Aca estan las modelos de ICDAS categ 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
# viernes 2 de agosto 2013
# WHO
modelo6.bin <- svyglm(ICDAS.categ ~ Sexo.rec, diseniopost1, family = quasibinomial())
summary(modelo6.bin)
##
## Call:
## svyglm(formula = ICDAS.categ ~ Sexo.rec, diseniopost1, family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.4927 0.1778 8.40 3.5e-10 ***
## Sexo.rec2-F 0.0216 0.2334 0.09 0.93
## ---
## 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) 1.1443 1.8411
## Sexo.rec2-F -0.4359 0.4792
modelo7.bin <- svyglm(ICDAS.categ ~ Nivel.Socieconomico.rec, diseniopost1, family = quasibinomial())
summary(modelo7.bin)
##
## Call:
## svyglm(formula = ICDAS.categ ~ Nivel.Socieconomico.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.834 0.156 5.36 4.7e-06 ***
## Nivel.Socieconomico.rec2-MEDIO 0.959 0.193 4.98 1.5e-05 ***
## Nivel.Socieconomico.rec3-BAJO 1.633 0.254 6.44 1.6e-07 ***
## ---
## 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: 5
confint(modelo7.bin)
## 2.5 % 97.5 %
## (Intercept) 0.5291 1.140
## Nivel.Socieconomico.rec2-MEDIO 0.5812 1.336
## Nivel.Socieconomico.rec3-BAJO 1.1358 2.130
modelo8.bin <- svyglm(ICDAS.categ ~ Nivel.Educativo.de.la.Madre1.rec, diseniopost1,
family = quasibinomial())
summary(modelo8.bin)
##
## Call:
## svyglm(formula = ICDAS.categ ~ Nivel.Educativo.de.la.Madre1.rec,
## diseniopost1, family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.523 0.170
## Nivel.Educativo.de.la.Madre1.rec2-High School 0.860 0.253
## Nivel.Educativo.de.la.Madre1.rec3-Elementary School 1.420 0.262
## t value Pr(>|t|)
## (Intercept) 3.07 0.0040 **
## Nivel.Educativo.de.la.Madre1.rec2-High School 3.39 0.0017 **
## Nivel.Educativo.de.la.Madre1.rec3-Elementary School 5.42 3.8e-06 ***
## ---
## 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) 0.1894 0.8573
## Nivel.Educativo.de.la.Madre1.rec2-High School 0.3633 1.3569
## Nivel.Educativo.de.la.Madre1.rec3-Elementary School 0.9070 1.9338
modelo9.bin <- svyglm(ICDAS.categ ~ Tipo.de.Escuela.rec, diseniopost1, family = quasibinomial())
summary(modelo9.bin)
##
## Call:
## svyglm(formula = ICDAS.categ ~ 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) 1.013 0.212 4.79 2.6e-05 ***
## Tipo.de.Escuela.rec2-Publica 0.711 0.249 2.86 0.0069 **
## ---
## 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) 0.5984 1.428
## Tipo.de.Escuela.rec2-Publica 0.2237 1.199
modelo11.bin <- svyglm(ICDAS.categ ~ AtenOdonto2.rec, diseniopost1, family = quasibinomial())
summary(modelo11.bin)
##
## Call:
## svyglm(formula = ICDAS.categ ~ AtenOdonto2.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.070 0.129 8.30
## AtenOdonto2.rec2-publica 1.378 0.261 5.29
## AtenOdonto2.rec3-nunca fue al dentista 1.698 0.459 3.70
## Pr(>|t|)
## (Intercept) 5.7e-10 ***
## AtenOdonto2.rec2-publica 5.8e-06 ***
## AtenOdonto2.rec3-nunca fue al dentista 7e-04 ***
## ---
## 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: 5
confint(modelo11.bin)
## 2.5 % 97.5 %
## (Intercept) 0.8173 1.323
## AtenOdonto2.rec2-publica 0.8669 1.888
## AtenOdonto2.rec3-nunca fue al dentista 0.7982 2.598
modelo12.bin <- svyglm(ICDAS.categ ~ FrCepDenti.4.rec, diseniopost1, family = quasibinomial())
summary(modelo12.bin)
##
## Call:
## svyglm(formula = ICDAS.categ ~ FrCepDenti.4.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 2.195 0.472 4.65
## FrCepDenti.4.rec2-1 vez al dias -0.450 0.451 -1.00
## FrCepDenti.4.rec3-2 veces al dia -0.680 0.508 -1.34
## FrCepDenti.4.rec4-3 veces al dia o mas -0.863 0.486 -1.78
## Pr(>|t|)
## (Intercept) 4.3e-05 ***
## FrCepDenti.4.rec2-1 vez al dias 0.325
## FrCepDenti.4.rec3-2 veces al dia 0.189
## FrCepDenti.4.rec4-3 veces al dia o mas 0.084 .
## ---
## 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) 1.270 3.11980
## FrCepDenti.4.rec2-1 vez al dias -1.334 0.43388
## FrCepDenti.4.rec3-2 veces al dia -1.675 0.31483
## FrCepDenti.4.rec4-3 veces al dia o mas -1.815 0.08916
modelo13.bin <- svyglm(ICDAS.categ ~ UsoDentifrico3.rec, diseniopost1, family = quasibinomial())
summary(modelo13.bin)
##
## Call:
## svyglm(formula = ICDAS.categ ~ UsoDentifrico3.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.4975 0.1121 13.36 6.1e-16 ***
## UsoDentifrico3.rec2-No 0.0575 0.8224 0.07 0.94
## ---
## 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) 1.278 1.717
## UsoDentifrico3.rec2-No -1.554 1.669
modelo14.bin <- svyglm(ICDAS.categ ~ FluorProf.rec, diseniopost1, family = quasibinomial())
summary(modelo14.bin)
##
## Call:
## svyglm(formula = ICDAS.categ ~ FluorProf.rec, diseniopost1, family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.4476 0.1787 8.10 8.4e-10 ***
## FluorProf.rec2-No 0.0678 0.2445 0.28 0.78
## ---
## 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) 1.0973 1.7979
## FluorProf.rec2-No -0.4114 0.5471
modelo15.bin <- svyglm(ICDAS.categ ~ RefrColaB.rec, diseniopost1, family = quasibinomial())
summary(modelo15.bin)
##
## Call:
## svyglm(formula = ICDAS.categ ~ RefrColaB.rec, diseniopost1, family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.586 0.256 6.20 3.4e-07 ***
## RefrColaB.rec2-A veces -0.153 0.311 -0.49 0.62
## RefrColaB.rec3-Nunca o raramente 0.103 0.340 0.30 0.76
## ---
## 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) 1.0843 2.0879
## RefrColaB.rec2-A veces -0.7626 0.4561
## RefrColaB.rec3-Nunca o raramente -0.5625 0.7692
modelo16.bin <- svyglm(ICDAS.categ ~ MateDulceB.rec, diseniopost1, family = quasibinomial())
summary(modelo16.bin)
##
## Call:
## svyglm(formula = ICDAS.categ ~ MateDulceB.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.348 0.424 5.54 2.6e-06 ***
## MateDulceB.rec2-A veces -0.142 0.549 -0.26 0.798
## MateDulceB.rec3-Nunca o raramente -1.172 0.444 -2.64 0.012 *
## ---
## 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) 1.518 3.1788
## MateDulceB.rec2-A veces -1.219 0.9351
## MateDulceB.rec3-Nunca o raramente -2.043 -0.3019
modelo17.bin <- svyglm(ICDAS.categ ~ GolosinasB.rec, diseniopost1, family = quasibinomial())
summary(modelo17.bin)
##
## Call:
## svyglm(formula = ICDAS.categ ~ GolosinasB.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.810 0.284 6.37 2e-07 ***
## GolosinasB.rec2-A veces -0.372 0.273 -1.36 0.18
## GolosinasB.rec3-Nunca o raramente -0.440 0.357 -1.23 0.23
## ---
## 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) 1.2527 2.3664
## GolosinasB.rec2-A veces -0.9067 0.1627
## GolosinasB.rec3-Nunca o raramente -1.1398 0.2598
modelo19.bin <- svyglm(ICDAS.categ ~ Masas.DulcesB.rec, diseniopost1, family = quasibinomial())
summary(modelo19.bin)
##
## Call:
## svyglm(formula = ICDAS.categ ~ Masas.DulcesB.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.087 0.229 9.13 5.1e-11
## Masas.DulcesB.rec2-A veces -0.688 0.256 -2.68 0.011
## Masas.DulcesB.rec3-Nunca o raramente -0.668 0.334 -2.00 0.053
##
## (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) 1.639 2.53528
## Masas.DulcesB.rec2-A veces -1.190 -0.18565
## Masas.DulcesB.rec3-Nunca o raramente -1.324 -0.01301
modelo20.bin <- svyglm(ICDAS.categ ~ UltmVisita1.rec, diseniopost1, family = quasibinomial())
summary(modelo20.bin)
##
## Call:
## svyglm(formula = ICDAS.categ ~ UltmVisita1.rec, diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.257 0.116 10.84
## UltmVisita1.rec2- años atrás 1.266 0.331 3.82
## UltmVisita1.rec3-Nunca fue al dentista 1.483 0.445 3.33
## Pr(>|t|)
## (Intercept) 4.8e-13 ***
## UltmVisita1.rec2- años atrás 0.00049 ***
## UltmVisita1.rec3-Nunca fue al dentista 0.00196 **
## ---
## 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: 5
confint(modelo20.bin)
## 2.5 % 97.5 %
## (Intercept) 1.0300 1.485
## UltmVisita1.rec2- años atrás 0.6171 1.916
## UltmVisita1.rec3-Nunca fue al dentista 0.6109 2.355
modelo21.bin <- svyglm(ICDAS.categ ~ IGS.rec1, diseniopost1, family = quasibinomial())
summary(modelo21.bin)
##
## Call:
## svyglm(formula = ICDAS.categ ~ IGS.rec1, diseniopost1, family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.4832 0.1988 7.46 6.9e-09 ***
## IGS.rec12-De 20 a 60 0.0336 0.2883 0.12 0.91
## IGS.rec13-Mas de 60 0.0683 0.2415 0.28 0.78
## ---
## 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) 1.0936 1.8729
## IGS.rec12-De 20 a 60 -0.5314 0.5986
## IGS.rec13-Mas de 60 -0.4051 0.5416
modelo22.bin <- svyglm(ICDAS.categ ~ Nive.Educativo.de.la.Madre2.rec, diseniopost1,
family = quasibinomial())
summary(modelo22.bin)
##
## Call:
## svyglm(formula = ICDAS.categ ~ Nive.Educativo.de.la.Madre2.rec,
## diseniopost1, family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.832 0.195
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL 0.744 0.248
## Nive.Educativo.de.la.Madre2.rec3-ELEMENTARY SCHOOL 1.531 0.338
## t value Pr(>|t|)
## (Intercept) 4.26 0.00014 ***
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL 2.99 0.00488 **
## Nive.Educativo.de.la.Madre2.rec3-ELEMENTARY SCHOOL 4.53 6e-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) 0.4490 1.215
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL 0.2571 1.231
## Nive.Educativo.de.la.Madre2.rec3-ELEMENTARY SCHOOL 0.8677 2.194