum_    n_
##   <fct>                   <int>                    <int> <dbl>    <dbl> <int>
## 1 17-24                     129                      708 3867. 2737931.   837
## 2 25-44                     267                     1548 3498. 5414690.  1815
## 3 45-64                     233                     1831 2424. 4437480.  2064
## 4 65+                       176                     1341 1438. 1928868.  1517

Survey design

Explore

length(unique(df0$Conglomerado_))
## [1] 1077
table(df0$Conglomerado_,df0$strata_)
##           
##            11 12 21 22 31 32 41 42 51 52 61 62 71 72 81 82 91 92 101 102 111
##   1101101   6  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101102   8  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101103   8  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101104  10  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101105   2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101106   4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101107   6  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101108   5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101109   9  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101110   5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101111   7  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101112   8  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101114   5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101115   8  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101119   7  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101120   5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101121   9  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101122   8  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101123   3  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101124   9  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101125   7  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101126   6  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101127   7  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101130   9  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101131   6  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101132   5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101133   8  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101136   2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1101137   7  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1105101   5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1105102   6  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1105103   4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1105201   0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107101   8  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107102   8  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107103   4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107104   5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107105   8  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107106   6  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107107   8  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107108   4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107109   4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107111   4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107112   3  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107113   4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107114   2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107115   6  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107116   7  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107117   6  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107118   4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107119   4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107120   5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1107121   5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1401201   0  8  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   1401202   0  7  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101101   0  0  4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101102   0  0  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101103   0  0  8  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101104   0  0  3  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101105   0  0  7  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101106   0  0  3  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101107   0  0  4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101108   0  0  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101109   0  0  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101110   0  0  6  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101111   0  0  8  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101112   0  0  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101113   0  0  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101114   0  0  4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101115   0  0  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101116   0  0  6  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101118   0  0  3  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101119   0  0  8  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101120   0  0  4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101121   0  0  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101123   0  0  4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101125   0  0  3  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101126   0  0  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101128   0  0  8  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101129   0  0  7  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101130   0  0  4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101131   0  0  7  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101132   0  0  8  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101133   0  0  4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101134   0  0  4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101135   0  0  9  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101136   0  0  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101137   0  0  9  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101138   0  0  7  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101139   0  0  6  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2101140   0  0 10  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2201101   0  0  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2201102   0  0  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2201103   0  0  4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2201104   0  0  8  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2201105   0  0  6  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2201106   0  0  7  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2201107   0  0  6  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2201108   0  0  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2201109   0  0  8  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2201110   0  0  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2201111   0  0  6  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2201113   0  0  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2201114   0  0  7  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
##   2201115   0  0  6  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0   0   0
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##   14204105   0   0   0   0   0   4   0   0   0
##   15101101   0   0   0   0   0   0   0   1   0
##   15101102   0   0   0   0   0   0   0   5   0
##   15101103   0   0   0   0   0   0   0   5   0
##   15101104   0   0   0   0   0   0   0   7   0
##   15101105   0   0   0   0   0   0   0   4   0
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##   15101107   0   0   0   0   0   0   0   7   0
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##   15101112   0   0   0   0   0   0   0   7   0
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##   15101116   0   0   0   0   0   0   0   7   0
##   15101118   0   0   0   0   0   0   0   6   0
##   15101119   0   0   0   0   0   0   0   4   0
##   15101120   0   0   0   0   0   0   0   6   0
##   15101121   0   0   0   0   0   0   0   6   0
##   15101122   0   0   0   0   0   0   0   5   0
##   15101123   0   0   0   0   0   0   0   6   0
##   15101124   0   0   0   0   0   0   0   7   0
##   15101125   0   0   0   0   0   0   0   5   0
##   15101126   0   0   0   0   0   0   0   4   0
##   15101127   0   0   0   0   0   0   0   6   0
##   15101128   0   0   0   0   0   0   0   5   0
##   15101129   0   0   0   0   0   0   0   6   0
##   15101130   0   0   0   0   0   0   0   5   0
##   15101131   0   0   0   0   0   0   0   7   0
##   15101132   0   0   0   0   0   0   0   4   0
##   15101133   0   0   0   0   0   0   0   5   0
##   15101134   0   0   0   0   0   0   0   6   0
##   15101135   0   0   0   0   0   0   0   7   0
##   15101136   0   0   0   0   0   0   0   5   0
##   15101137   0   0   0   0   0   0   0   6   0
##   15101138   0   0   0   0   0   0   0   7   0
##   15101139   0   0   0   0   0   0   0   6   0
##   15101140   0   0   0   0   0   0   0   4   0
##   15101141   0   0   0   0   0   0   0   7   0
##   15101142   0   0   0   0   0   0   0   6   0
##   15101143   0   0   0   0   0   0   0   7   0
##   15101144   0   0   0   0   0   0   0   7   0
##   15101146   0   0   0   0   0   0   0   7   0
##   15101148   0   0   0   0   0   0   0   5   0
##   15101149   0   0   0   0   0   0   0   7   0
##   15101150   0   0   0   0   0   0   0   6   0
##   15101151   0   0   0   0   0   0   0   7   0
##   15101152   0   0   0   0   0   0   0   7   0
##   15101153   0   0   0   0   0   0   0   7   0
##   15101154   0   0   0   0   0   0   0   7   0
##   15101155   0   0   0   0   0   0   0   6   0
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##   15101158   0   0   0   0   0   0   0   6   0
##   15101201   0   0   0   0   0   0   0   0  20
##   15101203   0   0   0   0   0   0   0   0  10
##   15101204   0   0   0   0   0   0   0   0  10

Survey design

Explore using ggplot

ggplot(df0)+
  geom_point(aes(x=Edad, y=Fexp_F1F2EX1p_Corr))

Survey design

ggplot(df0)+
  geom_point(aes(x=Edad, y=Fexp_F1F2EX1p_Corr, 
                 col=Educational_level, shape=Gender))

Subset of valid values for Survey design

create a subset with valid values: fexp, strata_ Conglomerado_

# drop cases
df0 <- subset(df0,!is.na(fexp)& !is.na(strata_)& !is.na(Conglomerado_))
# I do not step over df0 in case I want to use another fexp

Recycle the code <- Identify exposure & outcome

df0$exposure <- df0$Educational_level
df0$outcome <- df0$v_25_OH_Vitamina_D2_D3_corr
df0$outcome1 <- as.numeric(df0$v_25_OH_Vitamina_D2_D3_corr<30)
df0$outcome2 <- as.numeric(df0$v_25_OH_Vitamina_D2_D3_corr<20)
df0$outcome3 <- as.numeric(df0$v_25_OH_Vitamina_D2_D3_corr<12)
df0$outcome5_ <- NA
df0$outcome5_[df0$v_25_OH_Vitamina_D2_D3_corr>=30] <- 0
df0$outcome5_[df0$v_25_OH_Vitamina_D2_D3_corr<30] <- 1
df0$outcome5_[df0$v_25_OH_Vitamina_D2_D3_corr<20] <- 2
df0$outcome5_[df0$v_25_OH_Vitamina_D2_D3_corr<12] <- 3
df0$outcome5_ <- as.factor(df0$outcome5_)

ALL VARIABLES NEEDS TO BE CREATED BEFORE USING THE “as_survey_design”

Creating Survey design

survey_design <- df0 %>%
  as_survey_design(id=Conglomerado_, 
                   # nest=TRUE,
                   weight = fexp, 
                   strata=strata_ 
                   )
options(survey.lonely.psu="certainty")
# nest=TRUE IS REQUIERED TO ANALISE TRENDS USING ENS. 
# PSU LABELS ARE SHARED

options(survey.lonely.psu=“certainty”)????

res_0b <- df0 %>%
  group_by(Region,Area,strata_) %>%
  summarize(Conglomerado_l = length(unique(Conglomerado_)))
Single_PSU <- subset(res_0b,Conglomerado_l==1)
Single_PSU
## # A tibble: 1 x 4
## # Groups:   Region, Area [1]
##   Region Area  strata_ Conglomerado_l
##    <dbl> <fct>   <dbl>          <int>
## 1      3 Rural      22              1

options(survey.lonely.psu=“certainty”)????

http://r-survey.r-forge.r-project.org/survey/exmample-lonely.html

If only one PSU is sampled from a particular stratum the variance can’t be computed (there is no unbiased estimator and the standard estimator gives 0/0). Variance estimation in sample surveys involves variances computed within primary sampling units.

One exception to this is “certainty” PSUs in sampling without replacement, where the population has only one PSU in the stratum. With 100% sampling, there is no contribution to the variance from the first stage of sampling in this stratum. A single-PSU stratum makes no contribution to the variance (for multistage sampling it makes no contribution at that level of sampling).

WARNING Survey design

“As a general rule when working with complex survey data such as NHANES, you should never drop records from your analysis dataset before executing your analysis procedures. Instead, use the special statements provided in your software’s analysis procedure to perform subgroup analyses.” https://wwwn.cdc.gov/nchs/nhanes/tutorials/module4.aspx

Survey design: SUBSET

survey_design <- subset(survey_design,
                      !is.na(outcome) &!is.na(exposure) & Edad>=15)

Summary

df0 <- rio::import('F1_F2_EX_V9_20AUG18AP5.dta')
df0$Gender <- factor(df0$Sexo,
                   levels=c("1","2"),
                   labels=c("Male","Female"))
df0$Educational_level <- factor(df0$NEDU1,
                              levels=c("1","2","3"),
                              labels=c("Low","Mid","High"))
df0$Area <- factor(df0$Zona,
                 levels=c("1","2"),
                 labels=c("Urban","Rural"))
df0$Age <- factor(df0$Edad_Codificada,
                levels=c("1","2","3","4"),
                labels=c("17-24","25-44","45-64","65+"))

df0$exposure <- df0$Educational_level
df0$outcome <- df0$v_25_OH_Vitamina_D2_D3_corr
df0$outcome1 <- as.numeric(df0$v_25_OH_Vitamina_D2_D3_corr<30)
df0$outcome2 <- as.numeric(df0$v_25_OH_Vitamina_D2_D3_corr<20)
df0$outcome3 <- as.numeric(df0$v_25_OH_Vitamina_D2_D3_corr<12)

Summary

df0$outcome5_ <- NA
df0$outcome5_[df0$v_25_OH_Vitamina_D2_D3_corr>=30] <- 0
df0$outcome5_[df0$v_25_OH_Vitamina_D2_D3_corr<30] <- 1
df0$outcome5_[df0$v_25_OH_Vitamina_D2_D3_corr<20] <- 2
df0$outcome5_[df0$v_25_OH_Vitamina_D2_D3_corr<12] <- 3
df0$outcome5_ <- as.factor(df0$outcome5_)
df0$ENS <- 2017
df0$person <- 1
df0$Conglomerado_ <- NA
df0$Conglomerado_ <- df0$Conglomerado
df0$strata_ <- NA
df0$strata_ <- df0$Estrato
df0$fexp <- df0$Fexp_F1F2EX1p_Corr 
df0 <- subset(df0,!is.na(fexp)& !is.na(strata_)& !is.na(Conglomerado_))

Summary

# https://www.rdocumentation.org/packages/srvyr/versions/0.1.1/topics/as_survey_design
survey_design <- df0 %>%
  as_survey_design(id=Conglomerado_, 
                   weight = fexp, 
                   strata=strata_)
options(survey.lonely.psu="certainty")
## Survey design: SUBSET
survey_design <- subset(survey_design,
                      !is.na(outcome) &!is.na(exposure) & Edad>=15 )

Perform the analyses

# MEAN
# svyby(~outcome, ~person, survey_design, svymean)
# svyby(~outcome, ~person, survey_design, svymean, deff=TRUE)
# svyby(~outcome, ~exposure, survey_design, svymean, vartype=c("se","ci"), deff=TRUE)
# svyby(~outcome, ~exposure+Gender, survey_design, svymean, vartype=c("se","ci"), deff=TRUE)
# svyby(~outcome+Edad, ~exposure+Gender, survey_design, svymean, vartype=c("se","ci"), deff=TRUE)

# PROPORTION
# svyby(~outcome1, ~person, survey_design, svymean)
# svyby(~outcome1+outcome2, ~exposure+Gender, survey_design, svymean, 
#       vartype=c("se","ci"), deff=TRUE, proportion = TRUE,  prop_method = c("logit"))
# svyby(~outcome1, ~exposure+Gender, survey_design, svytotal, 
#       vartype=c("se","ci"), deff=TRUE, proportion = TRUE,  prop_method = c("logit"))

# CROSSTAB
# tbl <- svytable(~exposure+Area, survey_design)
# summary(tbl, statistic="Chisq")

# LINEAR REGRESSION
# svyglm(outcome~ Area+Edad, survey_design)
# svyglm(outcome~ Area+Edad, survey_design, deff=TRUE)

# LOGISTIC REGRESSION
# svyglm(outcome1 ~Area+Edad, survey_design, 
#                     family = quasibinomial(link = "logit"))
# svyglm(outcome1 ~Area+Edad, survey_design, 
#                     family = quasibinomial(link = "logit"), deff=TRUE)

Examples

  • tables

  • csv files to export data

  • plots with ggplot2

MEAN

svyby(~outcome, ~person, survey_design, svymean, vartype=c(“se”,“ci”), deff=TRUE)

svyby(~outcome, ~exposure, survey_design, svymean, vartype=c(“se”,“ci”), deff=TRUE)

svyby(~outcome, ~exposure+Gender, survey_design, svymean, vartype=c(“se”,“ci”), deff=TRUE)

svyby(~outcome+Edad, ~exposure+Gender, survey_design, svymean, vartype=c(“se”,“ci”), deff=TRUE)

Mean and error of plasma vitamin D levels (ng / mL)

We created this variable before df0$person <- 1 “person” is a “variable” with constant value=1 I use “person” to calculate mean in total sample using svyby instead of svymean()

# svyby(~outcome, ~person, survey_design, svymean)
svyby(~outcome, ~person, survey_design, svymean)
##   person outcome        se
## 1      1 19.8146 0.2964862
svyby(~outcome, ~exposure, survey_design, svymean)
##      exposure  outcome        se
## Low       Low 19.78772 0.6054778
## Mid       Mid 19.78517 0.3667705
## High     High 19.89291 0.6933440
svyby(~outcome, ~exposure, survey_design, svymean, vartype=c("se","ci"), 
      deff=TRUE)
##      exposure  outcome        se     ci_l     ci_u DEff.outcome
## Low       Low 19.78772 0.6054778 18.60101 20.97444     4.084561
## Mid       Mid 19.78517 0.3667705 19.06632 20.50403     3.238398
## High     High 19.89291 0.6933440 18.53398 21.25184     3.811599
svyby(~outcome+m4p3, ~person, survey_design, svymean, vartype=c("se","ci"), 
      deff=TRUE)
##   person outcome m4p3 se.outcome se.m4p3 ci_l.outcome ci_l.m4p3 ci_u.outcome
## 1      1 19.8146   NA        Inf     NaN         -Inf        NA          Inf
##   ci_u.m4p3 DEff.outcome DEff.m4p3
## 1        NA          Inf       NaN
survey_design <- subset(survey_design,
                      !is.na(m4p3))

svyby(~outcome+m4p3, ~person, survey_design, svymean, vartype=c("se","ci"), 
      deff=TRUE)
##   person  outcome     m4p3 se.outcome  se.m4p3 ci_l.outcome ci_l.m4p3
## 1      1 19.83054 82.72041  0.2970905 5.388483     19.24825  72.15918
##   ci_u.outcome ci_u.m4p3 DEff.outcome DEff.m4p3
## 1     20.41283  93.28164     3.753644 0.9830649
svyby(~outcome+m4p3, ~exposure+Age, survey_design, svymean, vartype=c("se","ci"), 
      deff=TRUE)
##            exposure   Age  outcome     m4p3 se.outcome   se.m4p3 ci_l.outcome
## Low.17-24       Low 17-24 21.60786 86.27520  1.9839076  5.566215     17.71948
## Mid.17-24       Mid 17-24 19.11087 82.02461  0.6840735  1.434332     17.77011
## High.17-24     High 17-24 20.94545 81.42157  1.4188238  1.286227     18.16461
## Low.25-44       Low 25-44 21.84417 98.56459  1.4273233  2.615225     19.04667
## Mid.25-44       Mid 25-44 20.80340 84.64787  0.5526297  7.986028     19.72026
## High.25-44     High 25-44 19.38793 87.47051  0.9628617  2.633382     17.50076
## Low.45-64       Low 45-64 24.83429 99.06341  3.3245625  2.579077     18.31827
## Mid.45-64       Mid 45-64 19.26649 99.47203  0.8574700  1.918700     17.58588
## High.45-64     High 45-64 15.75892 89.81648  1.4281759  2.800615     12.95975
## Low.65+         Low   65+ 19.06233 58.26738  0.5973700 29.388717     17.89151
## Mid.65+         Mid   65+ 18.18289 94.86977  0.6623262  1.244440     16.88475
## High.65+       High   65+ 21.65193 97.28270  1.4038781  2.580228     18.90038
##             ci_l.m4p3 ci_u.outcome ci_u.m4p3 DEff.outcome  DEff.m4p3
## Low.17-24  75.3656194     25.49625  97.18478     1.189552 1.91358502
## Mid.17-24  79.2133702     20.45163  84.83585     2.054094 2.27250427
## High.17-24 78.9006088     23.72630  83.94253     3.142143 1.99727737
## Low.25-44  93.4388446     24.64168 103.69034     2.601413 2.76890737
## Mid.25-44  68.9955381     21.88653 100.30019     3.066284 0.59066486
## High.25-44 82.3091729     21.27511  92.63184     4.039315 0.09325816
## Low.45-64  94.0085146     31.35031 104.11831     5.581114 3.06628755
## Mid.45-64  95.7114454     20.94710 103.23261     2.820480 3.78884125
## High.45-64 84.3273732     18.55810  95.30558     1.720665 2.16689926
## Low.65+     0.6665534     20.23316 115.86821     3.298001 1.64098556
## Mid.65+    92.4307102     19.48102  97.30883     2.708724 3.56503931
## High.65+   92.2255449     24.40348 102.33985     3.040342 3.48554822

Mean and error of plasma vitamin D levels (ng / mL)

Generar tabla de medias, se, n e IC95%

res0 <- svyby(~outcome, ~exposure, survey_design, svymean, 
              vartype=c("se","ci"), deff=TRUE)
res0$exposure_ <- "Educ level"

res0b <- survey_design %>%
  group_by(person,exposure) %>%
  summarize(n = unweighted(n()))
res0 <- full_join(res0,res0b)
res0$outcome_ <- "Mean"

res_0 <- svyby(~outcome, ~person, survey_design, svymean, 
               vartype=c("se","ci"), deff=TRUE)
res_0$exposure_ <- "All"
res_0b <- survey_design %>%
  group_by(person) %>%
  summarize(n = unweighted(n()))
res_0 <- full_join(res_0,res_0b)
res_0$outcome_ <- "Mean"
res_0 <- full_join(res_0,res_0b)
res0 <- full_join(res_0,res0)

Mean and error of plasma vitamin D levels (ng / mL)

res0
##   person  outcome        se     ci_l     ci_u DEff.outcome  exposure_    n
## 1      1 19.83054 0.2970905 19.24825 20.41283     3.753644        All 2845
## 2      1 19.81829 0.6100450 18.62262 21.01395     4.090887 Educ level  847
## 3      1 19.78779 0.3667968 19.06888 20.50670     3.235335 Educ level 1389
## 4      1 19.92301 0.6942942 18.56222 21.28380     3.827317 Educ level  609
##   outcome_ exposure
## 1     Mean     <NA>
## 2     Mean      Low
## 3     Mean      Mid
## 4     Mean     High

Mean and error of plasma vitamin D levels (ng / mL) in subpop

survey_design <- subset(survey_design,Edad<50 & Sexo==2)
# Déficit Vit D (M1 y ADM)  2499 
# Criterio de impresión de etiqueta: ( (ser mujer) & (edad>=15 & edad<=49) ) Ó (edad>=65) 
# Criterio de aplicación de Examen: ( (ser mujer) & (edad>=15 & edad<=49) ) Ó (edad>=65) 
res0 <- svyby(~outcome, ~person+exposure, survey_design, svymean, 
              vartype=c("se","ci"), deff=TRUE)
res0$exposure_ <- "Age"
res0b <- survey_design %>%
  group_by(person,exposure) %>%
  summarize(n = unweighted(n()))
res0 <- full_join(res0,res0b)
res0$outcome_ <- "Mean"
res_0 <- svyby(~outcome, ~person, survey_design, svymean, 
               vartype=c("se","ci"), deff=TRUE)
res_0$exposure <- "All"
res_0b <- survey_design %>%
  group_by(person) %>%
  summarize(n = unweighted(n()))
res_0 <- full_join(res_0,res_0b)
res_0$outcome_ <- "Mean"
res_0 <- full_join(res_0,res_0b)
res0 <- full_join(res_0,res0)

Mean and error of plasma vitamin D levels (ng / mL) in subpop

res0
##   person  outcome        se     ci_l     ci_u DEff.outcome exposure    n
## 1      1 20.07805 0.3682582 19.35628 20.79982     3.400738      All 1583
## 2      1 22.68074 1.4281898 19.88154 25.47994     3.958762      Low  113
## 3      1 20.06069 0.4159796 19.24538 20.87599     3.032236      Mid  976
## 4      1 19.60074 0.7852376 18.06170 21.13978     3.954881     High  494
##   outcome_ exposure_
## 1     Mean      <NA>
## 2     Mean       Age
## 3     Mean       Age
## 4     Mean       Age

Plot

# names(res0)
ggplot(res0)+
  geom_point(aes(x=exposure, y=outcome))

Plot

ggplot(res0)+
  geom_point(aes(x=exposure, y=outcome, col=exposure))+
  geom_errorbar(aes(x=exposure, y=outcome,
                    ymin = ci_l, ymax = ci_u),
                 width = 0.1,
                 size = 0.1,
                 position = position_dodge(0.9)
               )

plot_1<-ggplot(res0)+
  geom_point(aes(x=exposure, y=outcome, col=exposure))+
  geom_text(aes(x=exposure, y=outcome, label=round(outcome,1)))+
  geom_errorbar(aes(x=exposure, y=outcome,
                    ymin = ci_l, ymax = ci_u),
                width = 0.1,
                size = 0.1,
                position = position_dodge(0.9)
  )+
  ylab("Media VitD ng/dL")+
  xlab(paste("\n ","\n ","Educational level",sep=""))+
  labs(col = " ")+
  labs(shape = " ")+
  theme(plot.title = element_text(size=22),
        plot.caption = element_text(size=22),
        legend.position = "none",
        legend.text =  element_text(size = 12, 
                                    colour = "black", 
                                    angle = 0),
        strip.text.x = element_text(size = 12, 
                                    colour = "black", 
                                    angle = 0),
        axis.ticks = element_blank(),
        axis.text.x = element_text(size = 12, 
                                   colour = "black", 
                                   angle = 0, 
                                   hjust = 1),
        axis.text.y = element_text(size = 12, 
                                   colour = "black", 
                                   angle = 0, 
                                   hjust = 1),
        axis.title = element_text(size = 12, 
                                  colour = "black", 
                                  angle = 0),
        axis.line = element_line(colour = "grey40", 
                                 size = 1, 
                                 linetype = "solid"),
        panel.grid.minor.y = element_line(colour="grey90", size=0.1),
        panel.background = element_rect(fill="white"))

plot_1

PROPORTION

svyby(~outcome1, ~person, survey_design, svymean, vartype=c(“se”,“ci”), deff=TRUE, proportion = TRUE, prop_method = c(“logit”))

svyby(~outcome1, ~exposure+Gender, survey_design, svymean, vartype=c(“se”,“ci”), deff=TRUE, proportion = TRUE, prop_method = c(“logit”))

svyby(~outcome1+outcome2, ~exposure+Gender, survey_design, svymean, vartype=c(“se”,“ci”), deff=TRUE, proportion = TRUE, prop_method = c(“logit”))

svyby(~outcome5_, ~exposure+Gender, survey_design, svymean, vartype=c(“se”,“ci”), deff=TRUE, proportion = TRUE, prop_method = c(“logit”))

svyby(~outcome1, ~exposure, survey_design, svytotal, vartype=c(“se”,“ci”), deff=TRUE, proportion = TRUE, prop_method = c(“logit”))

svyby(~outcome1, ~exposure+Gender, survey_design, svytotal, vartype=c(“se”,“ci”), deff=TRUE, proportion = TRUE, prop_method = c(“logit”))

Prevalence of vitamin D <30, <20 and <12

svyby(~outcome1, ~exposure, survey_design, svytotal)
##      exposure  outcome1        se
## Low       Low  233965.3  44582.15
## Mid       Mid 2388145.6 158276.27
## High     High 1260101.0 117312.75
svyby(~outcome1, ~exposure, survey_design, svymean)
##      exposure  outcome1         se
## Low       Low 0.8512115 0.07232704
## Mid       Mid 0.8890693 0.01539621
## High     High 0.8993810 0.02483398

Prevalence of vitamin D <30, <20 and <12

res1  <- svyby(~outcome1, ~person+exposure, survey_design, svymean, 
               vartype=c("se","ci"), deff=TRUE, proportion = TRUE,  
               prop_method = c("logit"))
res1$exposure_ <- "Educational levels"
res1b <- survey_design %>%
  group_by(person,exposure) %>%
  summarize(n = unweighted(n()))
res1 <- full_join(res1,res1b)
res1$outcome1_ <- "Mean"
res_1 <- svyby(~outcome1, ~person, survey_design, svymean, 
               vartype=c("se","ci"), deff=TRUE, proportion = TRUE,  
               prop_method = c("logit"))
res_1$exposure <- "All"
res_1b <- survey_design %>%
  group_by(person) %>%
  summarize(n = unweighted(n()))
res_1 <- full_join(res_1,res_1b)
res_1$outcome1_ <- "Mean"
res_1 <- full_join(res_1,res_1b)
res1 <- full_join(res_1,res1)
# res1$value <- res1$outcome1
# res1$value <- res1$outcome1*111



res2 <- svyby(~outcome2, ~person+exposure, survey_design, svymean, 
              vartype=c("se","ci"), deff=TRUE, proportion = TRUE,  
              prop_method = c("logit"))
res2$exposure_ <- "Educational levels"
res2b <- survey_design %>%
  group_by(person,exposure) %>%
  summarize(n = unweighted(n()))
res2 <- full_join(res2,res2b)
res2$outcome_ <- "Mean"
res_2 <- svyby(~outcome2, ~person, survey_design, svymean, 
               vartype=c("se","ci"), deff=TRUE, proportion = TRUE,  
               prop_method = c("logit"))
res_2$exposure <- "All"
res_2b <- survey_design %>%
  group_by(person) %>%
  summarize(n = unweighted(n()))
res_2 <- full_join(res_2,res_2b)
res_2$outcome_ <- "Mean"
res_2 <- full_join(res_2,res_2b)
res2 <- full_join(res_2,res2)
# res2$value <- res2$outcome
# res2$value <- res2$outcome*100



res3 <- svyby(~outcome3, ~person+exposure, survey_design, svymean, 
              vartype=c("se","ci"), deff=TRUE, proportion = TRUE,  
              prop_method = c("logit"))
res3$exposure_ <- "Educational level"
res3b <- survey_design %>%
  group_by(person,exposure) %>%
  summarize(n = unweighted(n()))
res3 <- full_join(res3,res3b)
res3$outcome_ <- "Mean"
res_3 <- svyby(~outcome3, ~person, survey_design, svymean, 
               vartype=c("se","ci"), deff=TRUE, proportion = TRUE,  
               prop_method = c("logit"))
res_3$exposure <- "All"
res_3b <- survey_design %>%
  group_by(person) %>%
  summarize(n = unweighted(n()))
res_3 <- full_join(res_3,res_3b)
res_3$outcome_ <- "Mean"
res_3 <- full_join(res_3,res_3b)
res3 <- full_join(res_3,res3)
# res3$value <- res3$outcome
# res3$value <- res3$outcome*100

Prevalence of vitamin D <30

res1
##   person  outcome1         se      ci_l      ci_u DEff.outcome1 exposure    n
## 1      1 0.8899959 0.01326272 0.8640014 0.9159903      2.843363      All 1583
## 2      1 0.8512115 0.07232704 0.7094531 0.9929699      4.627977      Low  113
## 3      1 0.8890693 0.01539621 0.8588932 0.9192453      2.344242      Mid  976
## 4      1 0.8993810 0.02483398 0.8507073 0.9480547      3.361003     High  494
##   outcome1_          exposure_
## 1      Mean               <NA>
## 2      Mean Educational levels
## 3      Mean Educational levels
## 4      Mean Educational levels

Prevalence of vitamin D <20

res2
##   person  outcome2         se      ci_l      ci_u DEff.outcome2 exposure    n
## 1      1 0.5222102 0.02394133 0.4752860 0.5691343      3.635619      All 1583
## 2      1 0.3645948 0.07862445 0.2104938 0.5186959      2.989858      Low  113
## 3      1 0.5030312 0.03049085 0.4432702 0.5627922      3.627249      Mid  976
## 4      1 0.5899006 0.03967783 0.5121334 0.6676677      3.209430     High  494
##   outcome_          exposure_
## 1     Mean               <NA>
## 2     Mean Educational levels
## 3     Mean Educational levels
## 4     Mean Educational levels

Prevalence of vitamin D <12

res3
##   person   outcome3         se       ci_l      ci_u DEff.outcome3 exposure    n
## 1      1 0.16007292 0.01613137 0.12845602 0.1916898      3.063000      All 1583
## 2      1 0.06202279 0.02318446 0.01658208 0.1074635      1.035254      Low  113
## 3      1 0.15350749 0.02081670 0.11270751 0.1943075      3.252621      Mid  976
## 4      1 0.19189542 0.03003225 0.13303330 0.2507575      2.868425     High  494
##   outcome_         exposure_
## 1     Mean              <NA>
## 2     Mean Educational level
## 3     Mean Educational level
## 4     Mean Educational level
names(res1)<-gsub("come1","come",names(res1))
names(res2)<-gsub("come2","come",names(res2))
names(res3)<-gsub("come3","come",names(res2))
res1$type<-"oucome1"
res2$type<-"oucome2"
res3$type<-"oucome3"

res0 <- rbind(res1,res2,res3)
res0
##    person    outcome         se       ci_l      ci_u DEff.outcome exposure    n
## 1       1 0.88999586 0.01326272 0.86400141 0.9159903     2.843363      All 1583
## 2       1 0.85121154 0.07232704 0.70945314 0.9929699     4.627977      Low  113
## 3       1 0.88906925 0.01539621 0.85889324 0.9192453     2.344242      Mid  976
## 4       1 0.89938101 0.02483398 0.85070731 0.9480547     3.361003     High  494
## 5       1 0.52221016 0.02394133 0.47528602 0.5691343     3.635619      All 1583
## 6       1 0.36459484 0.07862445 0.21049376 0.5186959     2.989858      Low  113
## 7       1 0.50303119 0.03049085 0.44327022 0.5627922     3.627249      Mid  976
## 8       1 0.58990056 0.03967783 0.51213344 0.6676677     3.209430     High  494
## 9       1 0.16007292 0.01613137 0.12845602 0.1916898     3.063000      All 1583
## 10      1 0.06202279 0.02318446 0.01658208 0.1074635     1.035254      Low  113
## 11      1 0.15350749 0.02081670 0.11270751 0.1943075     3.252621      Mid  976
## 12      1 0.19189542 0.03003225 0.13303330 0.2507575     2.868425     High  494
##    outcome_          exposure_    type
## 1      Mean               <NA> oucome1
## 2      Mean Educational levels oucome1
## 3      Mean Educational levels oucome1
## 4      Mean Educational levels oucome1
## 5      Mean               <NA> oucome2
## 6      Mean Educational levels oucome2
## 7      Mean Educational levels oucome2
## 8      Mean Educational levels oucome2
## 9      Mean               <NA> oucome3
## 10     Mean  Educational level oucome3
## 11     Mean  Educational level oucome3
## 12     Mean  Educational level oucome3

Basic Plot

plot2<-ggplot(res0)+
  geom_point(aes(x=exposure, y=outcome, col=type))+
  geom_errorbar(aes(x=exposure, y=outcome,
                    ymin = ci_l, ymax = ci_u),
                 width = 0.1,size = 0.1, position = position_dodge(0.9))
plot2

You can change the labels, the scale into %, background color, etc.

plot2<-plot2 +
  facet_wrap(type~ . , scales = "free_y", nrow = 3)
plot2

# https://ggplot2.tidyverse.org/reference/labs.html
# https://www.r-graph-gallery.com/275-add-text-labels-with-ggplot2.html

Vitamin D Ranges

  • 0: Vit D >=30
  • 1: Vit D 20-29.9
  • 2: Vit D 12-19.9
  • 3: Vit D <12

Vitamin D Ranges

svyby(~outcome5_, ~person+exposure, survey_design, svytotal)
##        person exposure outcome5_0 outcome5_1 outcome5_2 outcome5_3
## 1.Low       1      Low   40896.22   133752.2   83165.41   17047.68
## 1.Mid       1      Mid  297973.16  1036944.1  938862.16  412339.35
## 1.High      1     High  140974.84   433605.6  557635.39  268860.05
##        se.outcome5_0 se.outcome5_1 se.outcome5_2 se.outcome5_3
## 1.Low       22076.61      30697.49      28029.96      5876.561
## 1.Mid       45819.89     107061.77      92261.07     59179.669
## 1.High      37482.16      65943.28      65035.51     48851.604
res0a <- svyby(~outcome5_, ~person+exposure, survey_design, svytotal, 
               vartype=c("se","ci"), deff=TRUE, proportion = TRUE,  
               prop_method = c("logit"))
res0a
##        person exposure outcome5_0 outcome5_1 outcome5_2 outcome5_3
## 1.Low       1      Low   40896.22   133752.2   83165.41   17047.68
## 1.Mid       1      Mid  297973.16  1036944.1  938862.16  412339.35
## 1.High      1     High  140974.84   433605.6  557635.39  268860.05
##        se.outcome5_0 se.outcome5_1 se.outcome5_2 se.outcome5_3 ci_l.outcome5_0
## 1.Low       22076.61      30697.49      28029.96      5876.561       -2373.128
## 1.Mid       45819.89     107061.77      92261.07     59179.669      208167.819
## 1.High      37482.16      65943.28      65035.51     48851.604       67511.154
##        ci_l.outcome5_1 ci_l.outcome5_2 ci_l.outcome5_3 ci_u.outcome5_0
## 1.Low         73586.23        28227.71        5529.828        84165.57
## 1.Mid        827106.88       758033.79      296349.332       387778.49
## 1.High       304359.13       430168.13      173112.663       214438.54
##        ci_u.outcome5_1 ci_u.outcome5_2 ci_u.outcome5_3 DEff.outcome5_0
## 1.Low         193918.2        138103.1        28565.53        5.707246
## 1.Mid        1246781.3       1119690.5       528329.37        2.877615
## 1.High        562852.1        685102.7       364607.43        3.900339
##        DEff.outcome5_1 DEff.outcome5_2 DEff.outcome5_3
## 1.Low         5.594298        5.521860       0.8803831
## 1.Mid         6.537463        5.061053       3.6433794
## 1.High        5.112210        4.435029       3.8663595
write.csv(res0a,file="check.csv")

Vitamin D Ranges

res0 <- svyby(~outcome5_, ~person+exposure, survey_design, svymean, 
              vartype=c("se","ci"), deff=TRUE, proportion = TRUE,  
              prop_method = c("logit"))
res0
##        person exposure outcome5_0 outcome5_1 outcome5_2 outcome5_3
## 1.Low       1      Low  0.1487885  0.4866167  0.3025721 0.06202279
## 1.Mid       1      Mid  0.1109307  0.3860381  0.3495237 0.15350749
## 1.High      1     High  0.1006190  0.3094805  0.3980051 0.19189542
##        se.outcome5_0 se.outcome5_1 se.outcome5_2 se.outcome5_3 ci_l.outcome5_0
## 1.Low     0.07232704    0.08151790    0.07835190    0.02318446     0.007030071
## 1.Mid     0.01539621    0.02790336    0.02756623    0.02081670     0.080754731
## 1.High    0.02483398    0.03453090    0.03593478    0.03003225     0.051945294
##        ci_l.outcome5_1 ci_l.outcome5_2 ci_l.outcome5_3 ci_u.outcome5_0
## 1.Low        0.3268446       0.1490052      0.01658208       0.2905469
## 1.Mid        0.3313485       0.2954949      0.11270751       0.1411068
## 1.High       0.2418011       0.3275743      0.13303330       0.1492927
##        ci_u.outcome5_1 ci_u.outcome5_2 ci_u.outcome5_3 DEff.outcome5_0
## 1.Low        0.6463888       0.4561390       0.1074635        4.627977
## 1.Mid        0.4407276       0.4035525       0.1943075        2.344242
## 1.High       0.3771598       0.4684360       0.2507575        3.361003
##        DEff.outcome5_1 DEff.outcome5_2 DEff.outcome5_3
## 1.Low         2.980395        3.259624        1.035254
## 1.Mid         3.204083        3.259937        3.252621
## 1.High        2.751736        2.657961        2.868425

Vitamin D Ranges

res0 <- svyby(~outcome5_, ~person+exposure, survey_design, svymean, 
              vartype=c("se","ci"), deff=TRUE, proportion = TRUE,  
              prop_method = c("logit"))
res0$exposure_ <- "Educational level"
res0b <- survey_design %>%
  group_by(person,exposure) %>%
  summarize(n = unweighted(n()))
res0 <- full_join(res0,res0b)
res0$outcome5__ <- "Mean"
res_0 <- svyby(~outcome5_, ~person, survey_design, svymean, 
               vartype=c("se","ci"), deff=TRUE, proportion = TRUE,  
               prop_method = c("logit"))
res_0$exposure <- "All"
res_0b <- survey_design %>%
  group_by(person) %>%
  summarize(n = unweighted(n()))
res_0 <- full_join(res_0,res_0b)
res_0$outcome5__ <- "Mean"
res_0 <- full_join(res_0,res_0b)
res0 <- full_join(res_0,res0)
res0$person <- NULL
res0$outcome5__ <- NULL
#Guardo data con unweighted n
res0
##   outcome5_0 outcome5_1 outcome5_2 outcome5_3 se.outcome5_0 se.outcome5_1
## 1  0.1100041  0.3677857  0.3621372 0.16007292    0.01326272    0.02208735
## 2  0.1487885  0.4866167  0.3025721 0.06202279    0.07232704    0.08151790
## 3  0.1109307  0.3860381  0.3495237 0.15350749    0.01539621    0.02790336
## 4  0.1006190  0.3094805  0.3980051 0.19189542    0.02483398    0.03453090
##   se.outcome5_2 se.outcome5_3 ci_l.outcome5_0 ci_l.outcome5_1 ci_l.outcome5_2
## 1    0.02194469    0.01613137     0.084009684       0.3244953       0.3191264
## 2    0.07835190    0.02318446     0.007030071       0.3268446       0.1490052
## 3    0.02756623    0.02081670     0.080754731       0.3313485       0.2954949
## 4    0.03593478    0.03003225     0.051945294       0.2418011       0.3275743
##   ci_l.outcome5_3 ci_u.outcome5_0 ci_u.outcome5_1 ci_u.outcome5_2
## 1      0.12845602       0.1359986       0.4110761       0.4051480
## 2      0.01658208       0.2905469       0.6463888       0.4561390
## 3      0.11270751       0.1411068       0.4407276       0.4035525
## 4      0.13303330       0.1492927       0.3771598       0.4684360
##   ci_u.outcome5_3 DEff.outcome5_0 DEff.outcome5_1 DEff.outcome5_2
## 1       0.1916898        2.843363        3.320414        3.299307
## 2       0.1074635        4.627977        2.980395        3.259624
## 3       0.1943075        2.344242        3.204083        3.259937
## 4       0.2507575        3.361003        2.751736        2.657961
##   DEff.outcome5_3 exposure    n         exposure_
## 1        3.063000      All 1583              <NA>
## 2        1.035254      Low  113 Educational level
## 3        3.252621      Mid  976 Educational level
## 4        2.868425     High  494 Educational level

CROSSTAB

tbl1 <- svytable(~exposure+Area, survey_design)
summary(tbl1, statistic="Chisq")
##         Area
## exposure   Urban   Rural
##     Low   215501   59360
##     Mid  2283632  402487
##     High 1363374   37702
## 
##  Pearson's X^2: Rao & Scott adjustment
## 
## data:  NextMethod()
## X-squared = 60.6, df = 2, p-value = 1.113e-10

Linear regression: svyglm gaussian

svyglm(outcome~ Area+Edad, survey_design, deff=TRUE)

Linear regression: svyglm gaussian: VitD & urbana/rural area ajusted by edad

Primero describo y luego modelo

#Describo
svyby(~outcome, ~Area, survey_design, svymean, vartype=c("se","ci"), deff=TRUE)
##        Area  outcome        se     ci_l     ci_u DEff.outcome
## Urban Urban 19.54797 0.4054021 18.75339 20.34254     3.553852
## Rural Rural 24.17664 0.6420721 22.91820 25.43508     1.849969

Linear regression: svyglm gaussian: VitD & urbana/rural area ajusted by edad

#Modelo
fit2 <- svyglm(outcome~ Area+Edad, survey_design, deff=TRUE)
summary(fit2)
## 
## Call:
## svyglm(formula = outcome ~ Area + Edad, design = survey_design, 
##     deff = TRUE)
## 
## Survey design:
## subset(survey_design, Edad < 50 & Sexo == 2)
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 19.75537    1.17661  16.790  < 2e-16 ***
## AreaRural    4.64072    0.76353   6.078 1.93e-09 ***
## Edad        -0.00660    0.03464  -0.191    0.849    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 60.97116)
## 
## Number of Fisher Scoring iterations: 2

Linear regression: svyglm gaussian: VitD & urbana/rural area ajusted by edad

fit2 <- svyglm(outcome~ Area+Edad, survey_design, deff=TRUE)
fit2 <- summary(fit2)
fit2 <- as.data.frame(coef(fit2))
fit2$name <- rownames(fit2)
fit2$outcome <- "Vit D numeric"

Linear regression: svyglm gaussian: VitD & urbana/rural area ajusted by edad

fit2
##                 Estimate Std. Error    t value     Pr(>|t|)        name
## (Intercept) 19.755369485  1.1766104 16.7900686 4.764151e-54 (Intercept)
## AreaRural    4.640717720  0.7635330  6.0779531 1.925020e-09   AreaRural
## Edad        -0.006599543  0.0346403 -0.1905163 8.489554e-01        Edad
##                   outcome
## (Intercept) Vit D numeric
## AreaRural   Vit D numeric
## Edad        Vit D numeric
write.csv(fit2,file="fit2.csv")

Logistic regression: svyglm logit

svyglm(outcome1 ~Area+Edad, survey_design, 
                    family = quasibinomial(link = "logit"), deff=TRUE)
## Stratified 1 - level Cluster Sampling design (with replacement)
## With (792) clusters.
## subset(survey_design, Edad < 50 & Sexo == 2)
## Sampling variables:
##  - ids: Conglomerado_
##  - strata: strata_
##  - weights: fexp
## 
## Call:  svyglm(formula = outcome1 ~ Area + Edad, design = survey_design, 
##     family = quasibinomial(link = "logit"), deff = TRUE)
## 
## Coefficients:
## (Intercept)    AreaRural         Edad  
##    2.292585    -0.995504    -0.001389  
## 
## Degrees of Freedom: 1582 Total (i.e. Null);  760 Residual
## Null Deviance:       1097 
## Residual Deviance: 1075  AIC: NA

Logistic regression: svyglm logit

fit2<-svyglm(outcome1 ~Area+Edad, survey_design, 
                    family = quasibinomial(link = "logit"), deff=TRUE)
summary(fit2)
## 
## Call:
## svyglm(formula = outcome1 ~ Area + Edad, design = survey_design, 
##     family = quasibinomial(link = "logit"), deff = TRUE)
## 
## Survey design:
## subset(survey_design, Edad < 50 & Sexo == 2)
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.292585   0.438579   5.227 2.22e-07 ***
## AreaRural   -0.995504   0.257065  -3.873 0.000117 ***
## Edad        -0.001389   0.013088  -0.106 0.915522    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasibinomial family taken to be 1.000615)
## 
## Number of Fisher Scoring iterations: 4

Logistic regression: VitD<30 and urbana/rural area ajusted by age

# res1a <- svyby(~outcome1, ~Area, survey_design, svymean, vartype=c("se","ci"), 
#                deff=TRUE)
fit_t <- svyglm(outcome1 ~Area+Edad, survey_design, 
                    family = quasibinomial(link = "logit"), deff=TRUE)
fit_t <- summary(fit_t)
fit_t <- as.data.frame(coef(fit_t))
fit_t$name <- rownames(fit_t)
fit_t$outcome <- "VitD<30"


fit_t$OR <- round(exp(fit_t$Estimate),digits=2)
fit_t$OR_i <- round(exp((fit_t$Estimate)-(1.96*fit_t$`Std. Error`)),2)
fit_t$OR_s <- round(exp((fit_t$Estimate)+(1.96*fit_t$`Std. Error`)),2)
fit_t$Estimate <- NULL
fit_t$`Std. Error` <- NULL
fit_t$`t value` <- NULL
fit_t$pvalue <- round(fit_t$`Pr(>|t|)`,digits=4)
fit_t$`Pr(>|t|)` <- NULL
fit_t <- subset(fit_t,name!="(Intercept)")
fit_t$name <- NULL

Logistic regression: VitD<30 and urbana/rural area ajusted by age

fit_t
##           outcome   OR OR_i OR_s pvalue
## AreaRural VitD<30 0.37 0.22 0.61 0.0001
## Edad      VitD<30 1.00 0.97 1.02 0.9155

Several logistic regressions

fit_t <- svyglm(outcome1 ~Area+Edad, survey_design, 
                    family = quasibinomial(link = "logit"), deff=TRUE)
fit_t <- summary(fit_t)
fit_t <- as.data.frame(coef(fit_t))
fit_t$name <- rownames(fit_t)
fit_t$outcome <- "VitD<30"
fit_t$OR <- round(exp(fit_t$Estimate),digits=2)
fit_t$OR_i <- round(exp((fit_t$Estimate)-(1.96*fit_t$`Std. Error`)),2)
fit_t$OR_s <- round(exp((fit_t$Estimate)+(1.96*fit_t$`Std. Error`)),2)
fit_t$Estimate <- NULL
fit_t$`Std. Error` <- NULL
fit_t$`t value` <- NULL
fit_t$pvalue <- round(fit_t$`Pr(>|t|)`,digits=4)
fit_t$`Pr(>|t|)` <- NULL
fit_t <- subset(fit_t,name!="(Intercept)")
fit_t$name <- NULL
fit_t1<-fit_t


fit_t <- svyglm(outcome2 ~Area+Edad, survey_design, 
                    family = quasibinomial(link = "logit"), deff=TRUE)
fit_t <- summary(fit_t)
fit_t <- as.data.frame(coef(fit_t))
fit_t$name <- rownames(fit_t)
fit_t$outcome <- "VitD<20"
fit_t$OR <- round(exp(fit_t$Estimate),digits=2)
fit_t$OR_i <- round(exp((fit_t$Estimate)-(1.96*fit_t$`Std. Error`)),2)
fit_t$OR_s <- round(exp((fit_t$Estimate)+(1.96*fit_t$`Std. Error`)),2)
fit_t$Estimate <- NULL
fit_t$`Std. Error` <- NULL
fit_t$`t value` <- NULL
fit_t$pvalue <- round(fit_t$`Pr(>|t|)`,digits=4)
fit_t$`Pr(>|t|)` <- NULL
fit_t <- subset(fit_t,name!="(Intercept)")
fit_t$name <- NULL
fit_t2<-fit_t



fit_t <- svyglm(outcome3 ~Area+Edad, survey_design, 
                    family = quasibinomial(link = "logit"), deff=TRUE)
fit_t <- summary(fit_t)
fit_t <- as.data.frame(coef(fit_t))
fit_t$name <- rownames(fit_t)
fit_t$outcome <- "VitD<12"
fit_t$OR <- round(exp(fit_t$Estimate),digits=2)
fit_t$OR_i <- round(exp((fit_t$Estimate)-(1.96*fit_t$`Std. Error`)),2)
fit_t$OR_s <- round(exp((fit_t$Estimate)+(1.96*fit_t$`Std. Error`)),2)
fit_t$Estimate <- NULL
fit_t$`Std. Error` <- NULL
fit_t$`t value` <- NULL
fit_t$pvalue <- round(fit_t$`Pr(>|t|)`,digits=4)
fit_t$`Pr(>|t|)` <- NULL
fit_t <- subset(fit_t,name!="(Intercept)")
fit_t$name <- NULL
fit_t3<-fit_t




fit_t <- svyglm(outcome1 ~Area+Educational_level, survey_design, 
                    family = quasibinomial(link = "logit"), deff=TRUE)
fit_t <- summary(fit_t)
fit_t <- as.data.frame(coef(fit_t))
fit_t$name <- rownames(fit_t)
fit_t$outcome <- "VitD<30"
fit_t$OR <- round(exp(fit_t$Estimate),digits=2)
fit_t$OR_i <- round(exp((fit_t$Estimate)-(1.96*fit_t$`Std. Error`)),2)
fit_t$OR_s <- round(exp((fit_t$Estimate)+(1.96*fit_t$`Std. Error`)),2)
fit_t$Estimate <- NULL
fit_t$`Std. Error` <- NULL
fit_t$`t value` <- NULL
fit_t$pvalue <- round(fit_t$`Pr(>|t|)`,digits=4)
fit_t$`Pr(>|t|)` <- NULL
fit_t <- subset(fit_t,name!="(Intercept)")
fit_t$name <- NULL
fit_t1b<-fit_t


fit_t <- svyglm(outcome2 ~Area+Educational_level, survey_design, 
                    family = quasibinomial(link = "logit"), deff=TRUE)
fit_t <- summary(fit_t)
fit_t <- as.data.frame(coef(fit_t))
fit_t$name <- rownames(fit_t)
fit_t$outcome <- "VitD<20"
fit_t$OR <- round(exp(fit_t$Estimate),digits=2)
fit_t$OR_i <- round(exp((fit_t$Estimate)-(1.96*fit_t$`Std. Error`)),2)
fit_t$OR_s <- round(exp((fit_t$Estimate)+(1.96*fit_t$`Std. Error`)),2)
fit_t$Estimate <- NULL
fit_t$`Std. Error` <- NULL
fit_t$`t value` <- NULL
fit_t$pvalue <- round(fit_t$`Pr(>|t|)`,digits=4)
fit_t$`Pr(>|t|)` <- NULL
fit_t <- subset(fit_t,name!="(Intercept)")
fit_t$name <- NULL
fit_t2b<-fit_t



fit_t <- svyglm(outcome3 ~Area+Educational_level, survey_design, 
                    family = quasibinomial(link = "logit"), deff=TRUE)
fit_t <- summary(fit_t)
fit_t <- as.data.frame(coef(fit_t))
fit_t$name <- rownames(fit_t)
fit_t$outcome <- "VitD<12"
fit_t$OR <- round(exp(fit_t$Estimate),digits=2)
fit_t$OR_i <- round(exp((fit_t$Estimate)-(1.96*fit_t$`Std. Error`)),2)
fit_t$OR_s <- round(exp((fit_t$Estimate)+(1.96*fit_t$`Std. Error`)),2)
fit_t$Estimate <- NULL
fit_t$`Std. Error` <- NULL
fit_t$`t value` <- NULL
fit_t$pvalue <- round(fit_t$`Pr(>|t|)`,digits=4)
fit_t$`Pr(>|t|)` <- NULL
fit_t <- subset(fit_t,name!="(Intercept)")
fit_t$name <- NULL
fit_t3b<-fit_t


fit_123a<-rbind(fit_t1,fit_t2,fit_t3,fit_t1b,fit_t2b,fit_t3b)

fit_123a$type <- fit_123a$outcome
fit_123a$outcome <- fit_123a$OR
fit_123a$ci_l <- fit_123a$OR_i
fit_123a$ci_u <- fit_123a$OR_s
fit_123a$exposure <- fit_123a$name


res0 <- fit_123a

res0$exposure<-rownames(res0)
res0$exposure<-gsub("\\d+","",res0$exposure)
res0$exposure_lbl<-res0$exposure
res0$exposure<-as.factor(res0$exposure)
# res0$exposure<-as.numeric(res0$exposure)
rownames(res0)<-NULL
res0
##    outcome   OR OR_i OR_s pvalue    type ci_l ci_u              exposure
## 1     0.37 0.37 0.22 0.61 0.0001 VitD<30 0.22 0.61             AreaRural
## 2     1.00 1.00 0.97 1.02 0.9155 VitD<30 0.97 1.02                  Edad
## 3     0.28 0.28 0.17 0.46 0.0000 VitD<20 0.17 0.46             AreaRural
## 4     1.00 1.00 0.99 1.02 0.8468 VitD<20 0.99 1.02                  Edad
## 5     0.29 0.29 0.11 0.73 0.0091 VitD<12 0.11 0.73             AreaRural
## 6     0.99 0.99 0.96 1.01 0.3696 VitD<12 0.96 1.01                  Edad
## 7     0.37 0.37 0.22 0.63 0.0003 VitD<30 0.22 0.63             AreaRural
## 8     1.30 1.30 0.38 4.42 0.6727 VitD<30 0.38 4.42  Educational_levelMid
## 9     1.23 1.23 0.32 4.76 0.7628 VitD<30 0.32 4.76 Educational_levelHigh
## 10    0.30 0.30 0.18 0.51 0.0000 VitD<20 0.18 0.51             AreaRural
## 11    1.68 1.68 0.81 3.48 0.1630 VitD<20 0.81 3.48  Educational_levelMid
## 12    2.08 2.08 0.97 4.48 0.0604 VitD<20 0.97 4.48 Educational_levelHigh
## 13    0.31 0.31 0.12 0.79 0.0148 VitD<12 0.12 0.79             AreaRural
## 14    2.62 2.62 1.10 6.22 0.0297 VitD<12 1.10 6.22  Educational_levelMid
## 15    3.10 3.10 1.27 7.52 0.0128 VitD<12 1.27 7.52 Educational_levelHigh
##             exposure_lbl
## 1              AreaRural
## 2                   Edad
## 3              AreaRural
## 4                   Edad
## 5              AreaRural
## 6                   Edad
## 7              AreaRural
## 8   Educational_levelMid
## 9  Educational_levelHigh
## 10             AreaRural
## 11  Educational_levelMid
## 12 Educational_levelHigh
## 13             AreaRural
## 14  Educational_levelMid
## 15 Educational_levelHigh

Several logistic regressions

res0
##    outcome   OR OR_i OR_s pvalue    type ci_l ci_u              exposure
## 1     0.37 0.37 0.22 0.61 0.0001 VitD<30 0.22 0.61             AreaRural
## 2     1.00 1.00 0.97 1.02 0.9155 VitD<30 0.97 1.02                  Edad
## 3     0.28 0.28 0.17 0.46 0.0000 VitD<20 0.17 0.46             AreaRural
## 4     1.00 1.00 0.99 1.02 0.8468 VitD<20 0.99 1.02                  Edad
## 5     0.29 0.29 0.11 0.73 0.0091 VitD<12 0.11 0.73             AreaRural
## 6     0.99 0.99 0.96 1.01 0.3696 VitD<12 0.96 1.01                  Edad
## 7     0.37 0.37 0.22 0.63 0.0003 VitD<30 0.22 0.63             AreaRural
## 8     1.30 1.30 0.38 4.42 0.6727 VitD<30 0.38 4.42  Educational_levelMid
## 9     1.23 1.23 0.32 4.76 0.7628 VitD<30 0.32 4.76 Educational_levelHigh
## 10    0.30 0.30 0.18 0.51 0.0000 VitD<20 0.18 0.51             AreaRural
## 11    1.68 1.68 0.81 3.48 0.1630 VitD<20 0.81 3.48  Educational_levelMid
## 12    2.08 2.08 0.97 4.48 0.0604 VitD<20 0.97 4.48 Educational_levelHigh
## 13    0.31 0.31 0.12 0.79 0.0148 VitD<12 0.12 0.79             AreaRural
## 14    2.62 2.62 1.10 6.22 0.0297 VitD<12 1.10 6.22  Educational_levelMid
## 15    3.10 3.10 1.27 7.52 0.0128 VitD<12 1.27 7.52 Educational_levelHigh
##             exposure_lbl
## 1              AreaRural
## 2                   Edad
## 3              AreaRural
## 4                   Edad
## 5              AreaRural
## 6                   Edad
## 7              AreaRural
## 8   Educational_levelMid
## 9  Educational_levelHigh
## 10             AreaRural
## 11  Educational_levelMid
## 12 Educational_levelHigh
## 13             AreaRural
## 14  Educational_levelMid
## 15 Educational_levelHigh

Plot

# res0$exposure<-as.factor(res0$exposure)
res0<-subset(res0,exposure!="AreaRural")
plot_1<-
  ggplot(res0)+
  geom_point(aes(x=exposure, y=outcome, col=exposure))+
  # geom_text(aes(x=exposure, y=outcome, label=round(outcome,1)))+
  geom_errorbar(aes(x=exposure, y=outcome,
                    ymin = ci_l, ymax = ci_u),
                width = 0.1,
                size = 0.1,
                position = position_dodge(0.9)
  )+
  geom_hline(yintercept =c(1),col=c("red"),size=0.1)+
  labs(title = "", 
       subtitle = "", 
       caption = "")+
  ylab("OR")+
  xlab("")+
  labs(col = " ")+
  labs(shape = " ")+
  theme(plot.title = element_text(size=22),
        plot.caption = element_text(size=22),
        legend.position = "right",
        legend.text =  element_text(size = 12, 
                                    colour = "black", 
                                    angle = 0),
        strip.text.x = element_text(size = 12, 
                                    colour = "black", 
                                    angle = 0),
        axis.ticks = element_blank(),
        axis.text.x = element_text(size = 6, 
                                   colour = "black", 
                                   angle = 40, 
                                   hjust = 1),
        axis.text.y = element_text(size = 12, 
                                   colour = "black", 
                                   angle = 0, 
                                   hjust = 1),
        axis.title = element_text(size = 12, 
                                  colour = "black", 
                                  angle = 0),
        axis.line = element_line(colour = "grey40", 
                                 size = 1, 
                                 linetype = "solid"),
        panel.grid.minor.y = element_line(colour="grey90", size=0.1),
        panel.background = element_rect(fill="white"))+
  # coord_flip()+
  # facet_wrap(type~ exposure, ncol=4, scales = "free")
  facet_wrap(.~ type,  scales = "fixed")
plot_1

Plot

plot_1

Survey desing variables for ENS2003

  • Factores de expansión:“fact_af1”,“fact_af2”,“fact_aex”,

  • Estrato:“region”

  • Conglomerado:“comuna”

Debes elegir un factor de exp. El factor de exp depende de tus variables de análisis.

# ENS_2003<- rio::import('~/MEGA/Bases/ENS_2003_Ministerio_de_Salud_Chile_Dpto_Epidemiología_VENT_region_comuna.sav')
# names(ENS_2003)[order(names(ENS_2003))]
# ENS_2003_ext<-ENS_2003[c("folio","fact_af1","fact_af2","fact_aex","region",
#                          "zona","comuna")]
# df2003<-subset(ENS_2003,!is.na(fact_af2)& !is.na(region)& !is.na(comuna))
# survey_design <- df2003 %>%
#   as_survey_design(id=comuna, weight = fact_af2, strata=region, nest=TRUE)
# options(survey.lonely.psu="certainty")
# survey_design<-subset(survey_design,!is.na(depresion1) &!is.na(sexo) &edad>=15)
# res0<-svyby(~depresion1, ~sexo+zona, survey_design, svymean,
#             vartype=c("se","ci"), method="beta")
# res0

Survey desing variables for ENS2010

  • Factores de expansión:“factor”,“FEXP1”,“FEXP2”,“FEXP_ex”,“FEXP_factor”

  • Estrato:“estrato”

  • Conglomerado:“id_Comuna”,“conglomerado1”,“conglomerado2”

Debes elegir un factor de exp y un conglomerado. El factor de exp depende de tus variables de análisis. Para el conglomerado… los tres se pueden justificar. Yo utilizo el que te entrega el mayor error de estimación (el mas “conservador”).

# ENS_2010<- rio::import('~/MEGA/Bases/ENCUESTA NACIONAL DE SALUD-ENS 2009-2010, DEPTO.EPIDEMIOLOGIA, MINSAL__comuna (04.2019).sav')
# names(ENS_2010)[order(names(ENS_2010))]
# ENS_2010_ext<-ENS_2010[c("ID","factor","FEXP1","FEXP2","FEXP_ex","FEXP_factor",
#                          "REGION","ZONA","id_Comuna","estrato","conglomerado1",
#                          "conglomerado2")]
# df2010<-subset(ENS_2010,!is.na(FEXP2)& !is.na(estrato)& !is.na(id_Comuna))
# survey_design <- df2010 %>%
#   as_survey_design(id=id_Comuna, weight = FEXP2, strata=estrato, nest=TRUE)
# options(survey.lonely.psu="certainty")
# survey_design<-subset(survey_design,!is.na(Depresion_1)&!is.na(SEXO)&EDAD>=15)
# res0<-svyby(~Depresion_1, ~SEXO+ZONA, survey_design, svymean,
#             vartype=c("se","ci"), method="beta")
# res0

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Tarea individual n°1 - Curso MSP 3010, 2020

Objetivo de la tarea:

Adquirir habilidades para realizar análisis de variables incluidas en las bases de datos de la Encuesta Nacional de Salud chilena, utilizando un paquete estadístico para “Muestras Complejas”.

Instrucciones:
  1. Revise previamente los cuestionarios de cualquier base de datos ENS disponible al público en Chile.
  2. Elija un tema de su interés y construya un indicador específico que pueda ser dicotomizado (0-1).
  1. Calcule una tasa de prevalencia nacional y según sexo para ese parámetro.

Tarea individual n°1 - Curso MSP 3010, 2020

  1. Calcule las tasas de prevalencia según nivel educacional (bajo, medio, alto).
  2. Estime los Odds Ratio para una comparación entre la prevalencia del nivel educacional bajo vs. Alto ajustado por edad y sexo (utilizando regresión logística en el módulo de muestras complejas).
  3. Ensaye realizar los mismos cálculos del punto 3, 4 y 5 fuera del módulo de muestras complejas. ¿Nota algún cambio? ¿De qué magnitud? ¿Por qué ocurre esto? Discuta con sus compañeros.

Tarea individual n°1 - Curso MSP 3010, 2020

  1. Entregar vía mail 2 archivos:

Archivo 1. Un archivo con 3 tablas (ejemplo):

  1. “Prevalencia de (consumo de 5 o más frutas o verduras al día) según sexo, Chile ENS 20XX”
  2. “Prevalencia de … según nivel educacional, Chile ENS …”
  3. “Odds ratio ajustado por edad y sexo para la comparación de prevalencias de … entre el nivel educacional bajo y Alto”

Archivo 2. Un archivo con la sintaxis utilizada para realizar estos análisis.

Tarea individual n°1 - Curso MSP 3010, 2020

Para realizar este trabajo contará con el siguiente apoyo:

  1. PDF de Clases sobre las bases de datos ENS y sobre la teoría estadística detrás del muestreo complejo.
  2. Lectura y apuntes sobre el uso del módulo de muestras complejas utilizando STATA o R.
  3. Talleres de STATA o R.

Fecha de entrega: miércoles 28 de octubre (hasta las 23.59), enviar archivos rotulados como 1_su nombre.xls y 2_su nombre.doc a Francisco Valenzuela: fjvalen2@uc.cl