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(WHO.modifiedcateg ~ Sexo.rec, diseniopost1, family = quasibinomial())
summary(modelo6.bin)
##
## Call:
## svyglm(formula = WHO.modifiedcateg ~ Sexo.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7679 0.1791 4.29 0.00012 ***
## Sexo.rec2-F -0.0591 0.2073 -0.29 0.77707
## ---
## 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) 0.4168 1.1189
## Sexo.rec2-F -0.4654 0.3472
modelo7.bin <- svyglm(WHO.modifiedcateg ~ Nivel.Socieconomico.rec, diseniopost1,
family = quasibinomial())
summary(modelo7.bin)
##
## Call:
## svyglm(formula = WHO.modifiedcateg ~ Nivel.Socieconomico.rec,
## diseniopost1, family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.144 0.219 0.66 0.51431
## Nivel.Socieconomico.rec2-MEDIO 0.797 0.220 3.62 0.00088 ***
## Nivel.Socieconomico.rec3-BAJO 1.276 0.241 5.28 5.8e-06 ***
## ---
## 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) -0.2851 0.5735
## Nivel.Socieconomico.rec2-MEDIO 0.3653 1.2286
## Nivel.Socieconomico.rec3-BAJO 0.8026 1.7489
modelo8.bin <- svyglm(WHO.modifiedcateg ~ Nivel.Educativo.de.la.Madre1.rec,
diseniopost1, family = quasibinomial())
summary(modelo8.bin)
##
## Call:
## svyglm(formula = WHO.modifiedcateg ~ Nivel.Educativo.de.la.Madre1.rec,
## diseniopost1, family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -0.120 0.200
## Nivel.Educativo.de.la.Madre1.rec2-High School 0.780 0.226
## Nivel.Educativo.de.la.Madre1.rec3-Elementary School 1.185 0.253
## t value Pr(>|t|)
## (Intercept) -0.60 0.5520
## Nivel.Educativo.de.la.Madre1.rec2-High School 3.45 0.0014 **
## Nivel.Educativo.de.la.Madre1.rec3-Elementary School 4.68 3.8e-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(modelo8.bin)
## 2.5 % 97.5 %
## (Intercept) -0.5111 0.2714
## Nivel.Educativo.de.la.Madre1.rec2-High School 0.3368 1.2233
## Nivel.Educativo.de.la.Madre1.rec3-Elementary School 0.6888 1.6815
modelo9.bin <- svyglm(WHO.modifiedcateg ~ Tipo.de.Escuela.rec, diseniopost1,
family = quasibinomial())
summary(modelo9.bin)
##
## Call:
## svyglm(formula = WHO.modifiedcateg ~ 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) 0.283 0.188 1.50 0.14
## Tipo.de.Escuela.rec2-Publica 0.640 0.236 2.71 0.01 *
## ---
## 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) -0.0865 0.6519
## Tipo.de.Escuela.rec2-Publica 0.1768 1.1028
modelo11.bin <- svyglm(WHO.modifiedcateg ~ AtenOdonto2.rec, diseniopost1, family = quasibinomial())
summary(modelo11.bin)
##
## Call:
## svyglm(formula = WHO.modifiedcateg ~ AtenOdonto2.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.415 0.149 2.79
## AtenOdonto2.rec2-publica 0.900 0.202 4.45
## AtenOdonto2.rec3-nunca fue al dentista 0.827 0.292 2.83
## Pr(>|t|)
## (Intercept) 0.0083 **
## AtenOdonto2.rec2-publica 7.7e-05 ***
## AtenOdonto2.rec3-nunca fue al dentista 0.0074 **
## ---
## 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) 0.1233 0.7075
## AtenOdonto2.rec2-publica 0.5031 1.2959
## AtenOdonto2.rec3-nunca fue al dentista 0.2550 1.3995
modelo12.bin <- svyglm(WHO.modifiedcateg ~ FrCepDenti.4.rec, diseniopost1, family = quasibinomial())
summary(modelo12.bin)
##
## Call:
## svyglm(formula = WHO.modifiedcateg ~ FrCepDenti.4.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.430 0.325 4.40
## FrCepDenti.4.rec2-1 vez al dias -0.539 0.369 -1.46
## FrCepDenti.4.rec3-2 veces al dia -0.692 0.346 -2.00
## FrCepDenti.4.rec4-3 veces al dia o mas -0.822 0.299 -2.75
## Pr(>|t|)
## (Intercept) 9.1e-05 ***
## FrCepDenti.4.rec2-1 vez al dias 0.1528
## FrCepDenti.4.rec3-2 veces al dia 0.0530 .
## FrCepDenti.4.rec4-3 veces al dia o mas 0.0094 **
## ---
## 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) 0.7936 2.06626
## FrCepDenti.4.rec2-1 vez al dias -1.2624 0.18433
## FrCepDenti.4.rec3-2 veces al dia -1.3690 -0.01425
## FrCepDenti.4.rec4-3 veces al dia o mas -1.4080 -0.23516
modelo13.bin <- svyglm(WHO.modifiedcateg ~ UsoDentifrico3.rec, diseniopost1,
family = quasibinomial())
summary(modelo13.bin)
##
## Call:
## svyglm(formula = WHO.modifiedcateg ~ UsoDentifrico3.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.730 0.116 6.27 2.4e-07 ***
## UsoDentifrico3.rec2-No 0.366 0.624 0.59 0.56
## ---
## 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) 0.5023 0.9587
## UsoDentifrico3.rec2-No -0.8583 1.5895
modelo14.bin <- svyglm(WHO.modifiedcateg ~ FluorProf.rec, diseniopost1, family = quasibinomial())
summary(modelo14.bin)
##
## Call:
## svyglm(formula = WHO.modifiedcateg ~ FluorProf.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.722 0.170 4.26 0.00013 ***
## FluorProf.rec2-No -0.014 0.202 -0.07 0.94512
## ---
## 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) 0.3895 1.0542
## FluorProf.rec2-No -0.4097 0.3817
modelo15.bin <- svyglm(WHO.modifiedcateg ~ RefrColaB.rec, diseniopost1, family = quasibinomial())
summary(modelo15.bin)
##
## Call:
## svyglm(formula = WHO.modifiedcateg ~ RefrColaB.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.860 0.228 3.78 0.00056 ***
## RefrColaB.rec2-A veces -0.160 0.268 -0.60 0.55361
## RefrColaB.rec3-Nunca o raramente -0.141 0.343 -0.41 0.68335
## ---
## 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) 0.4134 1.3058
## RefrColaB.rec2-A veces -0.6853 0.3649
## RefrColaB.rec3-Nunca o raramente -0.8127 0.5308
modelo16.bin <- svyglm(WHO.modifiedcateg ~ MateDulceB.rec, diseniopost1, family = quasibinomial())
summary(modelo16.bin)
##
## Call:
## svyglm(formula = WHO.modifiedcateg ~ MateDulceB.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.964 0.238 4.06 0.00025 ***
## MateDulceB.rec2-A veces 0.103 0.412 0.25 0.80506
## MateDulceB.rec3-Nunca o raramente -0.378 0.265 -1.43 0.16114
## ---
## 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) 0.4982 1.4296
## MateDulceB.rec2-A veces -0.7058 0.9109
## MateDulceB.rec3-Nunca o raramente -0.8973 0.1403
modelo17.bin <- svyglm(WHO.modifiedcateg ~ GolosinasB.rec, diseniopost1, family = quasibinomial())
summary(modelo17.bin)
##
## Call:
## svyglm(formula = WHO.modifiedcateg ~ GolosinasB.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.048 0.260 4.03 0.00027 ***
## GolosinasB.rec2-A veces -0.401 0.250 -1.61 0.11641
## GolosinasB.rec3-Nunca o raramente -0.363 0.250 -1.45 0.15530
## ---
## 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) 0.5378 1.55790
## GolosinasB.rec2-A veces -0.8909 0.08798
## GolosinasB.rec3-Nunca o raramente -0.8523 0.12727
modelo19.bin <- svyglm(WHO.modifiedcateg ~ Masas.DulcesB.rec, diseniopost1,
family = quasibinomial())
summary(modelo19.bin)
##
## Call:
## svyglm(formula = WHO.modifiedcateg ~ Masas.DulcesB.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.219 0.226 5.38 4.3e-06
## Masas.DulcesB.rec2-A veces -0.506 0.220 -2.30 0.027
## Masas.DulcesB.rec3-Nunca o raramente -0.789 0.330 -2.39 0.022
##
## (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) 0.7748 1.66223
## Masas.DulcesB.rec2-A veces -0.9373 -0.07406
## Masas.DulcesB.rec3-Nunca o raramente -1.4362 -0.14179
modelo20.bin <- svyglm(WHO.modifiedcateg ~ UltmVisita1.rec, diseniopost1, family = quasibinomial())
summary(modelo20.bin)
##
## Call:
## svyglm(formula = WHO.modifiedcateg ~ UltmVisita1.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.613 0.125 4.90
## UltmVisita1.rec2- años atrás 0.353 0.243 1.45
## UltmVisita1.rec3-Nunca fue al dentista 0.679 0.298 2.28
## Pr(>|t|)
## (Intercept) 1.9e-05 ***
## UltmVisita1.rec2- años atrás 0.155
## UltmVisita1.rec3-Nunca fue al dentista 0.029 *
## ---
## 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) 0.36764 0.8585
## UltmVisita1.rec2- años atrás -0.12324 0.8301
## UltmVisita1.rec3-Nunca fue al dentista 0.09485 1.2624
modelo21.bin <- svyglm(WHO.modifiedcateg ~ IGS.rec1, diseniopost1, family = quasibinomial())
summary(modelo21.bin)
##
## Call:
## svyglm(formula = WHO.modifiedcateg ~ IGS.rec1, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6898 0.1828 3.77 0.00056 ***
## IGS.rec12-De 20 a 60 0.0504 0.2529 0.20 0.84301
## IGS.rec13-Mas de 60 0.3930 0.2293 1.71 0.09487 .
## ---
## 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) 0.33154 1.0480
## IGS.rec12-De 20 a 60 -0.44528 0.5462
## IGS.rec13-Mas de 60 -0.05636 0.8424
modelo22.bin <- svyglm(WHO.modifiedcateg ~ Nive.Educativo.de.la.Madre2.rec,
diseniopost1, family = quasibinomial())
summary(modelo22.bin)
##
## Call:
## svyglm(formula = WHO.modifiedcateg ~ Nive.Educativo.de.la.Madre2.rec,
## diseniopost1, family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.225 0.265
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL 0.556 0.260
## Nive.Educativo.de.la.Madre2.rec3-ELEMENTARY SCHOOL 1.101 0.338
## t value Pr(>|t|)
## (Intercept) 0.85 0.4022
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL 2.13 0.0395 *
## Nive.Educativo.de.la.Madre2.rec3-ELEMENTARY SCHOOL 3.26 0.0024 **
## ---
## 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) -0.29512 0.7448
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL 0.04556 1.0665
## Nive.Educativo.de.la.Madre2.rec3-ELEMENTARY SCHOOL 0.43828 1.7646