Taller 2020 MSP3010: Muestras complejas y uso de bases de datos Encuesta Nacional de Salud (ENS) Chile en R
Alvaro Passi-Solar
24/09/2020
Contenidos
Recursos y requerimientos
Instalar y abrir librerÃa
Importar y explorar base de datos
Explorar variables del diseño muestral
Crear diseño muestral
Descriptivos
Regresión lineal
Regresión logÃstica
Plots
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 estadÃstico para “Muestras Complejas”.
Instrucciones:- Revise previamente los cuestionarios de cualquier base de datos ENS disponible al público en Chile.
- Elija un tema de su interés y construya un indicador especÃfico que pueda ser dicotomizado (0-1).
- Calcule una tasa de prevalencia nacional y según sexo para ese parámetro.
Tarea individual n°1 - Curso MSP 3010, 2020
- Calcule las tasas de prevalencia según nivel educacional (bajo, medio, alto).
- Estime los Odds Ratio para una comparación entre la prevalencia del nivel educacional bajo vs. Alto ajustado por edad y sexo (utilizando regresión logÃstica en el módulo de muestras complejas).
- 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
- Entregar vÃa mail 2 archivos:
Archivo 1. Un archivo con 3 tablas (ejemplo):
- “Prevalencia de (consumo de 5 o más frutas o verduras al dÃa) según sexo, Chile ENS 20XX”
- “Prevalencia de … según nivel educacional, Chile ENS …”
- “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:
- PDF de Clases sobre las bases de datos ENS y sobre la teorÃa estadÃstica detrás del muestreo complejo.
- Lectura y apuntes sobre el uso del módulo de muestras complejas utilizando STATA o R.
- 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
Recursos y requerimientos
Bases de datos
http://epi.minsal.cl/condiciones-de-uso/ http://epi.minsal.cl/bases-de-datos/
Bases de datos
– ENS 2003 (SPSS)
– Base de datos ENS 2003 – Región y comuna (SPSS)
– ENS 2009-2010
- SPSS, STATA
(a.1) ENS 2009-2010 Comuna (SPSS)
- Base de medicamentos
– ENS 2016-17
- 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)
Base Formulario 4
Manual de uso (Actualización 14/02/2018)
Libro de códigos F1, F2 , F3 y F4
Base de Medicamentos
Base formuarios y libro de codigos F3
Bases de datos
Base Minsal ENS2017 en formato .sav solo se puede abrir en SPSS.
Por alguna razón library(rio) no logra importar este archivo.sav
Descarga ENS2017 en formato .dta (Stata) en la web del curso https://cursos.canvas.uc.cl/courses/22528 Esta base importaremos a R
Cuestionarios y manuales
Opción 1: RStudio cloud
https://rstudio.cloud/project/1676149
No requiere instalar R. Trae la base ENS2017
Opción 2: R y RStudio instalados
Ideal si tienes cuenta en Rpubs
Paquetes/librerias R requeridos para el taller
- library(dplyr) #dplyr: a grammar of data manipulation#
- library(rio) #To import and export data#
- library(survey) #to use survey design
- library(srvyr) #survey design with grammar
Instalar librerias
RStudio te ofrece installar los paquetes por ti. Guarda el archivo. No demora en aparecer un mensaje en amarillo ofreciendo instalar 
Instalar librerias
# install.packages("dplyr")
Puedes activar el código borrando el # antes del texto
Puedes cambiar el “nombre del paquete” a instalar
Comillas en install.packages() son importantes
Cargar librerias
library(rio) #To import and export data# library(reshape2) #Modifiy data set shape design with grammar library(dplyr) #dplyr: a grammar of data manipulation#
## ## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats': ## ## filter, lag
## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union
library(ggplot2) #plot your results library(survey) #Analise using survey design
## Loading required package: grid
## Loading required package: Matrix
## Loading required package: survival
## ## Attaching package: 'survey'
## The following object is masked from 'package:graphics': ## ## dotchart
library(srvyr) #Analise using survey design with grammar
## ## Attaching package: 'srvyr'
## The following object is masked from 'package:stats': ## ## filter
Knitr (ctrl+shift+K)
Knit: Do you see that blue button with knitting sticks? Press it
Importar y explorar base de datos
Importar base de datos
- Dejar todos los archivos en la misma carpeta de trabajo (working directory: wd)
# 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 librerÃa “rio” o para asegurar que usas la función “import” del paquete “rio” y no de “otro paquete”.
Explorar base de datos
# View(df0) # WARNING View() de bases gigante # ENS no es gigante # nombres de variables / columnas # names(df0) # total columnas length(names(df0))
## [1] 1160
# total filas length(df0$IdEncuesta)
## [1] 6233
# options: # nrow(df0) # ncol(df0) # dim(df0)
Explorar base de datos
# A veces es mejor generar un elemento ("namesdf0")
namesdf0 <- names(df0)
# 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"
Explorar base de datos
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
Explorar base de datos
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"
Extraer y describir variables seleccionadas
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
Extraer y describir variables seleccionadas
# # Hmisc Trae conflictos con paquetes library(dplyr) y library(srvyr) library(summarytools)
## Registered S3 method overwritten by 'pryr': ## method from ## print.bytes Rcpp
## Warning in fun(libname, pkgname): couldn't connect to display ":0"
## system might not have X11 capabilities; in case of errors when using dfSummary(), set st_options(use.x11 = FALSE)
## For best results, restart R session and update pander using devtools:: or remotes::install_github('rapporter/pander')
https://dabblingwithdata.wordpress.com/2018/01/02/my-favourite-r-package-for-summarising-data/
Extraer y describir variables seleccionadas
mydata <- descr(df0_ext) mydata
## Non-numerical variable(s) ignored: FechaInicioF1
## Descriptive Statistics ## df0_ext$IdEncuesta ## Label: IdEncuesta ## N: 6233 ## ## Conglomerado Edad Edad_Codificada Estrato Fexp_EX1p_Corr ## ----------------- -------------- --------- ----------------- --------- ---------------- ## Mean 8503691.47 48.91 2.68 84.27 2666.48 ## Std.Dev 4187166.26 19.32 0.99 42.10 3928.00 ## Min 1101101.00 15.00 1.00 11.00 3.16 ## Q1 5109123.00 33.00 2.00 51.00 464.49 ## Median 8301107.00 50.00 3.00 81.00 1232.53 ## Q3 13101108.00 64.00 3.00 131.00 3071.31 ## Max 15101204.00 98.00 4.00 152.00 24000.00 ## MAD 5633900.76 23.72 1.48 59.30 1412.23 ## IQR 7991985.00 31.00 1.00 80.00 2606.82 ## CV 0.49 0.39 0.37 0.50 1.47 ## Skewness -0.10 0.05 -0.18 -0.08 3.07 ## SE.Skewness 0.03 0.03 0.03 0.03 0.03 ## Kurtosis -1.16 -0.98 -1.01 -1.19 11.04 ## N.Valid 6233.00 6233.00 6233.00 6233.00 5445.00 ## Pct.Valid 100.00 100.00 100.00 100.00 87.36 ## ## Table: Table continues below ## ## ## ## Fexp_EX2p_Corr Fexp_EX3p_Corr Fexp_F1F2EX1p_Corr Fexp_F1F2EX2p_Corr ## ----------------- ---------------- ---------------- -------------------- -------------------- ## Mean 3765.63 10532.52 2674.83 3774.10 ## Std.Dev 5411.16 14973.73 3933.49 5419.26 ## Min 4.32 10.27 3.16 4.34 ## Q1 726.04 1808.43 465.53 728.06 ## Median 1781.00 4823.59 1241.02 1791.02 ## Q3 4342.94 13031.06 3072.37 4351.14 ## Max 34000.00 98000.00 24000.00 34000.00 ## MAD 1991.71 5490.85 1424.99 1995.94 ## IQR 3615.35 11149.53 2606.36 3621.10 ## CV 1.44 1.42 1.47 1.44 ## Skewness 3.02 2.87 3.06 3.02 ## SE.Skewness 0.04 0.07 0.03 0.04 ## Kurtosis 10.85 10.18 10.99 10.81 ## N.Valid 3847.00 1376.00 5428.00 3847.00 ## Pct.Valid 61.72 22.08 87.08 61.72 ## ## Table: Table continues below ## ## ## ## Fexp_F1F2EX3p_Corr Fexp_F1F2p_Corr Fexp_F1p_Corr Fexp_F2p_Corr ## ----------------- -------------------- ----------------- --------------- --------------- ## Mean 10551.58 2630.25 2329.37 2630.25 ## Std.Dev 14988.75 3888.86 3526.21 3888.86 ## Min 10.41 3.13 2.59 3.13 ## Q1 1821.15 454.58 414.82 454.58 ## Median 4832.53 1209.35 1084.40 1209.35 ## Q3 13040.05 3018.56 2633.41 3018.56 ## Max 98000.00 24000.00 24000.00 24000.00 ## MAD 5514.50 1388.06 1223.40 1388.06 ## IQR 11141.28 2562.47 2218.59 2562.47 ## CV 1.42 1.48 1.51 1.48 ## Skewness 2.87 3.08 3.34 3.08 ## SE.Skewness 0.07 0.03 0.03 0.03 ## Kurtosis 10.15 11.22 13.68 11.22 ## N.Valid 1376.00 5520.00 6233.00 5520.00 ## Pct.Valid 22.08 88.56 100.00 88.56 ## ## Table: Table continues below ## ## ## ## IdEncuesta NEDU1 Region Sexo v_25_OH_Vitamina_D2_D3_corr Zona ## ----------------- ------------ --------- --------- --------- ----------------------------- --------- ## Mean 25763.61 1.98 7.85 1.63 19.74 1.16 ## Std.Dev 3651.47 0.68 3.93 0.48 8.37 0.37 ## Min 20006.00 1.00 1.00 1.00 1.30 1.00 ## Q1 22684.00 2.00 5.00 1.00 13.50 1.00 ## Median 25698.00 2.00 7.00 2.00 18.90 1.00 ## Q3 28501.00 2.00 11.00 2.00 24.90 1.00 ## Max 70000.00 3.00 15.00 2.00 59.70 2.00 ## MAD 4308.44 0.00 4.45 0.00 8.45 0.00 ## IQR 5817.00 0.00 6.00 1.00 11.40 0.00 ## CV 0.14 0.34 0.50 0.30 0.42 0.32 ## Skewness 1.36 0.02 0.06 -0.53 0.59 1.86 ## SE.Skewness 0.03 0.03 0.03 0.03 0.05 0.03 ## Kurtosis 13.25 -0.83 -0.89 -1.72 0.38 1.48 ## N.Valid 6233.00 6174.00 6233.00 6233.00 2886.00 6233.00 ## Pct.Valid 100.00 99.05 100.00 100.00 46.30 100.00
# View(mydata)
CHEQUEAR FACTOR DE EXPANSION
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_CorrÂ
Describir muestra cruda
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)
Describir muestra cruda
hist(df0$v_25_OH_Vitamina_D2_D3_corr)
Generar variables
df0$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_)))
## `summarise()` regrouping output by 'Region', 'Area' (override with `.groups` argument)
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(Conglomerado_l = length(unique(Conglomerado_)))
## `summarise()` regrouping output by 'Region', 'Area' (override with `.groups` argument)
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)
)
## `summarise()` regrouping output by 'Region', 'Area' (override with `.groups` argument)
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()
)
## `summarise()` ungrouping output (override with `.groups` argument)
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 ## 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13119109 0 0 0 8 0 0 0 0 0 ## 13119110 0 0 0 6 0 0 0 0 0 ## 13119111 0 0 0 7 0 0 0 0 0 ## 13119112 0 0 0 4 0 0 0 0 0 ## 13119113 0 0 0 2 0 0 0 0 0 ## 13120102 0 0 0 6 0 0 0 0 0 ## 13120104 0 0 0 6 0 0 0 0 0 ## 13120105 0 0 0 6 0 0 0 0 0 ## 13120106 0 0 0 5 0 0 0 0 0 ## 13121101 0 0 0 4 0 0 0 0 0 ## 13121102 0 0 0 3 0 0 0 0 0 ## 13121103 0 0 0 5 0 0 0 0 0 ## 13122101 0 0 0 6 0 0 0 0 0 ## 13122102 0 0 0 3 0 0 0 0 0 ## 13122104 0 0 0 4 0 0 0 0 0 ## 13122105 0 0 0 8 0 0 0 0 0 ## 13122106 0 0 0 6 0 0 0 0 0 ## 13122107 0 0 0 6 0 0 0 0 0 ## 13123101 0 0 0 2 0 0 0 0 0 ## 13123102 0 0 0 6 0 0 0 0 0 ## 13123105 0 0 0 6 0 0 0 0 0 ## 13124101 0 0 0 7 0 0 0 0 0 ## 13124102 0 0 0 6 0 0 0 0 0 ## 13124103 0 0 0 3 0 0 0 0 0 ## 13124104 0 0 0 6 0 0 0 0 0 ## 13124105 0 0 0 7 0 0 0 0 0 ## 13124106 0 0 0 4 0 0 0 0 0 ## 13125101 0 0 0 6 0 0 0 0 0 ## 13125102 0 0 0 4 0 0 0 0 0 ## 13125103 0 0 0 5 0 0 0 0 0 ## 13125104 0 0 0 5 0 0 0 0 0 ## 13125105 0 0 0 7 0 0 0 0 0 ## 13126101 0 0 0 5 0 0 0 0 0 ## 13126102 0 0 0 3 0 0 0 0 0 ## 13126103 0 0 0 5 0 0 0 0 0 ## 13127101 0 0 0 4 0 0 0 0 0 ## 13127102 0 0 0 6 0 0 0 0 0 ## 13127103 0 0 0 5 0 0 0 0 0 ## 13127104 0 0 0 4 0 0 0 0 0 ## 13128101 0 0 0 5 0 0 0 0 0 ## 13128102 0 0 0 4 0 0 0 0 0 ## 13128103 0 0 0 5 0 0 0 0 0 ## 13128104 0 0 0 6 0 0 0 0 0 ## 13129101 0 0 0 4 0 0 0 0 0 ## 13129102 0 0 0 3 0 0 0 0 0 ## 13129104 0 0 0 6 0 0 0 0 0 ## 13130101 0 0 0 7 0 0 0 0 0 ## 13130103 0 0 0 6 0 0 0 0 0 ## 13131101 0 0 0 6 0 0 0 0 0 ## 13131102 0 0 0 7 0 0 0 0 0 ## 13132101 0 0 0 4 0 0 0 0 0 ## 13132102 0 0 0 3 0 0 0 0 0 ## 13201102 0 0 0 7 0 0 0 0 0 ## 13201103 0 0 0 6 0 0 0 0 0 ## 13201106 0 0 0 8 0 0 0 0 0 ## 13201107 0 0 0 6 0 0 0 0 0 ## 13201109 0 0 0 4 0 0 0 0 0 ## 13201110 0 0 0 6 0 0 0 0 0 ## 13201111 0 0 0 3 0 0 0 0 0 ## 13201112 0 0 0 4 0 0 0 0 0 ## 13201113 0 0 0 5 0 0 0 0 0 ## 13201114 0 0 0 6 0 0 0 0 0 ## 13201115 0 0 0 7 0 0 0 0 0 ## 13201116 0 0 0 6 0 0 0 0 0 ## 13201117 0 0 0 8 0 0 0 0 0 ## 13301101 0 0 0 9 0 0 0 0 0 ## 13301102 0 0 0 8 0 0 0 0 0 ## 13301201 0 0 0 0 7 0 0 0 0 ## 13301202 0 0 0 0 8 0 0 0 0 ## 13302101 0 0 0 7 0 0 0 0 0 ## 13302102 0 0 0 4 0 0 0 0 0 ## 13401101 0 0 0 6 0 0 0 0 0 ## 13401102 0 0 0 8 0 0 0 0 0 ## 13401103 0 0 0 9 0 0 0 0 0 ## 13401104 0 0 0 6 0 0 0 0 0 ## 13401105 0 0 0 2 0 0 0 0 0 ## 13401106 0 0 0 6 0 0 0 0 0 ## 13401107 0 0 0 3 0 0 0 0 0 ## 13402102 0 0 0 7 0 0 0 0 0 ## 13403101 0 0 0 6 0 0 0 0 0 ## 13404101 0 0 0 7 0 0 0 0 0 ## 13501101 0 0 0 9 0 0 0 0 0 ## 13501102 0 0 0 4 0 0 0 0 0 ## 13501201 0 0 0 0 15 0 0 0 0 ## 13501202 0 0 0 0 12 0 0 0 0 ## 13601101 0 0 0 5 0 0 0 0 0 ## 13602101 0 0 0 5 0 0 0 0 0 ## 13603101 0 0 0 3 0 0 0 0 0 ## 13604101 0 0 0 5 0 0 0 0 0 ## 13605101 0 0 0 4 0 0 0 0 0 ## 13605102 0 0 0 6 0 0 0 0 0 ## 14101101 0 0 0 0 0 7 0 0 0 ## 14101103 0 0 0 0 0 5 0 0 0 ## 14101104 0 0 0 0 0 10 0 0 0 ## 14101105 0 0 0 0 0 1 0 0 0 ## 14101106 0 0 0 0 0 7 0 0 0 ## 14101107 0 0 0 0 0 1 0 0 0 ## 14101108 0 0 0 0 0 7 0 0 0 ## 14101109 0 0 0 0 0 6 0 0 0 ## 14101110 0 0 0 0 0 7 0 0 0 ## 14101111 0 0 0 0 0 7 0 0 0 ## 14101112 0 0 0 0 0 2 0 0 0 ## 14101113 0 0 0 0 0 7 0 0 0 ## 14101114 0 0 0 0 0 6 0 0 0 ## 14101115 0 0 0 0 0 6 0 0 0 ## 14101117 0 0 0 0 0 5 0 0 0 ## 14101118 0 0 0 0 0 1 0 0 0 ## 14101119 0 0 0 0 0 7 0 0 0 ## 14101120 0 0 0 0 0 6 0 0 0 ## 14101121 0 0 0 0 0 8 0 0 0 ## 14101122 0 0 0 0 0 8 0 0 0 ## 14101123 0 0 0 0 0 7 0 0 0 ## 14101124 0 0 0 0 0 1 0 0 0 ## 14101125 0 0 0 0 0 3 0 0 0 ## 14101201 0 0 0 0 0 0 10 0 0 ## 14101202 0 0 0 0 0 0 10 0 0 ## 14104101 0 0 0 0 0 4 0 0 0 ## 14104102 0 0 0 0 0 5 0 0 0 ## 14104103 0 0 0 0 0 8 0 0 0 ## 14104104 0 0 0 0 0 5 0 0 0 ## 14104105 0 0 0 0 0 5 0 0 0 ## 14104201 0 0 0 0 0 0 10 0 0 ## 14104202 0 0 0 0 0 0 10 0 0 ## 14107101 0 0 0 0 0 5 0 0 0 ## 14107102 0 0 0 0 0 5 0 0 0 ## 14107103 0 0 0 0 0 6 0 0 0 ## 14107104 0 0 0 0 0 3 0 0 0 ## 14107105 0 0 0 0 0 6 0 0 0 ## 14107201 0 0 0 0 0 0 10 0 0 ## 14107202 0 0 0 0 0 0 10 0 0 ## 14201101 0 0 0 0 0 5 0 0 0 ## 14201102 0 0 0 0 0 5 0 0 0 ## 14201103 0 0 0 0 0 5 0 0 0 ## 14201104 0 0 0 0 0 5 0 0 0 ## 14201105 0 0 0 0 0 6 0 0 0 ## 14201201 0 0 0 0 0 0 10 0 0 ## 14201202 0 0 0 0 0 0 10 0 0 ## 14203201 0 0 0 0 0 0 10 0 0 ## 14203202 0 0 0 0 0 0 10 0 0 ## 14204101 0 0 0 0 0 3 0 0 0 ## 14204102 0 0 0 0 0 5 0 0 0 ## 14204103 0 0 0 0 0 4 0 0 0 ## 14204104 0 0 0 0 0 4 0 0 0 ## 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 ## 15101106 0 0 0 0 0 0 0 5 0 ## 15101107 0 0 0 0 0 0 0 7 0 ## 15101108 0 0 0 0 0 0 0 7 0 ## 15101109 0 0 0 0 0 0 0 5 0 ## 15101110 0 0 0 0 0 0 0 6 0 ## 15101111 0 0 0 0 0 0 0 7 0 ## 15101112 0 0 0 0 0 0 0 7 0 ## 15101113 0 0 0 0 0 0 0 4 0 ## 15101114 0 0 0 0 0 0 0 4 0 ## 15101115 0 0 0 0 0 0 0 6 0 ## 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 ## 15101156 0 0 0 0 0 0 0 6 0 ## 15101157 0 0 0 0 0 0 0 6 0 ## 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_)))
## `summarise()` regrouping output by 'Region', 'Area' (override with `.groups` argument)
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)
RESUMEN
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)
RESUMEN
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_))
RESUMEN
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)
RESUMEN
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, svytotal)
## exposure outcome se ## Low Low 26233723 2039508 ## Mid Mid 63524073 3826458 ## High High 33046763 3157836
svyglm(outcome~ Area+Edad, survey_design)
## Stratified 1 - level Cluster Sampling design (with replacement) ## With (1000) clusters. ## subset(survey_design, !is.na(outcome) & !is.na(exposure) & Edad >= ## 15) ## Sampling variables: ## - ids: Conglomerado_ ## - strata: strata_ ## - weights: fexp ## ## Call: svyglm(formula = outcome ~ Area + Edad, design = survey_design) ## ## Coefficients: ## (Intercept) AreaRural Edad ## 20.31281 4.76045 -0.02467 ## ## Degrees of Freedom: 2858 Total (i.e. Null); 968 Residual ## Null Deviance: 191600 ## Residual Deviance: 184000 AIC: 22310
svyglm(outcome1 ~Area+Edad, survey_design,
family = quasibinomial(link = "logit"))
## Stratified 1 - level Cluster Sampling design (with replacement) ## With (1000) clusters. ## subset(survey_design, !is.na(outcome) & !is.na(exposure) & Edad >= ## 15) ## Sampling variables: ## - ids: Conglomerado_ ## - strata: strata_ ## - weights: fexp ## ## Call: svyglm(formula = outcome1 ~ Area + Edad, design = survey_design, ## family = quasibinomial(link = "logit")) ## ## Coefficients: ## (Intercept) AreaRural Edad ## 2.2632370 -0.9590043 -0.0007256 ## ## Degrees of Freedom: 2858 Total (i.e. Null); 968 Residual ## Null Deviance: 2006 ## Residual Deviance: 1967 AIC: NA
Regresion lineal: VitD y area urbana/rural ajustada por edad
#Modelo fit2 <- svyglm(outcome~ Area+Edad, survey_design) summary(fit2)
## ## Call: ## svyglm(formula = outcome ~ Area + Edad, design = survey_design) ## ## Survey design: ## subset(survey_design, !is.na(outcome) & !is.na(exposure) & Edad >= ## 15) ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 20.31281 0.68915 29.475 < 2e-16 *** ## AreaRural 4.76045 0.64637 7.365 3.79e-13 *** ## Edad -0.02467 0.01269 -1.943 0.0523 . ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## (Dispersion parameter for gaussian family taken to be 64.38195) ## ## Number of Fisher Scoring iterations: 2
Regresion logÃstica: VitD<30 y area urbana/rural ajustada por edad
fit_t <- svyglm(outcome1 ~Area+Edad, survey_design,
family = quasibinomial(link = "logit"))
summary(fit_t)
## ## Call: ## svyglm(formula = outcome1 ~ Area + Edad, design = survey_design, ## family = quasibinomial(link = "logit")) ## ## Survey design: ## subset(survey_design, !is.na(outcome) & !is.na(exposure) & Edad >= ## 15) ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 2.2632370 0.2477010 9.137 < 2e-16 *** ## AreaRural -0.9590043 0.2015656 -4.758 2.26e-06 *** ## Edad -0.0007256 0.0042031 -0.173 0.863 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## (Dispersion parameter for quasibinomial family taken to be 1.000294) ## ## Number of Fisher Scoring iterations: 4
Media y error de niveles plasmáticos de vitamina D (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)
## 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
Media y error de niveles plasmáticos de vitamina D (ng/mL)
Generar tabla de medias, se, n e IC95%
res0 <- svyby(~outcome, ~exposure, survey_design, svymean) res0$exposure_ <- "Age" res0b <- survey_design %>% group_by(person,exposure) %>% summarize(n = unweighted(n())) res0 <- full_join(res0,res0b)
## Joining, by = "exposure"
res0$outcome_ <- "Mean" res_0 <- svyby(~outcome, ~person, survey_design, svymean) res_0$exposure <- "All" res_0b <- survey_design %>% group_by(person) %>% summarize(n = unweighted(n())) res_0 <- full_join(res_0,res_0b)
## Joining, by = "person"
res_0$outcome_ <- "Mean" res_0 <- full_join(res_0,res_0b)
## Joining, by = c("person", "n")
res0 <- full_join(res_0,res0)
## Joining, by = c("person", "outcome", "se", "exposure", "n", "outcome_")
Media y error de niveles plasmáticos de vitamina D (ng/mL)
res0
## person outcome se exposure n outcome_ exposure_ ## 1 1 19.81460 0.2964862 All 2859 Mean <NA> ## 2 1 19.78772 0.6054778 Low 857 Mean Age ## 3 1 19.78517 0.3667705 Mid 1391 Mean Age ## 4 1 19.89291 0.6933440 High 611 Mean Age
Media y error de niveles plasmáticos de vitamina D (ng/mL) en subpoblación
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) res0$exposure_ <- "Age" res0b <- survey_design %>% group_by(person,exposure) %>% summarize(n = unweighted(n())) res0 <- full_join(res0,res0b)
## Joining, by = c("person", "exposure")
res0$outcome_ <- "Mean" res_0 <- svyby(~outcome, ~person, survey_design, svymean) res_0$exposure <- "All" res_0b <- survey_design %>% group_by(person) %>% summarize(n = unweighted(n())) res_0 <- full_join(res_0,res_0b)
## Joining, by = "person"
res_0$outcome_ <- "Mean" res_0 <- full_join(res_0,res_0b)
## Joining, by = c("person", "n")
res0 <- full_join(res_0,res0)
## Joining, by = c("person", "outcome", "se", "exposure", "n", "outcome_")
# res0$value <- res0$outcome
# res0$value <- res0$outcome*100
res0$CI_i <- (res0$outcome-1.96*res0$se)
res0$CI_s <- (res0$outcome+1.96*res0$se)
# svyciprop(formula, design, method = c("logit", "likelihood", "asin", "beta","mean"), level = 0.95, ...)
# idem que cofint---
confint(svyby(~outcome, ~person+exposure, survey_design, svymean))
## 2.5 % 97.5 % ## 1.Low 19.88154 25.47994 ## 1.Mid 19.24538 20.87599 ## 1.High 18.03851 21.11203
# confint(svyby(~outcome, ~person+exposure+Area, survey_design, svymean, # method="likelihood")) # confint(svyby(~outcome, ~person+exposure+Area, survey_design, svymean, # method="logit")) # confint(svyby(~outcome, ~person+exposure+Area, survey_design, svymean, # method="asin")) # confint(svyby(~outcome, ~person+exposure+Area, survey_design, svymean, # method="beta")) # confint(svyby(~outcome, ~person+exposure+Area, survey_design, svymean, # method="mean"))
Media y error de niveles plasmáticos de vitamina D (ng/mL) en subpoblación
res0
## person outcome se exposure n outcome_ exposure_ CI_i CI_s ## 1 1 20.06962 0.3680455 All 1584 Mean <NA> 19.34825 20.79099 ## 2 1 22.68074 1.4281898 Low 113 Mean Age 19.88149 25.47999 ## 3 1 20.06069 0.4159796 Mid 976 Mean Age 19.24537 20.87601 ## 4 1 19.57527 0.7840747 High 495 Mean Age 18.03849 21.11206
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_i, ymax = CI_s),
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_i, ymax = CI_s),
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
plot_1
Prevalencia de vitamina D <30, <20 y <12
res0a <- svyby(~outcome1, ~person+exposure, survey_design, svytotal) res0b <- svyby(~outcome1, ~person+exposure, survey_design, svymean) res1 <- svyby(~outcome1, ~person+exposure, survey_design, svymean) res1$exposure_ <- "Age groups" res1b <- survey_design %>% group_by(person,exposure) %>% summarize(n = unweighted(n())) res1 <- full_join(res1,res1b)
## Joining, by = c("person", "exposure")
res1$outcome1_ <- "Mean" res_1 <- svyby(~outcome1, ~person, survey_design, svymean) res_1$exposure <- "All" res_1b <- survey_design %>% group_by(person) %>% summarize(n = unweighted(n())) res_1 <- full_join(res_1,res_1b)
## Joining, by = "person"
res_1$outcome1_ <- "Mean" res_1 <- full_join(res_1,res_1b)
## Joining, by = c("person", "n")
res1 <- full_join(res_1,res1)
## Joining, by = c("person", "outcome1", "se", "exposure", "n", "outcome1_")
# res1$value <- res1$outcome1 # res1$value <- res1$outcome1*111 res1$CI_i <- (res1$outcome1-1.96*res1$se) res1$CI_s <- (res1$outcome1+1.96*res1$se) res2 <- svyby(~outcome2, ~person+exposure, survey_design, svymean) res2$exposure_ <- "Age groups" res2b <- survey_design %>% group_by(person,exposure) %>% summarize(n = unweighted(n())) res2 <- full_join(res2,res2b)
## Joining, by = c("person", "exposure")
res2$outcome_ <- "Mean" res_2 <- svyby(~outcome2, ~person, survey_design, svymean) res_2$exposure <- "All" res_2b <- survey_design %>% group_by(person) %>% summarize(n = unweighted(n())) res_2 <- full_join(res_2,res_2b)
## Joining, by = "person"
res_2$outcome_ <- "Mean" res_2 <- full_join(res_2,res_2b)
## Joining, by = c("person", "n")
res2 <- full_join(res_2,res2)
## Joining, by = c("person", "outcome2", "se", "exposure", "n", "outcome_")
# res2$value <- res2$outcome # res2$value <- res2$outcome*100 res2$CI_i <- (res2$outcome2-1.96*res2$se) res2$CI_s <- (res2$outcome2+1.96*res2$se) res3 <- svyby(~outcome3, ~person+exposure, survey_design, svymean) res3$exposure_ <- "Age groups" res3b <- survey_design %>% group_by(person,exposure) %>% summarize(n = unweighted(n())) res3 <- full_join(res3,res3b)
## Joining, by = c("person", "exposure")
res3$outcome_ <- "Mean" res_3 <- svyby(~outcome3, ~person, survey_design, svymean) res_3$exposure <- "All" res_3b <- survey_design %>% group_by(person) %>% summarize(n = unweighted(n())) res_3 <- full_join(res_3,res_3b)
## Joining, by = "person"
res_3$outcome_ <- "Mean" res_3 <- full_join(res_3,res_3b)
## Joining, by = c("person", "n")
res3 <- full_join(res_3,res3)
## Joining, by = c("person", "outcome3", "se", "exposure", "n", "outcome_")
# res3$value <- res3$outcome # res3$value <- res3$outcome*100 res3$CI_i <- (res3$outcome3-1.96*res3$se) res3$CI_s <- (res3$outcome3+1.96*res3$se)
Prevalencia de vitamina D <30
res1
## person outcome1 se exposure n outcome1_ exposure_ CI_i ## 1 1 0.8900505 0.01325489 All 1584 Mean <NA> 0.8640709 ## 2 1 0.8512115 0.07232704 Low 113 Mean Age groups 0.7094505 ## 3 1 0.8890693 0.01539621 Mid 976 Mean Age groups 0.8588927 ## 4 1 0.8995363 0.02479161 High 495 Mean Age groups 0.8509448 ## CI_s ## 1 0.9160300 ## 2 0.9929725 ## 3 0.9192458 ## 4 0.9481279
Prevalencia de vitamina D <20
res2
## person outcome2 se exposure n outcome_ exposure_ CI_i ## 1 1 0.5224473 0.02393065 All 1584 Mean <NA> 0.4755432 ## 2 1 0.3645948 0.07862445 Low 113 Mean Age groups 0.2104909 ## 3 1 0.5030312 0.03049085 Mid 976 Mean Age groups 0.4432691 ## 4 1 0.5905336 0.03962239 High 495 Mean Age groups 0.5128737 ## CI_s ## 1 0.5693514 ## 2 0.5186988 ## 3 0.5627933 ## 4 0.6681935
Prevalencia de vitamina D <12
res3
## person outcome3 se exposure n outcome_ exposure_ CI_i ## 1 1 0.16048978 0.01612776 All 1584 Mean <NA> 0.12887936 ## 2 1 0.06202279 0.02318446 Low 113 Mean Age groups 0.01658125 ## 3 1 0.15350749 0.02081670 Mid 976 Mean Age groups 0.11270676 ## 4 1 0.19314277 0.03000457 High 495 Mean Age groups 0.13433381 ## CI_s ## 1 0.1921002 ## 2 0.1074643 ## 3 0.1943082 ## 4 0.2519517
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 exposure n outcome_ exposure_ CI_i ## 1 1 0.89005046 0.01325489 All 1584 Mean <NA> 0.86407087 ## 2 1 0.85121154 0.07232704 Low 113 Mean Age groups 0.70945054 ## 3 1 0.88906925 0.01539621 Mid 976 Mean Age groups 0.85889269 ## 4 1 0.89953632 0.02479161 High 495 Mean Age groups 0.85094475 ## 5 1 0.52244729 0.02393065 All 1584 Mean <NA> 0.47554321 ## 6 1 0.36459484 0.07862445 Low 113 Mean Age groups 0.21049092 ## 7 1 0.50303119 0.03049085 Mid 976 Mean Age groups 0.44326913 ## 8 1 0.59053356 0.03962239 High 495 Mean Age groups 0.51287367 ## 9 1 0.16048978 0.01612776 All 1584 Mean <NA> 0.12887936 ## 10 1 0.06202279 0.02318446 Low 113 Mean Age groups 0.01658125 ## 11 1 0.15350749 0.02081670 Mid 976 Mean Age groups 0.11270676 ## 12 1 0.19314277 0.03000457 High 495 Mean Age groups 0.13433381 ## CI_s type ## 1 0.9160300 oucome1 ## 2 0.9929725 oucome1 ## 3 0.9192458 oucome1 ## 4 0.9481279 oucome1 ## 5 0.5693514 oucome2 ## 6 0.5186988 oucome2 ## 7 0.5627933 oucome2 ## 8 0.6681935 oucome2 ## 9 0.1921002 oucome3 ## 10 0.1074643 oucome3 ## 11 0.1943082 oucome3 ## 12 0.2519517 oucome3
Plot
plot2<-ggplot(res0)+
geom_point(aes(x=exposure, y=outcome, col=type))+
geom_errorbar(aes(x=exposure, y=outcome,
ymin = CI_i, ymax = CI_s),
width = 0.1,size = 0.1, position = position_dodge(0.9))
plot2
Plot
plot2<-plot2 + facet_wrap(type~ . , scales = "free_y", nrow = 3) plot2
Rangos de vitamina D
- 0: Vit D >=30
- 1: Vit D 20-29.9
- 2: Vit D 12-19.9
- 3: Vit D <12
Rangos de vitamina D
res0a <- svyby(~outcome5_, ~person+exposure, survey_design, svytotal) 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 271026.02 ## 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 48892.137
write.csv(res0a,file="check.csv")
Rangos de vitamina D
res0 <- svyby(~outcome5_, ~person+exposure, survey_design, svymean) 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 0.1004637 0.3090028 0.3973908 0.19314277 ## se.outcome5_0 se.outcome5_1 se.outcome5_2 se.outcome5_3 ## 1.Low 0.07232704 0.08151790 0.07835190 0.02318446 ## 1.Mid 0.01539621 0.02790336 0.02756623 0.02081670 ## 1.High 0.02479161 0.03449155 0.03588078 0.03000457
Rangos de vitamina D
res0 <- svyby(~outcome5_, ~person+exposure, survey_design, svymean) res0$exposure_ <- "Age groups" res0b <- survey_design %>% group_by(person,exposure) %>% summarize(n = unweighted(n())) res0 <- full_join(res0,res0b)
## Joining, by = c("person", "exposure")
res0$outcome5__ <- "Mean" res_0 <- svyby(~outcome5_, ~person, survey_design, svymean) res_0$exposure <- "All" res_0b <- survey_design %>% group_by(person) %>% summarize(n = unweighted(n())) res_0 <- full_join(res_0,res_0b)
## Joining, by = "person"
res_0$outcome5__ <- "Mean" res_0 <- full_join(res_0,res_0b)
## Joining, by = c("person", "n")
res0 <- full_join(res_0,res0)
## Joining, by = c("person", "outcome5_0", "outcome5_1", "outcome5_2", "outcome5_3", "se.outcome5_0", "se.outcome5_1", "se.outcome5_2", "se.outcome5_3", "exposure", "n", "outcome5__")
res0$person <- NULL res0$outcome5__ <- NULL #Guardo data con unweighted n res0n <- res0 #Derrito mi base de datos, original hacial el lado, ahora es hacia abajo res0 <- melt(res0)
## Using exposure, exposure_ as id variables
#Selecciono desviación estandar
res0a <- res0[grepl("se.",res0$variable),]
res0$variable <- gsub("se.o","se.O.",res0$variable)
#Selecciono medias
res0b <- res0[grepl("out",res0$variable),]
names(res0b) <- paste(names(res0b),"_",sep="")
# las pego lado a lado
res0c <- cbind(res0a,res0b)
res0 <- res0c
res0$se <- res0$value*100
res0$outcome5_ <- res0$value_*100
#calculo intervalo de confianza 95%
res0$CI_i <- (res0$outcome5_-1.96*res0$se)
res0$CI_s <- (res0$outcome5_+1.96*res0$se)
names(res0) <- make.unique(names(res0))
res0n <- res0n[c("exposure","n")]
res0 <- left_join(res0,res0n)
## Joining, by = "exposure"
res0 <- res0[order(res0$exposure_.1),]
res0 <- res0[c("exposure","variable_","outcome5_",
"se","CI_i","CI_s")]
write.csv(res0,file="res0.csv")
Rangos de vitamina D
res0
## exposure variable_ outcome5_ se CI_i CI_s ## 1 All outcome5_0 10.994954 1.325489 8.3969956 13.59291 ## 5 All outcome5_1 36.760317 2.207990 32.4326570 41.08798 ## 9 All outcome5_2 36.195751 2.193420 31.8966473 40.49485 ## 13 All outcome5_3 16.048978 1.612776 12.8879361 19.21002 ## 4 High outcome5_0 10.046368 2.479161 5.1872121 14.90552 ## 8 High outcome5_1 30.900275 3.449155 24.1399307 37.66062 ## 12 High outcome5_2 39.739079 3.588078 32.7064468 46.77171 ## 16 High outcome5_3 19.314277 3.000457 13.4333810 25.19517 ## 2 Low outcome5_0 14.878846 7.232704 0.7027466 29.05495 ## 6 Low outcome5_1 48.661669 8.151790 32.6841614 64.63918 ## 10 Low outcome5_2 30.257206 7.835190 14.9002342 45.61418 ## 14 Low outcome5_3 6.202279 2.318446 1.6581250 10.74643 ## 3 Mid outcome5_0 11.093075 1.539621 8.0754177 14.11073 ## 7 Mid outcome5_1 38.603806 2.790336 33.1347487 44.07286 ## 11 Mid outcome5_2 34.952370 2.756623 29.5493881 40.35535 ## 15 Mid outcome5_3 15.350749 2.081670 11.2706763 19.43082
Regresion lineal: VitD y area urbana/rural ajustada por edad
Primero describo y luego modelo
#Describo svyby(~outcome, ~Area, survey_design, svymean)
## Area outcome se ## Urban Urban 19.53875 0.4051727 ## Rural Rural 24.17664 0.6420721
Regresion lineal: VitD y area urbana/rural ajustada por edad
#Modelo fit2 <- svyglm(outcome~ Area+Edad, survey_design) summary(fit2)
## ## Call: ## svyglm(formula = outcome ~ Area + Edad, design = survey_design) ## ## Survey design: ## subset(survey_design, Edad < 50 & Sexo == 2) ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 19.729818 1.176567 16.769 < 2e-16 *** ## AreaRural 4.649011 0.763475 6.089 1.8e-09 *** ## Edad -0.006081 0.034642 -0.176 0.861 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## (Dispersion parameter for gaussian family taken to be 61.07582) ## ## Number of Fisher Scoring iterations: 2
Regresion lineal: VitD y area urbana/rural ajustada por edad
fit2 <- svyglm(outcome~ Area+Edad, survey_design) fit2 <- summary(fit2) fit2 <- as.data.frame(coef(fit2)) fit2$name <- rownames(fit2) fit2$outcome <- "Vit D numeric"
Regresion lineal: VitD y area urbana/rural ajustada por edad
fit2
## Estimate Std. Error t value Pr(>|t|) name ## (Intercept) 19.729818178 1.1765675 16.7689647 6.174439e-54 (Intercept) ## AreaRural 4.649010986 0.7634752 6.0892754 1.799608e-09 AreaRural ## Edad -0.006080526 0.0346421 -0.1755242 8.607145e-01 Edad ## outcome ## (Intercept) Vit D numeric ## AreaRural Vit D numeric ## Edad Vit D numeric
write.csv(fit2,file="fit2.csv")
Regresion logÃstica: VitD<30 y area urbana/rural ajustada por edad
res1a <- svyby(~outcome1, ~Area, survey_design, svymean)
fit_t <- svyglm(outcome1 ~Area+Edad, survey_design,
family = quasibinomial(link = "logit"))
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$name <- NULL
Regresion logÃstica: VitD<30 y area urbana/rural ajustada por edad
res1a
## Area outcome1 se ## Urban Urban 0.9046057 0.01422092 ## Rural Rural 0.7774459 0.03355157
fit_t
## outcome OR OR_i OR_s pvalue ## AreaRural VitD<30 0.37 0.22 0.61 0.0001 ## Edad VitD<30 1.00 0.97 1.02 0.9136
Regresion logÃstica: VitD<30 y area urbana/rural ajustada por edad
fit_t <- svyglm(outcome1 ~Area+Edad, survey_design,
family = quasibinomial(link = "logit"))
summary(fit_t)
## ## Call: ## svyglm(formula = outcome1 ~ Area + Edad, design = survey_design, ## family = quasibinomial(link = "logit")) ## ## Survey design: ## subset(survey_design, Edad < 50 & Sexo == 2) ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 2.29419 0.43864 5.230 2.19e-07 *** ## AreaRural -0.99606 0.25706 -3.875 0.000116 *** ## Edad -0.00142 0.01309 -0.108 0.913645 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## (Dispersion parameter for quasibinomial family taken to be 1.000614) ## ## Number of Fisher Scoring iterations: 4
Varias regresiones logÃsticas
fit_t <- svyglm(outcome1 ~Area+Edad, survey_design,
family = quasibinomial(link = "logit"))
fit_t <- summary(fit_t)
fit_t <- as.data.frame(coef(fit_t))
fit_t$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"))
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$name <- NULL
fit_t2<-fit_t
fit_t <- svyglm(outcome3 ~Area+Edad, survey_design,
family = quasibinomial(link = "logit"))
fit_t <- summary(fit_t)
fit_t <- as.data.frame(coef(fit_t))
fit_t$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"))
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$name <- NULL
fit_t1b<-fit_t
fit_t <- svyglm(outcome2 ~Area+Educational_level, survey_design,
family = quasibinomial(link = "logit"))
fit_t <- summary(fit_t)
fit_t <- as.data.frame(coef(fit_t))
fit_t$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"))
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_i <- fit_123a$OR_i
fit_123a$CI_s <- 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_i CI_s 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 0.9136 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 0.98 1.02 0.8521 VitD<20 0.98 1.02 Edad ## 5 0.28 0.28 0.11 0.73 0.0090 VitD<12 0.11 0.73 AreaRural ## 6 0.99 0.99 0.96 1.01 0.3617 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 0.7610 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 0.1631 VitD<20 0.81 3.48 Educational_levelMid ## 12 2.09 2.09 0.97 4.49 0.0595 VitD<20 0.97 4.49 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 3.12 3.12 1.29 7.58 0.0121 VitD<12 1.29 7.58 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
Varias regresiones logÃsticas
res0
## outcome OR OR_i OR_s pvalue type CI_i CI_s 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 0.9136 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 0.98 1.02 0.8521 VitD<20 0.98 1.02 Edad ## 5 0.28 0.28 0.11 0.73 0.0090 VitD<12 0.11 0.73 AreaRural ## 6 0.99 0.99 0.96 1.01 0.3617 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 0.7610 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 0.1631 VitD<20 0.81 3.48 Educational_levelMid ## 12 2.09 2.09 0.97 4.49 0.0595 VitD<20 0.97 4.49 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 3.12 3.12 1.29 7.58 0.0121 VitD<12 1.29 7.58 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)
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_i, ymax = CI_s),
width = 0.1,
size = 0.1,
position = position_dodge(0.9)
)+
geom_hline(yintercept =c(1),col=c("black"))+
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 = 10,
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 = "free_y")
plot_1
Plot
plot_1
unique(cbind(res0$exposure,res0$exposure_lbl))
## [,1] [,2] ## [1,] "1" "AreaRural" ## [2,] "2" "Edad" ## [3,] "4" "Educational_levelMid" ## [4,] "3" "Educational_levelHigh"
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Explore
https://faculty.washington.edu/tlumley/old-survey/survey-wss.pdf https://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx
ggplot https://www.datanovia.com/en/blog/the-a-z-of-rcolorbrewer-palette/