El diseño muestral es el que se logró armar considerando los diseños de cada país Finalmente se trabaja con un diseño equivalente a 2 etapas, primera donde las psu son las escuelas, el pais es la variable de estratficacióny se usan como pesos muestrales los que se consideraron en Uruguay (ver diseño de Uruguay y para POA se creó un nuevo vector de peso usando como variables de ajuste distribucion por sexo y por tipo de escuela)
LOs analisis son unificados (no por pais)
names(disenio_urubra$variables) [1] “id” “pais” “ficha”
[4] “eta” “tipoesc” “sexoinv”
[7] “weight” “weight.rec” “numesc”
[10] “numer_esc” “idade” “socioecon4cat”
[13] “socioecon3cat” “escolmae” “escolmae13cat”
[16] “escolmaerecat23cat” “freqescov” “freqescovacat”
[19] “usofio” “freqfio” “usocreme”
[22] “idadcreme” “visidentcatonde” “visiquando”
[25] “fluorprof” “idadefluor” “isg.”
[28] “isg45” “isg20” “cposoms”
[31] “cpodoms” “cpodoms” “cposbere”
[34] “cpodbere” “prevbere” “cposicdas”
[37] “cpodicdas” “previcdas”
# 28 de octubre 2013
load("~/Dropbox/odontologia/maestria_anunziatta/BancoUnidoBrasUru/BRASURU(28102013).RData")
library(survey)
## Attaching package: 'survey'
##
## The following object(s) are masked from 'package:graphics':
##
## dotchart
levels(disenio_urubra$variables$prevoms)
## [1] "1-SinCavi" "2-conCavi"
table(disenio_urubra$variables$cpodoms, disenio_urubra$variables$prevoms)
##
## 1-SinCavi 2-conCavi
## 0 1105 0
## 1 0 495
## 2 0 404
## 3 0 259
## 4 0 217
## 5 0 108
## 6 0 38
## 7 0 22
## 8 0 14
## 9 0 12
## 10 0 3
## 11 0 3
## 14 0 1
## 15 0 1
modelo0.poi <- svyglm(cpodoms ~ pais, disenio_urubra, family = quasipoisson())
summary(modelo0.poi)
##
## Call:
## svyglm(formula = cpodoms ~ pais, disenio_urubra, family = quasipoisson())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3328 0.0623 5.34 8.5e-07 ***
## paisUru 0.1613 0.0924 1.75 0.085 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.224)
##
## Number of Fisher Scoring iterations: 5
confint(modelo0.poi)
## 2.5 % 97.5 %
## (Intercept) 0.21066 0.4550
## paisUru -0.01974 0.3423
exp(modelo0.poi$coefficients)
## (Intercept) paisUru
## 1.395 1.175
modelo1.poi <- svyglm(cpodoms ~ sexoinv, disenio_urubra, family = quasipoisson())
summary(modelo1.poi)
##
## Call:
## svyglm(formula = cpodoms ~ 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) 0.3915 0.0648 6.04 4.6e-08 ***
## sexoinv2-F 0.0732 0.0735 1.00 0.32
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.233)
##
## Number of Fisher Scoring iterations: 5
confint(modelo1.poi)
## 2.5 % 97.5 %
## (Intercept) 0.26442 0.5186
## sexoinv2-F -0.07076 0.2172
exp(modelo1.poi$coefficients)
## (Intercept) sexoinv2-F
## 1.479 1.076
modelo2.poi <- svyglm(cpodoms ~ socioecon4cat, disenio_urubra, family = quasipoisson())
summary(modelo2.poi)
##
## Call:
## svyglm(formula = cpodoms ~ socioecon4cat, disenio_urubra, family = quasipoisson())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.227 0.158 -1.44 0.154
## socioecon4cat2-medio-alto 0.564 0.231 2.45 0.017 *
## socioecon4cat3-medio-bajo 0.795 0.173 4.60 1.6e-05 ***
## socioecon4cat4-bajo 0.922 0.170 5.41 6.6e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.148)
##
## Number of Fisher Scoring iterations: 6
confint(modelo2.poi)
## 2.5 % 97.5 %
## (Intercept) -0.5358 0.08197
## socioecon4cat2-medio-alto 0.1122 1.01644
## socioecon4cat3-medio-bajo 0.4562 1.13317
## socioecon4cat4-bajo 0.5885 1.25613
exp(modelo2.poi$coefficients)
## (Intercept) socioecon4cat2-medio-alto
## 0.797 1.758
## socioecon4cat3-medio-bajo socioecon4cat4-bajo
## 2.214 2.515
modelo3.poi <- svyglm(cpodoms ~ socioecon3cat, disenio_urubra, family = quasipoisson())
summary(modelo3.poi)
##
## Call:
## svyglm(formula = cpodoms ~ socioecon3cat, 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) -0.1135 0.0825 -1.38 0.17
## socioecon3cat2-medio 0.6183 0.0986 6.27 1.8e-08 ***
## socioecon3cat3-bajo 0.8089 0.1034 7.82 1.9e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.138)
##
## Number of Fisher Scoring iterations: 5
confint(modelo3.poi)
## 2.5 % 97.5 %
## (Intercept) -0.2751 0.04817
## socioecon3cat2-medio 0.4250 0.81158
## socioecon3cat3-bajo 0.6062 1.01157
exp(modelo3.poi$coefficients)
## (Intercept) socioecon3cat2-medio socioecon3cat3-bajo
## 0.8927 1.8557 2.2454
modelo4.poi <- svyglm(cpodoms ~ escolmaerecat23cat, disenio_urubra, family = quasipoisson())
summary(modelo4.poi)
##
## Call:
## svyglm(formula = cpodoms ~ escolmaerecat23cat, disenio_urubra,
## family = quasipoisson())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0114 0.1356 0.08 0.9330
## escolmaerecat23cat2-high school 0.4003 0.1337 2.99 0.0037
## escolmaerecat23cat3-elementary school 0.6250 0.1513 4.13 8.9e-05
##
## (Intercept)
## escolmaerecat23cat2-high school **
## escolmaerecat23cat3-elementary school ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.208)
##
## Number of Fisher Scoring iterations: 6
confint(modelo4.poi)
## 2.5 % 97.5 %
## (Intercept) -0.2544 0.2772
## escolmaerecat23cat2-high school 0.1382 0.6623
## escolmaerecat23cat3-elementary school 0.3286 0.9215
exp(modelo4.poi$coefficients)
## (Intercept)
## 1.012
## escolmaerecat23cat2-high school
## 1.492
## escolmaerecat23cat3-elementary school
## 1.868
modelo5.poi <- svyglm(cpodoms ~ escolmae13cat, disenio_urubra, family = quasipoisson())
summary(modelo5.poi)
##
## Call:
## svyglm(formula = cpodoms ~ escolmae13cat, 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) -0.275 0.124 -2.21 0.03 *
## escolmae13cat2-high school 0.507 0.213 2.38 0.02 *
## escolmae13cat3-Elementary School 0.884 0.134 6.62 3.9e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.128)
##
## Number of Fisher Scoring iterations: 6
confint(modelo5.poi)
## 2.5 % 97.5 %
## (Intercept) -0.51843 -0.03082
## escolmae13cat2-high school 0.08954 0.92480
## escolmae13cat3-Elementary School 0.62255 1.14621
exp(modelo5.poi$coefficients)
## (Intercept) escolmae13cat2-high school
## 0.7599 1.6606
## escolmae13cat3-Elementary School
## 2.4215
modelo6.poi <- svyglm(cpodoms ~ freqescovacat, disenio_urubra, family = quasipoisson())
summary(modelo6.poi)
##
## Call:
## svyglm(formula = cpodoms ~ freqescovacat, disenio_urubra, family = quasipoisson())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6176 0.0473 13.07 < 2e-16 ***
## freqescovacat2-veces al d\xeda -0.1737 0.0845 -2.05 0.043 *
## freqescovacat3-veces al d\xeda -0.3166 0.0726 -4.36 3.9e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.197)
##
## Number of Fisher Scoring iterations: 5
confint(modelo6.poi)
## 2.5 % 97.5 %
## (Intercept) 0.5250 0.710275
## freqescovacat2-veces al d\xeda -0.3394 -0.008004
## freqescovacat3-veces al d\xeda -0.4590 -0.174279
exp(modelo6.poi$coefficients)
## (Intercept) freqescovacat2-veces al d\xeda
## 1.8545 0.8406
## freqescovacat3-veces al d\xeda
## 0.7286
modelo7.poi <- svyglm(cpodoms ~ usocreme, disenio_urubra, family = quasipoisson())
summary(modelo7.poi)
##
## Call:
## svyglm(formula = cpodoms ~ 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) 0.4225 0.0499 8.46 1e-12 ***
## usocreme2-No 0.1866 0.1920 0.97 0.33
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.239)
##
## Number of Fisher Scoring iterations: 5
confint(modelo7.poi)
## 2.5 % 97.5 %
## (Intercept) 0.3246 0.5204
## usocreme2-No -0.1898 0.5629
exp(modelo7.poi$coefficients)
## (Intercept) usocreme2-No
## 1.526 1.205
modelo8.poi <- svyglm(cpodoms ~ visidentcatonde, disenio_urubra, family = quasipoisson())
summary(modelo8.poi)
##
## Call:
## svyglm(formula = cpodoms ~ visidentcatonde, disenio_urubra, family = quasipoisson())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.2528 0.0655 3.86
## visidentcatonde2-Publico 0.4146 0.0723 5.73
## visidentcatonde3-Nunca fue al dentista 0.2323 0.0954 2.43
## Pr(>|t|)
## (Intercept) 0.00023 ***
## visidentcatonde2-Publico 1.7e-07 ***
## visidentcatonde3-Nunca fue al dentista 0.01721 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.235)
##
## Number of Fisher Scoring iterations: 6
confint(modelo8.poi)
## 2.5 % 97.5 %
## (Intercept) 0.12444 0.3812
## visidentcatonde2-Publico 0.27292 0.5563
## visidentcatonde3-Nunca fue al dentista 0.04521 0.4193
exp(modelo8.poi$coefficients)
## (Intercept)
## 1.288
## visidentcatonde2-Publico
## 1.514
## visidentcatonde3-Nunca fue al dentista
## 1.261
modelo9.poi <- svyglm(cpodoms ~ visiquando, disenio_urubra, family = quasipoisson())
summary(modelo9.poi)
##
## Call:
## svyglm(formula = cpodoms ~ visiquando, 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) 0.4188 0.0607 6.90 1.1e-09 ***
## visiquando2- m\xe1s de 2 a\xf1os -0.0173 0.1448 -0.12 0.91
## visiquando3- nunca fue 0.0736 0.0923 0.80 0.43
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.249)
##
## Number of Fisher Scoring iterations: 5
confint(modelo9.poi)
## 2.5 % 97.5 %
## (Intercept) 0.2999 0.5377
## visiquando2- m\xe1s de 2 a\xf1os -0.3012 0.2666
## visiquando3- nunca fue -0.1072 0.2545
exp(modelo9.poi$coefficients)
## (Intercept) visiquando2- m\xe1s de 2 a\xf1os
## 1.5202 0.9828
## visiquando3- nunca fue
## 1.0764
modelo10.poi <- svyglm(cpodoms ~ fluorprof, disenio_urubra, family = quasipoisson())
summary(modelo10.poi)
##
## Call:
## svyglm(formula = cpodoms ~ fluorprof, disenio_urubra, family = quasipoisson())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4214 0.0597 7.05 5.6e-10 ***
## fluorprof2-No -0.0118 0.0692 -0.17 0.86
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.246)
##
## Number of Fisher Scoring iterations: 5
confint(modelo10.poi)
## 2.5 % 97.5 %
## (Intercept) 0.3043 0.5385
## fluorprof2-No -0.1475 0.1238
exp(modelo10.poi$coefficients)
## (Intercept) fluorprof2-No
## 1.5240 0.9882
modelo11.poi <- svyglm(cpodoms ~ isg20 + pais, disenio_urubra, family = quasipoisson())
summary(modelo11.poi)
##
## Call:
## svyglm(formula = cpodoms ~ isg20 + 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) 0.157 0.113 1.38 0.17095
## isg202- 20 a 60 0.042 0.105 0.40 0.69003
## isg203- >=60 0.421 0.102 4.12 9.3e-05 ***
## paisUru 0.282 0.082 3.44 0.00095 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.197)
##
## Number of Fisher Scoring iterations: 5
confint(modelo11.poi)
## 2.5 % 97.5 %
## (Intercept) -0.06551 0.3787
## isg202- 20 a 60 -0.16350 0.2474
## isg203- >=60 0.22067 0.6209
## paisUru 0.12105 0.4424
exp(modelo11.poi$coefficients)
## (Intercept) isg202- 20 a 60 isg203- >=60 paisUru
## 1.170 1.043 1.523 1.325
modelo12.poi <- svyglm(cpodoms ~ isg45, disenio_urubra, family = quasipoisson())
summary(modelo12.poi)
##
## Call:
## svyglm(formula = cpodoms ~ isg45, disenio_urubra, family = quasipoisson())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3574 0.0701 5.10 2.3e-06 ***
## isg452- 45 a 60 0.0554 0.0849 0.65 0.51626
## isg453- >=60 0.2938 0.0727 4.04 0.00012 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.229)
##
## Number of Fisher Scoring iterations: 5
confint(modelo12.poi)
## 2.5 % 97.5 %
## (Intercept) 0.2200 0.4947
## isg452- 45 a 60 -0.1111 0.2218
## isg453- >=60 0.1513 0.4362
exp(modelo12.poi$coefficients)
## (Intercept) isg452- 45 a 60 isg453- >=60
## 1.430 1.057 1.341
modelo13.poi <- svyglm(cpodoms ~ tipoesc, disenio_urubra, family = quasipoisson())
summary(modelo13.poi)
##
## Call:
## svyglm(formula = cpodoms ~ 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) -0.0741 0.0747 -0.99 0.32
## tipoesc2-Publica 0.6237 0.0818 7.62 4.4e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.127)
##
## Number of Fisher Scoring iterations: 5
confint(modelo13.poi)
## 2.5 % 97.5 %
## (Intercept) -0.2206 0.07238
## tipoesc2-Publica 0.4633 0.78404
exp(modelo13.poi$coefficients)
## (Intercept) tipoesc2-Publica
## 0.9286 1.8658