Aca estan las modelos de WHODFMT considerandola como variable binaria Son regresiones uivariadas
load("~/Dropbox/odontologia/maestria_anunziatta/julio2013/datos_tana_25072013.RData")
library(survey)
## Attaching package: 'survey'
## The following object(s) are masked from 'package:graphics':
##
## dotchart
# sabado 27 de julio 2013
# WHO
modelo6.bin <- svyglm(WHODMFTcateg ~ Sexo.rec, diseniopost1, family = quasibinomial())
summary(modelo6.bin)
##
## Call:
## svyglm(formula = WHODMFTcateg ~ Sexo.rec, diseniopost1, family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4935 0.1422 3.47 0.0013 **
## Sexo.rec2-F -0.0272 0.2043 -0.13 0.8949
## ---
## 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(modelo6.bin)
## 2.5 % 97.5 %
## (Intercept) 0.2148 0.7723
## Sexo.rec2-F -0.4277 0.3733
modelo7.bin <- svyglm(WHODMFTcateg ~ Nivel.Socieconomico.rec, diseniopost1,
family = quasibinomial())
summary(modelo7.bin)
##
## Call:
## svyglm(formula = WHODMFTcateg ~ Nivel.Socieconomico.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.198 0.160 -1.23 0.2254
## Nivel.Socieconomico.rec2-MEDIO 0.902 0.218 4.14 0.0002 ***
## Nivel.Socieconomico.rec3-BAJO 1.406 0.212 6.64 8.5e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9904)
##
## Number of Fisher Scoring iterations: 4
confint(modelo7.bin)
## 2.5 % 97.5 %
## (Intercept) -0.5123 0.1167
## Nivel.Socieconomico.rec2-MEDIO 0.4743 1.3288
## Nivel.Socieconomico.rec3-BAJO 0.9910 1.8202
modelo8.bin <- svyglm(WHODMFTcateg ~ Nivel.Educativo.de.la.Madre1.rec, diseniopost1,
family = quasibinomial())
summary(modelo8.bin)
##
## Call:
## svyglm(formula = WHODMFTcateg ~ Nivel.Educativo.de.la.Madre1.rec,
## diseniopost1, family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -0.422 0.160
## Nivel.Educativo.de.la.Madre1.rec2-High School 0.712 0.300
## Nivel.Educativo.de.la.Madre1.rec3-Elementary School 1.281 0.213
## t value Pr(>|t|)
## (Intercept) -2.63 0.012 *
## Nivel.Educativo.de.la.Madre1.rec2-High School 2.37 0.023 *
## Nivel.Educativo.de.la.Madre1.rec3-Elementary School 6.02 6e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.975)
##
## Number of Fisher Scoring iterations: 4
confint(modelo8.bin)
## 2.5 % 97.5 %
## (Intercept) -0.7357 -0.1074
## Nivel.Educativo.de.la.Madre1.rec2-High School 0.1233 1.2999
## Nivel.Educativo.de.la.Madre1.rec3-Elementary School 0.8633 1.6977
modelo9.bin <- svyglm(WHODMFTcateg ~ Tipo.de.Escuela.rec, diseniopost1, family = quasibinomial())
summary(modelo9.bin)
##
## Call:
## svyglm(formula = WHODMFTcateg ~ Tipo.de.Escuela.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0562 0.1509 0.37 0.7113
## Tipo.de.Escuela.rec2-Publica 0.5873 0.1828 3.21 0.0027 **
## ---
## 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(modelo9.bin)
## 2.5 % 97.5 %
## (Intercept) -0.2394 0.3519
## Tipo.de.Escuela.rec2-Publica 0.2292 0.9455
modelo11.bin <- svyglm(WHODMFTcateg ~ AtenOdonto2.rec, diseniopost1, family = quasibinomial())
summary(modelo11.bin)
##
## Call:
## svyglm(formula = WHODMFTcateg ~ AtenOdonto2.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.1089 0.0986 1.10
## AtenOdonto2.rec2-publica 1.0082 0.1662 6.07
## AtenOdonto2.rec3-nunca fue al dentista 0.9248 0.2462 3.76
## Pr(>|t|)
## (Intercept) 0.27688
## AtenOdonto2.rec2-publica 5.1e-07 ***
## AtenOdonto2.rec3-nunca fue al dentista 0.00059 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9809)
##
## Number of Fisher Scoring iterations: 4
confint(modelo11.bin)
## 2.5 % 97.5 %
## (Intercept) -0.08448 0.3022
## AtenOdonto2.rec2-publica 0.68241 1.3339
## AtenOdonto2.rec3-nunca fue al dentista 0.44225 1.4074
modelo12.bin <- svyglm(WHODMFTcateg ~ FrCepDenti.4.rec, diseniopost1, family = quasibinomial())
summary(modelo12.bin)
##
## Call:
## svyglm(formula = WHODMFTcateg ~ FrCepDenti.4.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.286 0.285 4.52
## FrCepDenti.4.rec2-1 vez al dias -0.665 0.346 -1.92
## FrCepDenti.4.rec3-2 veces al dia -0.720 0.296 -2.43
## FrCepDenti.4.rec4-3 veces al dia o mas -0.999 0.298 -3.35
## Pr(>|t|)
## (Intercept) 6.5e-05 ***
## FrCepDenti.4.rec2-1 vez al dias 0.0624 .
## FrCepDenti.4.rec3-2 veces al dia 0.0202 *
## FrCepDenti.4.rec4-3 veces al dia o mas 0.0019 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9731)
##
## Number of Fisher Scoring iterations: 4
confint(modelo12.bin)
## 2.5 % 97.5 %
## (Intercept) 0.7284 1.84396
## FrCepDenti.4.rec2-1 vez al dias -1.3419 0.01281
## FrCepDenti.4.rec3-2 veces al dia -1.3007 -0.13929
## FrCepDenti.4.rec4-3 veces al dia o mas -1.5834 -0.41537
modelo13.bin <- svyglm(WHODMFTcateg ~ UsoDentifrico3.rec, diseniopost1, family = quasibinomial())
summary(modelo13.bin)
##
## Call:
## svyglm(formula = WHODMFTcateg ~ UsoDentifrico3.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.468 0.088 5.32 4.9e-06 ***
## UsoDentifrico3.rec2-No 0.567 0.597 0.95 0.35
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9971)
##
## Number of Fisher Scoring iterations: 4
confint(modelo13.bin)
## 2.5 % 97.5 %
## (Intercept) 0.2953 0.6401
## UsoDentifrico3.rec2-No -0.6029 1.7377
modelo14.bin <- svyglm(WHODMFTcateg ~ FluorProf.rec, diseniopost1, family = quasibinomial())
summary(modelo14.bin)
##
## Call:
## svyglm(formula = WHODMFTcateg ~ FluorProf.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4331 0.1313 3.30 0.0021 **
## FluorProf.rec2-No 0.0426 0.1712 0.25 0.8048
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9298)
##
## Number of Fisher Scoring iterations: 4
confint(modelo14.bin)
## 2.5 % 97.5 %
## (Intercept) 0.1758 0.6903
## FluorProf.rec2-No -0.2930 0.3782
modelo15.bin <- svyglm(WHODMFTcateg ~ RefrColaB.rec, diseniopost1, family = quasibinomial())
summary(modelo15.bin)
##
## Call:
## svyglm(formula = WHODMFTcateg ~ RefrColaB.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.650 0.243 2.67 0.011 *
## RefrColaB.rec2-A veces -0.232 0.294 -0.79 0.434
## RefrColaB.rec3-Nunca o raramente -0.171 0.380 -0.45 0.656
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9886)
##
## Number of Fisher Scoring iterations: 4
confint(modelo15.bin)
## 2.5 % 97.5 %
## (Intercept) 0.1730 1.1272
## RefrColaB.rec2-A veces -0.8080 0.3432
## RefrColaB.rec3-Nunca o raramente -0.9153 0.5743
modelo16.bin <- svyglm(WHODMFTcateg ~ MateDulceB.rec, diseniopost1, family = quasibinomial())
summary(modelo16.bin)
##
## Call:
## svyglm(formula = WHODMFTcateg ~ MateDulceB.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.698 0.279 2.51 0.017 *
## MateDulceB.rec2-A veces 0.182 0.409 0.44 0.659
## MateDulceB.rec3-Nunca o raramente -0.409 0.294 -1.39 0.172
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9722)
##
## Number of Fisher Scoring iterations: 4
confint(modelo16.bin)
## 2.5 % 97.5 %
## (Intercept) 0.1521 1.2449
## MateDulceB.rec2-A veces -0.6199 0.9841
## MateDulceB.rec3-Nunca o raramente -0.9842 0.1668
modelo17.bin <- svyglm(WHODMFTcateg ~ GolosinasB.rec, diseniopost1, family = quasibinomial())
summary(modelo17.bin)
##
## Call:
## svyglm(formula = WHODMFTcateg ~ GolosinasB.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.828 0.209 3.96 0.00033 ***
## GolosinasB.rec2-A veces -0.459 0.243 -1.89 0.06672 .
## GolosinasB.rec3-Nunca o raramente -0.410 0.239 -1.71 0.09526 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9766)
##
## Number of Fisher Scoring iterations: 4
confint(modelo17.bin)
## 2.5 % 97.5 %
## (Intercept) 0.4182 1.23867
## GolosinasB.rec2-A veces -0.9345 0.01718
## GolosinasB.rec3-Nunca o raramente -0.8787 0.05932
modelo19.bin <- svyglm(WHODMFTcateg ~ Masas.DulcesB.rec, diseniopost1, family = quasibinomial())
summary(modelo19.bin)
##
## Call:
## svyglm(formula = WHODMFTcateg ~ Masas.DulcesB.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.981 0.187 5.25 6.5e-06
## Masas.DulcesB.rec2-A veces -0.559 0.193 -2.90 0.0063
## Masas.DulcesB.rec3-Nunca o raramente -0.743 0.322 -2.31 0.0266
##
## (Intercept) ***
## Masas.DulcesB.rec2-A veces **
## Masas.DulcesB.rec3-Nunca o raramente *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9852)
##
## Number of Fisher Scoring iterations: 4
confint(modelo19.bin)
## 2.5 % 97.5 %
## (Intercept) 0.6149 1.3470
## Masas.DulcesB.rec2-A veces -0.9365 -0.1807
## Masas.DulcesB.rec3-Nunca o raramente -1.3741 -0.1125
modelo20.bin <- svyglm(WHODMFTcateg ~ UltmVisita1.rec, diseniopost1, family = quasibinomial())
summary(modelo20.bin)
##
## Call:
## svyglm(formula = WHODMFTcateg ~ UltmVisita1.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.3872 0.0844 4.59
## UltmVisita1.rec2- años atrás 0.0455 0.3167 0.14
## UltmVisita1.rec3-Nunca fue al dentista 0.6898 0.2455 2.81
## Pr(>|t|)
## (Intercept) 5e-05 ***
## UltmVisita1.rec2- años atrás 0.8865
## UltmVisita1.rec3-Nunca fue al dentista 0.0079 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9785)
##
## Number of Fisher Scoring iterations: 4
confint(modelo20.bin)
## 2.5 % 97.5 %
## (Intercept) 0.2218 0.5526
## UltmVisita1.rec2- años atrás -0.5751 0.6661
## UltmVisita1.rec3-Nunca fue al dentista 0.2086 1.1710
modelo21.bin <- svyglm(WHODMFTcateg ~ IGS.rec1, diseniopost1, family = quasibinomial())
summary(modelo21.bin)
##
## Call:
## svyglm(formula = WHODMFTcateg ~ IGS.rec1, diseniopost1, family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.404 0.140 2.89 0.0064 **
## IGS.rec12-De 20 a 60 0.121 0.208 0.58 0.5627
## IGS.rec13-Mas de 60 0.322 0.193 1.67 0.1042
## ---
## 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(modelo21.bin)
## 2.5 % 97.5 %
## (Intercept) 0.13024 0.6771
## IGS.rec12-De 20 a 60 -0.28563 0.5282
## IGS.rec13-Mas de 60 -0.05694 0.7015
modelo22.bin <- svyglm(WHODMFTcateg ~ Nive.Educativo.de.la.Madre2.rec, diseniopost1,
family = quasibinomial())
summary(modelo22.bin)
##
## Call:
## svyglm(formula = WHODMFTcateg ~ Nive.Educativo.de.la.Madre2.rec,
## diseniopost1, family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -0.0728 0.1838
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL 0.5609 0.2193
## Nive.Educativo.de.la.Madre2.rec3-ELEMENTARY SCHOOL 1.2731 0.2542
## t value Pr(>|t|)
## (Intercept) -0.40 0.694
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL 2.56 0.015 *
## Nive.Educativo.de.la.Madre2.rec3-ELEMENTARY SCHOOL 5.01 1.4e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.975)
##
## Number of Fisher Scoring iterations: 4
confint(modelo22.bin)
## 2.5 % 97.5 %
## (Intercept) -0.4331 0.2874
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL 0.1311 0.9908
## Nive.Educativo.de.la.Madre2.rec3-ELEMENTARY SCHOOL 0.7748 1.7713