Aca están las modelos de ICDAS considerandola como variable de conteo Son regresiones univariadas
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
# domingo 28 de julio 2013
# ICDAS
modelo6.poi <- svyglm(ICDAS ~ Sexo.rec, diseniopost1, family = quasipoisson())
summary(modelo6.poi)
##
## Call:
## svyglm(formula = ICDAS ~ Sexo.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.6819 0.0595 28.27 <2e-16 ***
## Sexo.rec2-F 0.0742 0.0975 0.76 0.45
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.454)
##
## Number of Fisher Scoring iterations: 5
confint(modelo6.poi)
## 2.5 % 97.5 %
## (Intercept) 1.5653 1.7985
## Sexo.rec2-F -0.1169 0.2653
modelo7.poi <- svyglm(ICDAS ~ Nivel.Socieconomico.rec, diseniopost1, family = quasipoisson())
summary(modelo7.poi)
##
## Call:
## svyglm(formula = ICDAS ~ Nivel.Socieconomico.rec, diseniopost1,
## family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.3791 0.0442 31.22 < 2e-16 ***
## Nivel.Socieconomico.rec2-MEDIO 0.4335 0.0485 8.93 9.0e-11 ***
## Nivel.Socieconomico.rec3-BAJO 0.5476 0.0603 9.09 5.8e-11 ***
## ---
## 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: 5
confint(modelo7.poi)
## 2.5 % 97.5 %
## (Intercept) 1.2925 1.4656
## Nivel.Socieconomico.rec2-MEDIO 0.3384 0.5286
## Nivel.Socieconomico.rec3-BAJO 0.4295 0.6657
modelo8.poi <- svyglm(ICDAS ~ Nivel.Educativo.de.la.Madre1.rec, diseniopost1,
family = quasipoisson())
summary(modelo8.poi)
##
## Call:
## svyglm(formula = ICDAS ~ Nivel.Educativo.de.la.Madre1.rec, diseniopost1,
## family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 1.2822 0.0870
## Nivel.Educativo.de.la.Madre1.rec2-High School 0.2822 0.0897
## Nivel.Educativo.de.la.Madre1.rec3-Elementary School 0.5805 0.0995
## t value Pr(>|t|)
## (Intercept) 14.73 < 2e-16 ***
## Nivel.Educativo.de.la.Madre1.rec2-High School 3.15 0.0033 **
## Nivel.Educativo.de.la.Madre1.rec3-Elementary School 5.83 1.1e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.313)
##
## Number of Fisher Scoring iterations: 5
confint(modelo8.poi)
## 2.5 % 97.5 %
## (Intercept) 1.1116 1.4528
## Nivel.Educativo.de.la.Madre1.rec2-High School 0.1064 0.4581
## Nivel.Educativo.de.la.Madre1.rec3-Elementary School 0.3854 0.7756
modelo9.poi <- svyglm(ICDAS ~ Tipo.de.Escuela.rec, diseniopost1, family = quasipoisson())
summary(modelo9.poi)
##
## Call:
## svyglm(formula = ICDAS ~ Tipo.de.Escuela.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.4212 0.0856 16.60 <2e-16 ***
## Tipo.de.Escuela.rec2-Publica 0.3866 0.0938 4.12 2e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.349)
##
## Number of Fisher Scoring iterations: 5
confint(modelo9.poi)
## 2.5 % 97.5 %
## (Intercept) 1.2533 1.5890
## Tipo.de.Escuela.rec2-Publica 0.2028 0.5704
modelo11.poi <- svyglm(ICDAS ~ AtenOdonto2.rec, diseniopost1, family = quasipoisson())
summary(modelo11.poi)
##
## Call:
## svyglm(formula = ICDAS ~ AtenOdonto2.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.5686 0.0410 38.27
## AtenOdonto2.rec2-publica 0.2921 0.0739 3.95
## AtenOdonto2.rec3-nunca fue al dentista 0.4161 0.0748 5.56
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## AtenOdonto2.rec2-publica 0.00034 ***
## AtenOdonto2.rec3-nunca fue al dentista 2.5e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.396)
##
## Number of Fisher Scoring iterations: 5
confint(modelo11.poi)
## 2.5 % 97.5 %
## (Intercept) 1.4883 1.6489
## AtenOdonto2.rec2-publica 0.1472 0.4370
## AtenOdonto2.rec3-nunca fue al dentista 0.2695 0.5627
modelo12.poi <- svyglm(ICDAS ~ FrCepDenti.4.rec, diseniopost1, family = quasipoisson())
summary(modelo12.poi)
##
## Call:
## svyglm(formula = ICDAS ~ FrCepDenti.4.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.8874 0.0825 22.87
## FrCepDenti.4.rec2-1 vez al dias -0.1763 0.1216 -1.45
## FrCepDenti.4.rec3-2 veces al dia -0.1538 0.0890 -1.73
## FrCepDenti.4.rec4-3 veces al dia o mas -0.2232 0.0771 -2.90
## Pr(>|t|)
## (Intercept) <2e-16 ***
## FrCepDenti.4.rec2-1 vez al dias 0.1556
## FrCepDenti.4.rec3-2 veces al dia 0.0924 .
## FrCepDenti.4.rec4-3 veces al dia o mas 0.0064 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.364)
##
## Number of Fisher Scoring iterations: 5
confint(modelo12.poi)
## 2.5 % 97.5 %
## (Intercept) 1.7256 2.04918
## FrCepDenti.4.rec2-1 vez al dias -0.4146 0.06195
## FrCepDenti.4.rec3-2 veces al dia -0.3283 0.02058
## FrCepDenti.4.rec4-3 veces al dia o mas -0.3742 -0.07215
modelo13.poi <- svyglm(ICDAS ~ UsoDentifrico3.rec, diseniopost1, family = quasipoisson())
summary(modelo13.poi)
##
## Call:
## svyglm(formula = ICDAS ~ UsoDentifrico3.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7183 0.0346 49.72 <2e-16 ***
## UsoDentifrico3.rec2-No -0.1400 0.2158 -0.65 0.52
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.458)
##
## Number of Fisher Scoring iterations: 5
confint(modelo13.poi)
## 2.5 % 97.5 %
## (Intercept) 1.6505 1.7860
## UsoDentifrico3.rec2-No -0.5629 0.2829
modelo14.poi <- svyglm(ICDAS ~ FluorProf.rec, diseniopost1, family = quasipoisson())
summary(modelo14.poi)
##
## Call:
## svyglm(formula = ICDAS ~ FluorProf.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.70569 0.04699 36.30 <2e-16 ***
## FluorProf.rec2-No 0.00295 0.06802 0.04 0.97
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.195)
##
## Number of Fisher Scoring iterations: 5
confint(modelo14.poi)
## 2.5 % 97.5 %
## (Intercept) 1.6136 1.7978
## FluorProf.rec2-No -0.1304 0.1363
modelo15.poi <- svyglm(ICDAS ~ RefrColaB.rec, diseniopost1, family = quasipoisson())
summary(modelo15.poi)
##
## Call:
## svyglm(formula = ICDAS ~ RefrColaB.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7979 0.0702 25.60 <2e-16 ***
## RefrColaB.rec2-A veces -0.1375 0.0783 -1.76 0.087 .
## RefrColaB.rec3-Nunca o raramente 0.0100 0.1018 0.10 0.922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.408)
##
## Number of Fisher Scoring iterations: 5
confint(modelo15.poi)
## 2.5 % 97.5 %
## (Intercept) 1.6603 1.93557
## RefrColaB.rec2-A veces -0.2909 0.01595
## RefrColaB.rec3-Nunca o raramente -0.1895 0.20956
modelo16.poi <- svyglm(ICDAS ~ MateDulceB.rec, diseniopost1, family = quasipoisson())
summary(modelo16.poi)
##
## Call:
## svyglm(formula = ICDAS ~ MateDulceB.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.9439 0.0682 28.52 < 2e-16 ***
## MateDulceB.rec2-A veces -0.0911 0.1073 -0.85 0.4
## MateDulceB.rec3-Nunca o raramente -0.3577 0.0639 -5.60 2.2e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.251)
##
## Number of Fisher Scoring iterations: 5
confint(modelo16.poi)
## 2.5 % 97.5 %
## (Intercept) 1.8103 2.0775
## MateDulceB.rec2-A veces -0.3014 0.1193
## MateDulceB.rec3-Nunca o raramente -0.4828 -0.2325
modelo17.poi <- svyglm(ICDAS ~ GolosinasB.rec, diseniopost1, family = quasipoisson())
summary(modelo17.poi)
##
## Call:
## svyglm(formula = ICDAS ~ GolosinasB.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8129 0.0615 29.47 <2e-16 ***
## GolosinasB.rec2-A veces -0.1111 0.0701 -1.59 0.12
## GolosinasB.rec3-Nunca o raramente -0.1991 0.1235 -1.61 0.12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.359)
##
## Number of Fisher Scoring iterations: 5
confint(modelo17.poi)
## 2.5 % 97.5 %
## (Intercept) 1.6923 1.93344
## GolosinasB.rec2-A veces -0.2485 0.02628
## GolosinasB.rec3-Nunca o raramente -0.4411 0.04284
modelo19.poi <- svyglm(ICDAS ~ Masas.DulcesB.rec, diseniopost1, family = quasipoisson())
summary(modelo19.poi)
##
## Call:
## svyglm(formula = ICDAS ~ Masas.DulcesB.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8722 0.0591 31.67 <2e-16
## Masas.DulcesB.rec2-A veces -0.1986 0.0704 -2.82 0.0076
## Masas.DulcesB.rec3-Nunca o raramente -0.1644 0.1125 -1.46 0.1525
##
## (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 quasipoisson family taken to be 3.385)
##
## Number of Fisher Scoring iterations: 5
confint(modelo19.poi)
## 2.5 % 97.5 %
## (Intercept) 1.7564 1.98808
## Masas.DulcesB.rec2-A veces -0.3365 -0.06062
## Masas.DulcesB.rec3-Nunca o raramente -0.3849 0.05617
modelo20.poi <- svyglm(ICDAS ~ UltmVisita1.rec, diseniopost1, family = quasipoisson())
summary(modelo20.poi)
##
## Call:
## svyglm(formula = ICDAS ~ UltmVisita1.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.6414 0.0402 40.88
## UltmVisita1.rec2- años atrás 0.2062 0.0922 2.24
## UltmVisita1.rec3-Nunca fue al dentista 0.3375 0.0665 5.08
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## UltmVisita1.rec2- años atrás 0.031 *
## UltmVisita1.rec3-Nunca fue al dentista 1.1e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.401)
##
## Number of Fisher Scoring iterations: 5
confint(modelo20.poi)
## 2.5 % 97.5 %
## (Intercept) 1.56274 1.7201
## UltmVisita1.rec2- años atrás 0.02553 0.3869
## UltmVisita1.rec3-Nunca fue al dentista 0.20718 0.4678
modelo21.poi <- svyglm(ICDAS ~ IGS.rec1, diseniopost1, family = quasipoisson())
summary(modelo21.poi)
##
## Call:
## svyglm(formula = ICDAS ~ IGS.rec1, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.70945 0.06040 28.30 <2e-16 ***
## IGS.rec12-De 20 a 60 -0.00301 0.09702 -0.03 0.98
## IGS.rec13-Mas de 60 0.13223 0.08248 1.60 0.12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.46)
##
## Number of Fisher Scoring iterations: 5
confint(modelo21.poi)
## 2.5 % 97.5 %
## (Intercept) 1.59106 1.8278
## IGS.rec12-De 20 a 60 -0.19317 0.1872
## IGS.rec13-Mas de 60 -0.02943 0.2939
modelo22.poi <- svyglm(ICDAS ~ Nive.Educativo.de.la.Madre2.rec, diseniopost1,
family = quasipoisson())
summary(modelo22.poi)
##
## Call:
## svyglm(formula = ICDAS ~ Nive.Educativo.de.la.Madre2.rec, diseniopost1,
## family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 1.3870 0.0747
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL 0.3426 0.0873
## Nive.Educativo.de.la.Madre2.rec3-ELEMENTARY SCHOOL 0.5739 0.0934
## t value Pr(>|t|)
## (Intercept) 18.56 < 2e-16 ***
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL 3.92 0.00036 ***
## Nive.Educativo.de.la.Madre2.rec3-ELEMENTARY SCHOOL 6.14 4e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.341)
##
## Number of Fisher Scoring iterations: 5
confint(modelo22.poi)
## 2.5 % 97.5 %
## (Intercept) 1.2405 1.5335
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL 0.1715 0.5136
## Nive.Educativo.de.la.Madre2.rec3-ELEMENTARY SCHOOL 0.3907 0.7570