Prevalencia (prevere) y Extensión (cpodbere) por ciudad, con IC y p valor de Wald test, creo que para extensión solo miramos los intervalos de confianza.
Regresión Logística Uni-variada para prevere: 1) país 2) sexo 3) Escolmaerecat23 4) tipoesc 5) freqescovacat 6) usocreme 7) fluorprof Regresión Poisson Uni-variada para cpodbere: 1) país 2) sexo 3) Escolmaerecat23 4) tipoesc 5) freqescovacat 6) usocreme 7) fluorprof Si todo es parecido prevoms y cpodoms La Multi-variada en ambos modelos irían: 1) país 3) escmaerecat23 4) tipoesc 5) freqescovat Van todas las que queden <0,10 en la uni-variada. Por eso va variable país. Espero, que puedas el finde…..Besos Anun
# 3 de Noviembre 2013
#
# load('~/Dropbox/odontologia/maestria_anunziatta/BancoUnidoBrasUru/BRASURU(28102013).RData')
load("~/Dropbox/odontologia/maestria_anunziatta/BancoUnidoBrasUru/BRASURU(02112013).RData")
library(survey)
##
## Attaching package: 'survey'
##
## The following object(s) are masked from 'package:graphics':
##
## dotchart
library(car)
## Loading required package: MASS
## Loading required package: nnet
levels(disenio_urubra$variables$prevbere)
## [1] "1-LC" "2-NTL"
addmargins(table(disenio_urubra$variables$prevbere, disenio_urubra$variables$pais))
##
## Bra Uru Sum
## 1-LC 986 764 1750
## 2-NTL 542 390 932
## Sum 1528 1154 2682
prevbere <- as.numeric(disenio_urubra$variables$prevbere)
table(prevbere)
## prevbere
## 1 2
## 1750 932
prevbere <- as.factor(prevbere)
prevbere <- recode(prevbere, "1='1-LC';2='0-NTL'")
table(prevbere)
## prevbere
## 0-NTL 1-LC
## 932 1750
disenio_urubra$variables$prevbere <- prevbere
addmargins(table(disenio_urubra$variables$cpodbere, disenio_urubra$variables$pais))
##
## Bra Uru Sum
## 0 542 390 932
## 1 272 131 403
## 2 228 104 332
## 3 154 98 252
## 4 129 107 236
## 5 77 100 177
## 6 50 64 114
## 7 23 56 79
## 8 18 27 45
## 9 14 23 37
## 10 11 22 33
## 11 4 11 15
## 12 2 10 12
## 13 1 1 2
## 14 0 4 4
## 15 0 1 1
## 17 1 1 2
## 18 2 1 3
## 20 0 1 1
## 21 0 1 1
## 26 0 1 1
## Sum 1528 1154 2682
addmargins(table(disenio_urubra$variables$cpodbere, disenio_urubra$variables$prevbere))
##
## 0-NTL 1-LC Sum
## 0 932 0 932
## 1 0 403 403
## 2 0 332 332
## 3 0 252 252
## 4 0 236 236
## 5 0 177 177
## 6 0 114 114
## 7 0 79 79
## 8 0 45 45
## 9 0 37 37
## 10 0 33 33
## 11 0 15 15
## 12 0 12 12
## 13 0 2 2
## 14 0 4 4
## 15 0 1 1
## 17 0 2 2
## 18 0 3 3
## 20 0 1 1
## 21 0 1 1
## 26 0 1 1
## Sum 932 1750 2682
round(svymean(~prevbere, disenio_urubra, deff = TRUE) * 100, 2)
## mean SE DEff
## prevbere0-NTL 34.9000 0.0184 4.23
## prevbere1-LC 65.1000 0.0184 4.23
round(confint(svymean(~prevbere, disenio_urubra, deff = TRUE) * 100), 2)
## 2.5 % 97.5 %
## prevbere0-NTL 34.86 34.94
## prevbere1-LC 65.06 65.14
round(svymean(~cpodbere, disenio_urubra, deff = TRUE), 2) * 1
## mean SE DEff
## cpodbere 2.440 0.145 7.43
round(confint(svymean(~cpodbere, disenio_urubra, deff = TRUE)), 2)
## 2.5 % 97.5 %
## cpodbere 2.15 2.72
round(svyby(~prevbere, ~pais, disenio_urubra, svymean, deff = TRUE)[, 2:4] *
100, 2)
## prevbere0-NTL prevbere1-LC se.prevbere0-NTL
## Bra 36.47 63.53 2.06
## Uru 33.71 66.29 2.76
round(confint(svyby(~prevbere, ~pais, disenio_urubra, svymean, deff = TRUE)) *
100, 2)
## 2.5 % 97.5 %
## Bra:prevbere0-NTL 32.44 40.51
## Uru:prevbere0-NTL 28.30 39.11
## Bra:prevbere1-LC 59.49 67.56
## Uru:prevbere1-LC 60.89 71.70
round(svyby(~cpodbere, ~pais, disenio_urubra, svymean, deff = TRUE)[, 2:4] *
1, 2)
## cpodbere se DEff.cpodbere
## Bra 1.96 0.10 3.05
## Uru 2.80 0.22 6.12
round(confint(svyby(~cpodbere, ~pais, disenio_urubra, svymean, deff = TRUE)) *
1, 2)
## 2.5 % 97.5 %
## Bra 1.76 2.16
## Uru 2.37 3.24
TABLAS
tabla1 <- svyby(~prevbere, ~pais, disenio_urubra, svymean, na.rm = TRUE, deff = TRUE)
round(tabla1[, 2:4] * 100, 1)
## prevbere0-NTL prevbere1-LC se.prevbere0-NTL
## Bra 36.5 63.5 2.1
## Uru 33.7 66.3 2.8
round(confint(tabla1) * 100, 2)
## 2.5 % 97.5 %
## Bra:prevbere0-NTL 32.44 40.51
## Uru:prevbere0-NTL 28.30 39.11
## Bra:prevbere1-LC 59.49 67.56
## Uru:prevbere1-LC 60.89 71.70
svychisq(~prevbere + pais, disenio_urubra, statistic = "Chisq")
##
## Pearson's X^2: Rao & Scott adjustment
##
## data: svychisq(~prevbere + pais, disenio_urubra, statistic = "Chisq")
## X-squared = 2.218, df = 1, p-value = 0.4239
levels(disenio_urubra$variables$sexoinv) <- c("M", "F")
tabla2 <- svyby(~prevbere, ~sexoinv + pais, disenio_urubra, svymean, keep.var = TRUE,
na.rm = TRUE, deff = TRUE)
round(tabla2[, 3:5] * 100, 1)
## prevbere0-NTL prevbere1-LC se.prevbere0-NTL
## M.Bra 39.4 60.6 2.5
## F.Bra 33.5 66.5 2.3
## M.Uru 32.6 67.4 3.9
## F.Uru 34.9 65.1 3.3
tabla3 <- svyby(~prevbere, ~socioecon4cat + pais, disenio_urubra, svymean, na.rm = TRUE,
deff = TRUE)
round(tabla3[, 3:5] * 100, 1)
## prevbere0-NTL prevbere1-LC se.prevbere0-NTL
## 1-alto.Bra 48.0 52.0 3.5
## 2-medio-alto.Bra 45.4 54.6 2.7
## 3-medio-bajo.Bra 31.7 68.3 2.0
## 4-bajo.Bra 27.9 72.1 4.5
## 1-alto.Uru 52.8 47.2 6.0
## 2-medio-alto.Uru 34.8 65.2 4.2
## 3-medio-bajo.Uru 27.6 72.4 3.6
## 4-bajo.Uru 20.0 80.0 3.4
tabla4 <- svyby(~prevbere, ~socioecon3cat + pais, disenio_urubra, svymean, na.rm = TRUE,
deff = TRUE)
round(tabla4[, 3:5] * 100, 1)
## prevbere0-NTL prevbere1-LC se.prevbere0-NTL
## 1-alto.Bra 48.0 52.0 3.5
## 2-medio.Bra 35.9 64.1 2.0
## 3-bajo.Bra 27.9 72.1 4.5
## 1-alto.Uru 48.6 51.4 4.6
## 2-medio.Uru 29.0 71.0 2.5
## 3-bajo.Uru 20.0 80.0 3.4
tabla5 <- svyby(~prevbere, ~escolmaerecat23cat + pais, disenio_urubra, svymean,
na.rm = TRUE, deff = TRUE)
round(tabla5[, 3:5] * 100, 1)
## prevbere0-NTL prevbere1-LC se.prevbere0-NTL
## 1-college-university.Bra 48.1 51.9 4.1
## 2-high school.Bra 41.5 58.5 2.7
## 3-elementary school.Bra 28.8 71.2 2.2
## 1-college-university.Uru 47.0 53.0 5.7
## 2-high school.Uru 32.6 67.4 2.6
## 3-elementary school.Uru 21.1 78.9 3.2
tabla5a <- svyby(~prevbere, ~escolmae13cat + pais, disenio_urubra, svymean,
na.rm = TRUE, deff = TRUE)
round(tabla5a[, 3:5] * 100, 1)
## prevbere0-NTL prevbere1-LC se.prevbere0-NTL
## 1-college-university.Bra 47.8 52.2 4.0
## 2-high school.Bra 46.2 53.8 2.7
## 3-Elementary School.Bra 29.1 70.9 2.1
## 1-college-university.Uru 55.4 44.6 4.2
## 2-high school.Uru 36.4 63.6 5.5
## 3-Elementary School.Uru 26.2 73.8 2.6
tabla6 <- svyby(~prevbere, ~freqescovacat + pais, disenio_urubra, svymean, na.rm = TRUE,
deff = TRUE)
round(tabla6[, 3:5] * 100, 1)
## prevbere0-NTL prevbere1-LC se.prevbere0-NTL
## 1-menos de 1 vez al d\xeda.Bra 25.3 74.7 2.8
## 2-veces al d\xeda.Bra 38.5 61.5 2.5
## 3-veces al d\xeda.Bra 40.8 59.2 3.0
## 1-menos de 1 vez al d\xeda.Uru 26.2 73.8 4.0
## 2-veces al d\xeda.Uru 33.9 66.1 4.0
## 3-veces al d\xeda.Uru 36.7 63.3 3.3
tabla7 <- svyby(~prevbere, ~usocreme + pais, disenio_urubra, svymean, na.rm = TRUE,
deff = TRUE)
round(tabla7[, 3:5] * 100, 1)
## prevbere0-NTL prevbere1-LC se.prevbere0-NTL
## 1-Si.Bra 36.7 63.3 2.0
## 2-No.Bra 24.2 75.8 9.0
## 1-Si.Uru 33.9 66.1 2.8
## 2-No.Uru 26.2 73.8 10.9
tabla8 <- svyby(~prevbere, ~visidentcatonde + pais, disenio_urubra, svymean,
na.rm = TRUE, deff = TRUE)
round(tabla8[, 3:5] * 100, 1)
## prevbere0-NTL prevbere1-LC se.prevbere0-NTL
## 1-Convenio particular.Bra 42.1 57.9 2.1
## 2-Publico.Bra 28.5 71.5 2.6
## 3-Nunca fue al dentista.Bra 34.7 65.3 3.1
## 1-Convenio particular.Uru 41.7 58.3 3.4
## 2-Publico.Uru 21.5 78.5 2.7
## 3-Nunca fue al dentista.Uru 23.7 76.3 5.1
tabla9 <- svyby(~prevbere, ~visiquando + pais, disenio_urubra, svymean, na.rm = TRUE,
deff = TRUE)
round(tabla9[, 3:5] * 100, 1)
## prevbere0-NTL prevbere1-LC se.prevbere0-NTL
## 1- menos de 1 a\xf1o.Bra 36.6 63.4 2.4
## 2- m\xe1s de 2 a\xf1os.Bra 37.9 62.1 2.8
## 3- nunca fue.Bra 34.7 65.3 3.1
## 1- menos de 1 a\xf1o.Uru 36.4 63.6 3.0
## 2- m\xe1s de 2 a\xf1os.Uru 29.8 70.2 5.5
## 3- nunca fue.Uru 22.6 77.4 5.1
tabla10 <- svyby(~prevbere, ~fluorprof + pais, disenio_urubra, svymean, na.rm = TRUE,
deff = TRUE)
round(tabla10[, 3:5] * 100, 1)
## prevbere0-NTL prevbere1-LC se.prevbere0-NTL
## 1-Si.Bra 37.3 62.7 2.1
## 2-No.Bra 35.0 65.0 2.9
## 1-Si.Uru 34.4 65.6 3.6
## 2-No.Uru 34.2 65.8 3.9
tabla11 <- svyby(~prevbere, ~isg20 + pais, disenio_urubra, svymean, na.rm = TRUE,
deff = TRUE)
round(tabla11[, 3:5] * 100, 1)
## prevbere0-NTL prevbere1-LC se.prevbere0-NTL
## 1- <=20.Bra 39.5 60.5 7.4
## 2- 20 a 60.Bra 42.1 57.9 2.2
## 3- >=60.Bra 24.4 75.6 2.1
## 1- <=20.Uru 35.0 65.0 4.3
## 2- 20 a 60.Uru 33.5 66.5 3.4
## 3- >=60.Uru 26.8 73.2 5.2
tabla12 <- svyby(~prevbere, ~isg45 + pais, disenio_urubra, svymean, na.rm = TRUE,
deff = TRUE)
round(tabla12[, 3:5] * 100, 1)
## prevbere0-NTL prevbere1-LC se.prevbere0-NTL
## 1- <=45.Bra 47.1 52.9 2.8
## 2- 45 a 60.Bra 36.9 63.1 2.5
## 3- >=60.Bra 24.4 75.6 2.1
## 1- <=45.Uru 35.7 64.3 3.3
## 2- 45 a 60.Uru 25.6 74.4 4.6
## 3- >=60.Uru 26.8 73.2 5.2
tabla13 <- svyby(~prevbere, ~tipoesc + pais, disenio_urubra, svymean, na.rm = TRUE,
deff = TRUE)
round(tabla13[, 3:5] * 100, 1)
## prevbere0-NTL prevbere1-LC se.prevbere0-NTL
## 1-Particular.Bra 52.1 47.9 2.9
## 2-Publica.Bra 32.1 67.9 1.9
## 1-Particular.Uru 46.2 53.8 4.0
## 2-Publica.Uru 29.1 70.9 2.7
tabla13 <- svyby(~cpodbere, ~pais, disenio_urubra, svymean, na.rm = TRUE, deff = TRUE)
round(tabla13[, 2:4] * 1, 1)
## cpodbere se DEff.cpodbere
## Bra 2.0 0.1 3.0
## Uru 2.8 0.2 6.1
round(confint(tabla13) * 1, 2)
## 2.5 % 97.5 %
## Bra 1.76 2.16
## Uru 2.37 3.24
tabla14 <- svyby(~cpodbere, ~sexoinv + pais, disenio_urubra, svymean, keep.var = TRUE,
na.rm = TRUE, deff = TRUE)
round(tabla14[, 3:5] * 1, 1)
## cpodbere se DEff.cpodbere
## M.Bra 1.9 0.1 2.3
## F.Bra 2.0 0.1 1.7
## M.Uru 2.8 0.3 4.3
## F.Uru 2.8 0.2 4.2
tabla15 <- svyby(~cpodbere, ~socioecon4cat + pais, disenio_urubra, svymean,
na.rm = TRUE, deff = TRUE)
round(tabla15[, 3:5] * 1, 1)
## cpodbere se DEff.cpodbere
## 1-alto.Bra 1.2 0.1 0.9
## 2-medio-alto.Bra 1.5 0.1 0.9
## 3-medio-bajo.Bra 2.2 0.1 2.0
## 4-bajo.Bra 2.4 0.2 1.1
## 1-alto.Uru 1.7 0.4 3.3
## 2-medio-alto.Uru 2.7 0.4 7.1
## 3-medio-bajo.Uru 3.5 0.4 3.4
## 4-bajo.Uru 3.2 0.3 2.8
tabla16 <- svyby(~cpodbere, ~socioecon3cat + pais, disenio_urubra, svymean,
na.rm = TRUE, deff = TRUE)
round(tabla16[, 3:5] * 1, 1)
## cpodbere se DEff.cpodbere
## 1-alto.Bra 1.2 0.1 0.9
## 2-medio.Bra 2.0 0.1 2.2
## 3-bajo.Bra 2.4 0.2 1.1
## 1-alto.Uru 2.0 0.3 3.5
## 2-medio.Uru 3.2 0.2 3.4
## 3-bajo.Uru 3.2 0.3 2.8
tabla17 <- svyby(~cpodbere, ~escolmaerecat23cat + pais, disenio_urubra, svymean,
na.rm = TRUE, deff = TRUE)
round(tabla17[, 3:5] * 1, 1)
## cpodbere se DEff.cpodbere
## 1-college-university.Bra 1.2 0.1 1.8
## 2-high school.Bra 1.7 0.1 0.8
## 3-elementary school.Bra 2.4 0.1 2.1
## 1-college-university.Uru 2.0 0.4 4.7
## 2-high school.Uru 2.9 0.2 2.0
## 3-elementary school.Uru 3.5 0.4 4.7
tabla17a <- svyby(~cpodbere, ~escolmae13cat + pais, disenio_urubra, svymean,
na.rm = TRUE, deff = TRUE)
round(tabla17a[, 3:5] * 1, 1)
## cpodbere se DEff.cpodbere
## 1-college-university.Bra 1.2 0.1 0.9
## 2-high school.Bra 1.5 0.1 1.1
## 3-Elementary School.Bra 2.4 0.1 2.0
## 1-college-university.Uru 1.6 0.3 2.3
## 2-high school.Uru 2.6 0.4 4.5
## 3-Elementary School.Uru 3.2 0.2 3.6
tabla18 <- svyby(~cpodbere, ~freqescovacat + pais, disenio_urubra, svymean,
na.rm = TRUE, deff = TRUE)
round(tabla18[, 3:5] * 1, 1)
## cpodbere se DEff.cpodbere
## 1-menos de 1 vez al d\xeda.Bra 2.5 0.1 0.8
## 2-veces al d\xeda.Bra 1.9 0.1 2.5
## 3-veces al d\xeda.Bra 1.6 0.1 1.5
## 1-menos de 1 vez al d\xeda.Uru 3.4 0.3 2.0
## 2-veces al d\xeda.Uru 2.8 0.3 3.9
## 3-veces al d\xeda.Uru 2.6 0.3 3.8
tabla19 <- svyby(~cpodbere, ~usocreme + pais, disenio_urubra, svymean, na.rm = TRUE,
deff = TRUE)
round(tabla19[, 3:5] * 1, 1)
## cpodbere se DEff.cpodbere
## 1-Si.Bra 1.9 0.1 2.9
## 2-No.Bra 2.7 0.6 1.4
## 1-Si.Uru 2.8 0.2 5.9
## 2-No.Uru 2.6 0.8 1.8
tabla20 <- svyby(~cpodbere, ~visidentcatonde + pais, disenio_urubra, svymean,
na.rm = TRUE, deff = TRUE)
round(tabla20[, 3:5] * 1, 1)
## cpodbere se DEff.cpodbere
## 1-Convenio particular.Bra 1.7 0.1 1.7
## 2-Publico.Bra 2.4 0.2 1.9
## 3-Nunca fue al dentista.Bra 2.1 0.2 2.0
## 1-Convenio particular.Uru 2.4 0.3 7.1
## 2-Publico.Uru 3.3 0.3 2.7
## 3-Nunca fue al dentista.Uru 3.6 0.4 2.9
tabla21 <- svyby(~cpodbere, ~visiquando + pais, disenio_urubra, svymean, na.rm = TRUE,
deff = TRUE)
round(tabla21[, 3:5] * 1, 1)
## cpodbere se DEff.cpodbere
## 1- menos de 1 a\xf1o.Bra 1.9 0.1 2.3
## 2- m\xe1s de 2 a\xf1os.Bra 1.9 0.1 1.4
## 3- nunca fue.Bra 2.1 0.2 2.0
## 1- menos de 1 a\xf1o.Uru 2.6 0.3 7.0
## 2- m\xe1s de 2 a\xf1os.Uru 3.1 0.5 4.1
## 3- nunca fue.Uru 3.6 0.4 2.8
tabla22 <- svyby(~cpodbere, ~fluorprof + pais, disenio_urubra, svymean, na.rm = TRUE,
deff = TRUE)
round(tabla22[, 3:5] * 1, 2)
## cpodbere se DEff.cpodbere
## 1-Si.Bra 1.83 0.10 2.08
## 2-No.Bra 2.20 0.16 2.15
## 1-Si.Uru 2.80 0.27 4.81
## 2-No.Uru 2.77 0.29 3.79
tabla23 <- svyby(~cpodbere, ~isg20 + pais, disenio_urubra, svymean, na.rm = TRUE,
deff = TRUE)
round(tabla23[, 3:5] * 1, 1)
## cpodbere se DEff.cpodbere
## 1- <=20.Bra 1.7 0.4 1.4
## 2- 20 a 60.Bra 1.6 0.1 2.4
## 3- >=60.Bra 2.8 0.2 1.6
## 1- <=20.Uru 2.6 0.3 7.5
## 2- 20 a 60.Uru 2.9 0.2 2.4
## 3- >=60.Uru 3.7 0.4 1.3
tabla24 <- svyby(~cpodbere, ~isg45 + pais, disenio_urubra, svymean, na.rm = TRUE,
deff = TRUE)
round(tabla24[, 3:5] * 1, 1)
## cpodbere se DEff.cpodbere
## 1- <=45.Bra 1.4 0.1 1.7
## 2- 45 a 60.Bra 1.8 0.1 1.7
## 3- >=60.Bra 2.8 0.2 1.6
## 1- <=45.Uru 2.6 0.3 7.4
## 2- 45 a 60.Uru 3.3 0.4 1.4
## 3- >=60.Uru 3.7 0.4 1.3
tabla25 <- svyby(~cpodbere, ~escolmae13cat + pais, disenio_urubra, svymean,
na.rm = TRUE, deff = TRUE)
round(tabla25[, 3:5] * 1, 1)
## cpodbere se DEff.cpodbere
## 1-college-university.Bra 1.2 0.1 0.9
## 2-high school.Bra 1.5 0.1 1.1
## 3-Elementary School.Bra 2.4 0.1 2.0
## 1-college-university.Uru 1.6 0.3 2.3
## 2-high school.Uru 2.6 0.4 4.5
## 3-Elementary School.Uru 3.2 0.2 3.6
tabla26 <- svyby(~cpodbere, ~tipoesc + pais, disenio_urubra, svymean, na.rm = TRUE,
deff = TRUE)
round(tabla26[, 3:5] * 1, 1)
## cpodbere se DEff.cpodbere
## 1-Particular.Bra 1.2 0.1 1.0
## 2-Publica.Bra 2.2 0.1 2.0
## 1-Particular.Uru 1.9 0.1 0.8
## 2-Publica.Uru 3.1 0.3 6.0
RL Univariada
1) país 2) sexo 3) Escolmaerecat23 4) tipoesc 5) freqescovacat 6) usocreme 7) fluorprof
modelo1.bin <- svyglm(prevbere ~ pais, disenio_urubra, family = quasibinomial())
summary(modelo1.bin)
##
## Call:
## svyglm(formula = prevbere ~ pais, disenio_urubra, family = quasibinomial())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.5548 0.0888 6.25 1.9e-08 ***
## paisUru 0.1216 0.1521 0.80 0.43
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1)
##
## Number of Fisher Scoring iterations: 4
confint(modelo1.bin)
## 2.5 % 97.5 %
## (Intercept) 0.3808 0.7288
## paisUru -0.1765 0.4196
modelo2.bin <- svyglm(prevbere ~ sexoinv, disenio_urubra, family = quasibinomial())
summary(modelo2.bin)
##
## Call:
## svyglm(formula = prevbere ~ sexoinv, disenio_urubra, family = quasibinomial())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.5989 0.1149 5.21 1.4e-06 ***
## sexoinvF 0.0507 0.1334 0.38 0.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1)
##
## Number of Fisher Scoring iterations: 4
confint(modelo2.bin)
## 2.5 % 97.5 %
## (Intercept) 0.3737 0.8241
## sexoinvF -0.2107 0.3122
modelo3.bin <- svyglm(prevbere ~ escolmaerecat23cat, disenio_urubra, family = quasibinomial())
summary(modelo3.bin)
##
## Call:
## svyglm(formula = prevbere ~ escolmaerecat23cat, disenio_urubra,
## family = quasibinomial())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.105 0.162 0.65 0.5176
## escolmaerecat23cat2-high school 0.486 0.162 2.99 0.0037
## escolmaerecat23cat3-elementary school 0.951 0.195 4.89 5.2e-06
##
## (Intercept)
## escolmaerecat23cat2-high school **
## escolmaerecat23cat3-elementary school ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.003)
##
## Number of Fisher Scoring iterations: 4
confint(modelo3.bin)
## 2.5 % 97.5 %
## (Intercept) -0.2120 0.4225
## escolmaerecat23cat2-high school 0.1678 0.8046
## escolmaerecat23cat3-elementary school 0.5700 1.3329
modelo4.bin <- svyglm(prevbere ~ tipoesc, disenio_urubra, family = quasibinomial())
summary(modelo4.bin)
##
## Call:
## svyglm(formula = prevbere ~ tipoesc, disenio_urubra, family = quasibinomial())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0626 0.1159 0.54 0.59
## tipoesc2-Publica 0.7631 0.1348 5.66 2.3e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1)
##
## Number of Fisher Scoring iterations: 4
confint(modelo4.bin)
## 2.5 % 97.5 %
## (Intercept) -0.1646 0.2898
## tipoesc2-Publica 0.4989 1.0272
modelo5.bin <- svyglm(prevbere ~ freqescovacat, disenio_urubra, family = quasibinomial())
summary(modelo5.bin)
##
## Call:
## svyglm(formula = prevbere ~ freqescovacat, disenio_urubra, family = quasibinomial())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.059 0.130 8.13 4.8e-12 ***
## freqescovacat2-veces al d\xeda -0.494 0.138 -3.58 6e-04 ***
## freqescovacat3-veces al d\xeda -0.577 0.141 -4.09 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.001)
##
## Number of Fisher Scoring iterations: 4
confint(modelo5.bin)
## 2.5 % 97.5 %
## (Intercept) 0.8038 1.3144
## freqescovacat2-veces al d\xeda -0.7646 -0.2231
## freqescovacat3-veces al d\xeda -0.8531 -0.3002
modelo6.bin <- svyglm(prevbere ~ usocreme, disenio_urubra, family = quasibinomial())
summary(modelo6.bin)
##
## Call:
## svyglm(formula = prevbere ~ usocreme, disenio_urubra, family = quasibinomial())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6142 0.0825 7.45 9.8e-11 ***
## usocreme2-No 0.4657 0.3953 1.18 0.24
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.002)
##
## Number of Fisher Scoring iterations: 4
confint(modelo6.bin)
## 2.5 % 97.5 %
## (Intercept) 0.4525 0.7759
## usocreme2-No -0.3091 1.2404
modelo7.bin <- svyglm(prevbere ~ fluorprof, disenio_urubra, family = quasibinomial())
summary(modelo7.bin)
##
## Call:
## svyglm(formula = prevbere ~ fluorprof, disenio_urubra, family = quasibinomial())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.5880 0.0966 6.09 3.8e-08 ***
## fluorprof2-No 0.0510 0.1280 0.40 0.69
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9991)
##
## Number of Fisher Scoring iterations: 4
confint(modelo7.bin)
## 2.5 % 97.5 %
## (Intercept) 0.3987 0.7774
## fluorprof2-No -0.1998 0.3018
R POISSON UNIVARIADA
modelo1.poi <- svyglm(cpodbere ~ pais, disenio_urubra, family = quasipoisson())
summary(modelo1.poi)
##
## Call:
## svyglm(formula = cpodbere ~ pais, disenio_urubra, family = quasipoisson())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6716 0.0516 13.02 < 2e-16 ***
## paisUru 0.3590 0.0946 3.79 0.00029 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.184)
##
## Number of Fisher Scoring iterations: 5
confint(modelo1.poi)
## 2.5 % 97.5 %
## (Intercept) 0.5706 0.7727
## paisUru 0.1735 0.5445
modelo2.poi <- svyglm(cpodbere ~ sexoinv, disenio_urubra, family = quasipoisson())
summary(modelo2.poi)
##
## Call:
## svyglm(formula = cpodbere ~ sexoinv, disenio_urubra, family = quasipoisson())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.88867 0.07574 11.73 <2e-16 ***
## sexoinvF 0.00562 0.06931 0.08 0.94
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.312)
##
## Number of Fisher Scoring iterations: 5
confint(modelo2.poi)
## 2.5 % 97.5 %
## (Intercept) 0.7402 1.0371
## sexoinvF -0.1302 0.1415
modelo3.poi <- svyglm(cpodbere ~ escolmaerecat23cat, disenio_urubra, family = quasipoisson())
summary(modelo3.poi)
##
## Call:
## svyglm(formula = cpodbere ~ escolmaerecat23cat, disenio_urubra,
## family = quasipoisson())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.570 0.171 3.34 0.0013
## escolmaerecat23cat2-high school 0.330 0.142 2.33 0.0226
## escolmaerecat23cat3-elementary school 0.471 0.184 2.55 0.0126
##
## (Intercept) **
## escolmaerecat23cat2-high school *
## escolmaerecat23cat3-elementary school *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.276)
##
## Number of Fisher Scoring iterations: 6
confint(modelo3.poi)
## 2.5 % 97.5 %
## (Intercept) 0.23573 0.9041
## escolmaerecat23cat2-high school 0.05181 0.6079
## escolmaerecat23cat3-elementary school 0.10937 0.8317
modelo4.poi <- svyglm(cpodbere ~ tipoesc, disenio_urubra, family = quasipoisson())
summary(modelo4.poi)
##
## Call:
## svyglm(formula = cpodbere ~ tipoesc, disenio_urubra, family = quasipoisson())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4710 0.0801 5.88 9.0e-08 ***
## tipoesc2-Publica 0.5271 0.0972 5.42 6.1e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.189)
##
## Number of Fisher Scoring iterations: 5
confint(modelo4.poi)
## 2.5 % 97.5 %
## (Intercept) 0.3141 0.6280
## tipoesc2-Publica 0.3366 0.7176
modelo5.poi <- svyglm(cpodbere ~ freqescovacat, disenio_urubra, family = quasipoisson())
summary(modelo5.poi)
##
## Call:
## svyglm(formula = cpodbere ~ freqescovacat, disenio_urubra, family = quasipoisson())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0850 0.0589 18.41 <2e-16 ***
## freqescovacat2-veces al d\xeda -0.2295 0.0793 -2.89 0.0049 **
## freqescovacat3-veces al d\xeda -0.2735 0.0945 -2.90 0.0049 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.231)
##
## Number of Fisher Scoring iterations: 5
confint(modelo5.poi)
## 2.5 % 97.5 %
## (Intercept) 0.9695 1.20054
## freqescovacat2-veces al d\xeda -0.3849 -0.07414
## freqescovacat3-veces al d\xeda -0.4587 -0.08837
modelo6.poi <- svyglm(cpodbere ~ usocreme, disenio_urubra, family = quasipoisson())
summary(modelo6.poi)
##
## Call:
## svyglm(formula = cpodbere ~ usocreme, disenio_urubra, family = quasipoisson())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.8884 0.0601 14.79 <2e-16 ***
## usocreme2-No 0.0732 0.1998 0.37 0.72
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.315)
##
## Number of Fisher Scoring iterations: 5
confint(modelo6.poi)
## 2.5 % 97.5 %
## (Intercept) 0.7707 1.0062
## usocreme2-No -0.3184 0.4647
modelo7.poi <- svyglm(cpodbere ~ fluorprof, disenio_urubra, family = quasipoisson())
summary(modelo7.poi)
##
## Call:
## svyglm(formula = cpodbere ~ fluorprof, disenio_urubra, family = quasipoisson())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.8562 0.0728 11.77 <2e-16 ***
## fluorprof2-No 0.0686 0.0790 0.87 0.39
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.314)
##
## Number of Fisher Scoring iterations: 5
confint(modelo7.poi)
## 2.5 % 97.5 %
## (Intercept) 0.71360 0.9989
## fluorprof2-No -0.08614 0.2234