Aca están las modelos de WHO.modified 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
# WHO.modified
modelo6.poi <- svyglm(WHO.modified ~ Sexo.rec, diseniopost1, family = quasipoisson())
summary(modelo6.poi)
##
## Call:
## svyglm(formula = WHO.modified ~ Sexo.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0580 0.1070 9.89 4.7e-12 ***
## Sexo.rec2-F -0.0267 0.1048 -0.25 0.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.387)
##
## Number of Fisher Scoring iterations: 5
confint(modelo6.poi)
## 2.5 % 97.5 %
## (Intercept) 0.8483 1.2677
## Sexo.rec2-F -0.2321 0.1788
modelo7.poi <- svyglm(WHO.modified ~ Nivel.Socieconomico.rec, diseniopost1,
family = quasipoisson())
summary(modelo7.poi)
##
## Call:
## svyglm(formula = WHO.modified ~ Nivel.Socieconomico.rec, diseniopost1,
## family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.705 0.167 4.21 0.00016 ***
## Nivel.Socieconomico.rec2-MEDIO 0.470 0.144 3.27 0.00235 **
## Nivel.Socieconomico.rec3-BAJO 0.475 0.204 2.33 0.02547 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.275)
##
## Number of Fisher Scoring iterations: 6
confint(modelo7.poi)
## 2.5 % 97.5 %
## (Intercept) 0.37706 1.0336
## Nivel.Socieconomico.rec2-MEDIO 0.18785 0.7513
## Nivel.Socieconomico.rec3-BAJO 0.07508 0.8743
modelo8.poi <- svyglm(WHO.modified ~ Nivel.Educativo.de.la.Madre1.rec, diseniopost1,
family = quasipoisson())
summary(modelo8.poi)
##
## Call:
## svyglm(formula = WHO.modified ~ Nivel.Educativo.de.la.Madre1.rec,
## diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.514 0.240
## Nivel.Educativo.de.la.Madre1.rec2-High School 0.482 0.187
## Nivel.Educativo.de.la.Madre1.rec3-Elementary School 0.659 0.242
## t value Pr(>|t|)
## (Intercept) 2.14 0.0389 *
## Nivel.Educativo.de.la.Madre1.rec2-High School 2.57 0.0142 *
## Nivel.Educativo.de.la.Madre1.rec3-Elementary School 2.72 0.0099 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.229)
##
## Number of Fisher Scoring iterations: 6
confint(modelo8.poi)
## 2.5 % 97.5 %
## (Intercept) 0.04355 0.9844
## Nivel.Educativo.de.la.Madre1.rec2-High School 0.11502 0.8495
## Nivel.Educativo.de.la.Madre1.rec3-Elementary School 0.18406 1.1335
modelo9.poi <- svyglm(WHO.modified ~ Tipo.de.Escuela.rec, diseniopost1, family = quasipoisson())
summary(modelo9.poi)
##
## Call:
## svyglm(formula = WHO.modified ~ 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) 0.6712 0.0837 8.02 1.1e-09 ***
## Tipo.de.Escuela.rec2-Publica 0.4814 0.1211 3.97 3e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.246)
##
## Number of Fisher Scoring iterations: 5
confint(modelo9.poi)
## 2.5 % 97.5 %
## (Intercept) 0.5072 0.8352
## Tipo.de.Escuela.rec2-Publica 0.2440 0.7188
modelo11.poi <- svyglm(WHO.modified ~ AtenOdonto2.rec, diseniopost1, family = quasipoisson())
summary(modelo11.poi)
##
## Call:
## svyglm(formula = WHO.modified ~ AtenOdonto2.rec, diseniopost1,
## family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.914 0.113 8.06
## AtenOdonto2.rec2-publica 0.272 0.140 1.94
## AtenOdonto2.rec3-nunca fue al dentista 0.359 0.168 2.14
## Pr(>|t|)
## (Intercept) 1.1e-09 ***
## AtenOdonto2.rec2-publica 0.060 .
## AtenOdonto2.rec3-nunca fue al dentista 0.039 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.337)
##
## Number of Fisher Scoring iterations: 5
confint(modelo11.poi)
## 2.5 % 97.5 %
## (Intercept) 0.691978 1.1365
## AtenOdonto2.rec2-publica -0.002697 0.5472
## AtenOdonto2.rec3-nunca fue al dentista 0.030006 0.6871
modelo12.poi <- svyglm(WHO.modified ~ FrCepDenti.4.rec, diseniopost1, family = quasipoisson())
summary(modelo12.poi)
##
## Call:
## svyglm(formula = WHO.modified ~ FrCepDenti.4.rec, diseniopost1,
## family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.329 0.162 8.21
## FrCepDenti.4.rec2-1 vez al dias -0.192 0.209 -0.92
## FrCepDenti.4.rec3-2 veces al dia -0.284 0.196 -1.45
## FrCepDenti.4.rec4-3 veces al dia o mas -0.363 0.202 -1.80
## Pr(>|t|)
## (Intercept) 9e-10 ***
## FrCepDenti.4.rec2-1 vez al dias 0.364
## FrCepDenti.4.rec3-2 veces al dia 0.156
## FrCepDenti.4.rec4-3 veces al dia o mas 0.081 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.19)
##
## Number of Fisher Scoring iterations: 5
confint(modelo12.poi)
## 2.5 % 97.5 %
## (Intercept) 1.0118 1.64604
## FrCepDenti.4.rec2-1 vez al dias -0.6025 0.21791
## FrCepDenti.4.rec3-2 veces al dia -0.6681 0.09982
## FrCepDenti.4.rec4-3 veces al dia o mas -0.7593 0.03291
modelo13.poi <- svyglm(WHO.modified ~ UsoDentifrico3.rec, diseniopost1, family = quasipoisson())
summary(modelo13.poi)
##
## Call:
## svyglm(formula = WHO.modified ~ UsoDentifrico3.rec, diseniopost1,
## family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0445 0.0724 14.43 <2e-16 ***
## UsoDentifrico3.rec2-No -0.0946 0.3083 -0.31 0.76
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.376)
##
## Number of Fisher Scoring iterations: 5
confint(modelo13.poi)
## 2.5 % 97.5 %
## (Intercept) 0.9027 1.1863
## UsoDentifrico3.rec2-No -0.6989 0.5097
modelo14.poi <- svyglm(WHO.modified ~ FluorProf.rec, diseniopost1, family = quasipoisson())
summary(modelo14.poi)
##
## Call:
## svyglm(formula = WHO.modified ~ FluorProf.rec, diseniopost1,
## family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0621 0.1022 10.39 1.2e-12 ***
## FluorProf.rec2-No -0.0447 0.1081 -0.41 0.68
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.182)
##
## Number of Fisher Scoring iterations: 5
confint(modelo14.poi)
## 2.5 % 97.5 %
## (Intercept) 0.8617 1.2625
## FluorProf.rec2-No -0.2565 0.1672
modelo15.poi <- svyglm(WHO.modified ~ RefrColaB.rec, diseniopost1, family = quasipoisson())
summary(modelo15.poi)
##
## Call:
## svyglm(formula = WHO.modified ~ RefrColaB.rec, diseniopost1,
## family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.2066 0.0986 12.24 1.4e-14 ***
## RefrColaB.rec2-A veces -0.2035 0.0809 -2.51 0.016 *
## RefrColaB.rec3-Nunca o raramente -0.2658 0.1439 -1.85 0.073 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.303)
##
## Number of Fisher Scoring iterations: 5
confint(modelo15.poi)
## 2.5 % 97.5 %
## (Intercept) 1.0134 1.39975
## RefrColaB.rec2-A veces -0.3621 -0.04485
## RefrColaB.rec3-Nunca o raramente -0.5478 0.01613
modelo16.poi <- svyglm(WHO.modified ~ MateDulceB.rec, diseniopost1, family = quasipoisson())
summary(modelo16.poi)
##
## Call:
## svyglm(formula = WHO.modified ~ MateDulceB.rec, diseniopost1,
## family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.126 0.156 7.21 1.5e-08 ***
## MateDulceB.rec2-A veces 0.129 0.195 0.66 0.51
## MateDulceB.rec3-Nunca o raramente -0.194 0.136 -1.43 0.16
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.222)
##
## Number of Fisher Scoring iterations: 5
confint(modelo16.poi)
## 2.5 % 97.5 %
## (Intercept) 0.8197 1.43198
## MateDulceB.rec2-A veces -0.2536 0.51247
## MateDulceB.rec3-Nunca o raramente -0.4603 0.07196
modelo17.poi <- svyglm(WHO.modified ~ GolosinasB.rec, diseniopost1, family = quasipoisson())
summary(modelo17.poi)
##
## Call:
## svyglm(formula = WHO.modified ~ GolosinasB.rec, diseniopost1,
## family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.214 0.132 9.19 4.4e-11 ***
## GolosinasB.rec2-A veces -0.191 0.106 -1.80 0.080 .
## GolosinasB.rec3-Nunca o raramente -0.370 0.155 -2.39 0.022 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.252)
##
## Number of Fisher Scoring iterations: 5
confint(modelo17.poi)
## 2.5 % 97.5 %
## (Intercept) 0.9554 1.47343
## GolosinasB.rec2-A veces -0.3983 0.01716
## GolosinasB.rec3-Nunca o raramente -0.6734 -0.06602
modelo19.poi <- svyglm(WHO.modified ~ Masas.DulcesB.rec, diseniopost1, family = quasipoisson())
summary(modelo19.poi)
##
## Call:
## svyglm(formula = WHO.modified ~ Masas.DulcesB.rec, diseniopost1,
## family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.2238 0.1112 11.01 3.1e-13
## Masas.DulcesB.rec2-A veces -0.1978 0.0939 -2.11 0.042
## Masas.DulcesB.rec3-Nunca o raramente -0.2908 0.1724 -1.69 0.100
##
## (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 3.312)
##
## Number of Fisher Scoring iterations: 5
confint(modelo19.poi)
## 2.5 % 97.5 %
## (Intercept) 1.0059 1.44168
## Masas.DulcesB.rec2-A veces -0.3818 -0.01369
## Masas.DulcesB.rec3-Nunca o raramente -0.6288 0.04714
modelo20.poi <- svyglm(WHO.modified ~ UltmVisita1.rec, diseniopost1, family = quasipoisson())
summary(modelo20.poi)
##
## Call:
## svyglm(formula = WHO.modified ~ UltmVisita1.rec, diseniopost1,
## family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.9907 0.0999 9.92
## UltmVisita1.rec2- años atrás 0.1389 0.1906 0.73
## UltmVisita1.rec3-Nunca fue al dentista 0.2827 0.1530 1.85
## Pr(>|t|)
## (Intercept) 5.7e-12 ***
## UltmVisita1.rec2- años atrás 0.471
## UltmVisita1.rec3-Nunca fue al dentista 0.073 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.316)
##
## Number of Fisher Scoring iterations: 5
confint(modelo20.poi)
## 2.5 % 97.5 %
## (Intercept) 0.79498 1.1865
## UltmVisita1.rec2- años atrás -0.23461 0.5123
## UltmVisita1.rec3-Nunca fue al dentista -0.01712 0.5825
modelo21.poi <- svyglm(WHO.modified ~ IGS.rec1, diseniopost1, family = quasipoisson())
summary(modelo21.poi)
##
## Call:
## svyglm(formula = WHO.modified ~ IGS.rec1, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.954 0.116 8.23 7.1e-10 ***
## IGS.rec12-De 20 a 60 0.132 0.126 1.05 0.3010
## IGS.rec13-Mas de 60 0.361 0.122 2.95 0.0054 **
## ---
## 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(modelo21.poi)
## 2.5 % 97.5 %
## (Intercept) 0.7270 1.1818
## IGS.rec12-De 20 a 60 -0.1151 0.3801
## IGS.rec13-Mas de 60 0.1214 0.6001
modelo22.poi <- svyglm(WHO.modified ~ Nive.Educativo.de.la.Madre2.rec, diseniopost1,
family = quasipoisson())
summary(modelo22.poi)
##
## Call:
## svyglm(formula = WHO.modified ~ Nive.Educativo.de.la.Madre2.rec,
## diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.763 0.211
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL 0.296 0.184
## Nive.Educativo.de.la.Madre2.rec3-ELEMENTARY SCHOOL 0.492 0.244
## t value Pr(>|t|)
## (Intercept) 3.62 0.00088 ***
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL 1.61 0.11638
## Nive.Educativo.de.la.Madre2.rec3-ELEMENTARY SCHOOL 2.02 0.05077 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 3.255)
##
## Number of Fisher Scoring iterations: 5
confint(modelo22.poi)
## 2.5 % 97.5 %
## (Intercept) 0.35011 1.1768
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL -0.06473 0.6558
## Nive.Educativo.de.la.Madre2.rec3-ELEMENTARY SCHOOL 0.01440 0.9695