Aca están las modelos de WHODFMT 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
# sabado 27 de julio 2013
# WHO DMFT
modelo6.poi <- svyglm(WHODMFT ~ Sexo.rec, diseniopost1, family = quasipoisson())
summary(modelo6.poi)
##
## Call:
## svyglm(formula = WHODMFT ~ Sexo.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4799 0.0869 5.52 2.6e-06 ***
## Sexo.rec2-F 0.0390 0.1202 0.32 0.75
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.247)
##
## Number of Fisher Scoring iterations: 5
confint(modelo6.poi)
## 2.5 % 97.5 %
## (Intercept) 0.3096 0.6502
## Sexo.rec2-F -0.1966 0.2745
modelo7.poi <- svyglm(WHODMFT ~ Nivel.Socieconomico.rec, diseniopost1, family = quasipoisson())
summary(modelo7.poi)
##
## Call:
## svyglm(formula = WHODMFT ~ Nivel.Socieconomico.rec, diseniopost1,
## family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0897 0.1100 -0.82 0.42
## Nivel.Socieconomico.rec2-MEDIO 0.7642 0.1327 5.76 1.3e-06 ***
## Nivel.Socieconomico.rec3-BAJO 0.8183 0.1317 6.21 3.2e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.05)
##
## Number of Fisher Scoring iterations: 5
confint(modelo7.poi)
## 2.5 % 97.5 %
## (Intercept) -0.3053 0.1258
## Nivel.Socieconomico.rec2-MEDIO 0.5041 1.0243
## Nivel.Socieconomico.rec3-BAJO 0.5602 1.0765
modelo8.poi <- svyglm(WHODMFT ~ Nivel.Educativo.de.la.Madre1.rec, diseniopost1,
family = quasipoisson())
summary(modelo8.poi)
##
## Call:
## svyglm(formula = WHODMFT ~ Nivel.Educativo.de.la.Madre1.rec,
## diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -0.231 0.128
## Nivel.Educativo.de.la.Madre1.rec2-High School 0.615 0.212
## Nivel.Educativo.de.la.Madre1.rec3-Elementary School 0.906 0.136
## t value Pr(>|t|)
## (Intercept) -1.80 0.0805 .
## Nivel.Educativo.de.la.Madre1.rec2-High School 2.90 0.0063 **
## Nivel.Educativo.de.la.Madre1.rec3-Elementary School 6.67 7.7e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.066)
##
## Number of Fisher Scoring iterations: 5
confint(modelo8.poi)
## 2.5 % 97.5 %
## (Intercept) -0.4825 0.02091
## Nivel.Educativo.de.la.Madre1.rec2-High School 0.1986 1.03061
## Nivel.Educativo.de.la.Madre1.rec3-Elementary School 0.6402 1.17254
modelo.po9i <- svyglm(WHODMFT ~ Tipo.de.Escuela.rec, diseniopost1, family = quasipoisson())
summary(modelo9.poi)
## Error: objeto 'modelo9.poi' no encontrado
confint(modelo9.poi)
## Error: objeto 'modelo9.poi' no encontrado
modelo11.poi <- svyglm(WHODMFT ~ AtenOdonto2.rec, diseniopost1, family = quasipoisson())
summary(modelo11.poi)
##
## Call:
## svyglm(formula = WHODMFT ~ AtenOdonto2.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.3012 0.0728 4.14
## AtenOdonto2.rec2-publica 0.4719 0.0949 4.97
## AtenOdonto2.rec3-nunca fue al dentista 0.3596 0.1370 2.62
## Pr(>|t|)
## (Intercept) 0.00019 ***
## AtenOdonto2.rec2-publica 1.5e-05 ***
## AtenOdonto2.rec3-nunca fue al dentista 0.01255 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.23)
##
## Number of Fisher Scoring iterations: 6
confint(modelo11.poi)
## 2.5 % 97.5 %
## (Intercept) 0.15860 0.4438
## AtenOdonto2.rec2-publica 0.28591 0.6579
## AtenOdonto2.rec3-nunca fue al dentista 0.09101 0.6282
modelo12.poi <- svyglm(WHODMFT ~ FrCepDenti.4.rec, diseniopost1, family = quasipoisson())
summary(modelo12.poi)
##
## Call:
## svyglm(formula = WHODMFT ~ FrCepDenti.4.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.837 0.140 5.98
## FrCepDenti.4.rec2-1 vez al dias -0.271 0.201 -1.35
## FrCepDenti.4.rec3-2 veces al dia -0.288 0.193 -1.49
## FrCepDenti.4.rec4-3 veces al dia o mas -0.466 0.130 -3.58
## Pr(>|t|)
## (Intercept) 7.5e-07 ***
## FrCepDenti.4.rec2-1 vez al dias 0.186
## FrCepDenti.4.rec3-2 veces al dia 0.144
## FrCepDenti.4.rec4-3 veces al dia o mas 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.12)
##
## Number of Fisher Scoring iterations: 5
confint(modelo12.poi)
## 2.5 % 97.5 %
## (Intercept) 0.5628 1.11209
## FrCepDenti.4.rec2-1 vez al dias -0.6654 0.12288
## FrCepDenti.4.rec3-2 veces al dia -0.6658 0.08981
## FrCepDenti.4.rec4-3 veces al dia o mas -0.7214 -0.21120
modelo13.poi <- svyglm(WHODMFT ~ UsoDentifrico3.rec, diseniopost1, family = quasipoisson())
summary(modelo13.poi)
##
## Call:
## svyglm(formula = WHODMFT ~ UsoDentifrico3.rec, diseniopost1,
## family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4952 0.0498 9.94 4e-12 ***
## UsoDentifrico3.rec2-No 0.0620 0.2914 0.21 0.83
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.242)
##
## Number of Fisher Scoring iterations: 5
confint(modelo13.poi)
## 2.5 % 97.5 %
## (Intercept) 0.3976 0.5927
## UsoDentifrico3.rec2-No -0.5092 0.6331
modelo14.poi <- svyglm(WHODMFT ~ FluorProf.rec, diseniopost1, family = quasipoisson())
summary(modelo14.poi)
##
## Call:
## svyglm(formula = WHODMFT ~ FluorProf.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.5430 0.0716 7.58 4.1e-09 ***
## FluorProf.rec2-No -0.1263 0.0883 -1.43 0.16
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.1)
##
## Number of Fisher Scoring iterations: 5
confint(modelo14.poi)
## 2.5 % 97.5 %
## (Intercept) 0.4027 0.6834
## FluorProf.rec2-No -0.2994 0.0467
modelo15.poi <- svyglm(WHODMFT ~ RefrColaB.rec, diseniopost1, family = quasipoisson())
summary(modelo15.poi)
##
## Call:
## svyglm(formula = WHODMFT ~ RefrColaB.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6952 0.0931 7.47 6.9e-09 ***
## RefrColaB.rec2-A veces -0.2625 0.0936 -2.80 0.008 **
## RefrColaB.rec3-Nunca o raramente -0.2904 0.1372 -2.12 0.041 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.188)
##
## Number of Fisher Scoring iterations: 5
confint(modelo15.poi)
## 2.5 % 97.5 %
## (Intercept) 0.5127 0.87766
## RefrColaB.rec2-A veces -0.4459 -0.07899
## RefrColaB.rec3-Nunca o raramente -0.5592 -0.02152
modelo16.poi <- svyglm(WHODMFT ~ MateDulceB.rec, diseniopost1, family = quasipoisson())
summary(modelo16.poi)
##
## Call:
## svyglm(formula = WHODMFT ~ MateDulceB.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.573 0.169 3.39 0.0017 **
## MateDulceB.rec2-A veces 0.153 0.220 0.69 0.4930
## MateDulceB.rec3-Nunca o raramente -0.203 0.161 -1.26 0.2153
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.13)
##
## Number of Fisher Scoring iterations: 5
confint(modelo16.poi)
## 2.5 % 97.5 %
## (Intercept) 0.2417 0.9039
## MateDulceB.rec2-A veces -0.2794 0.5846
## MateDulceB.rec3-Nunca o raramente -0.5185 0.1126
modelo17.poi <- svyglm(WHODMFT ~ GolosinasB.rec, diseniopost1, family = quasipoisson())
summary(modelo17.poi)
##
## Call:
## svyglm(formula = WHODMFT ~ GolosinasB.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.689 0.101 6.85 4.6e-08 ***
## GolosinasB.rec2-A veces -0.247 0.111 -2.23 0.032 *
## GolosinasB.rec3-Nunca o raramente -0.313 0.139 -2.26 0.030 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.179)
##
## Number of Fisher Scoring iterations: 5
confint(modelo17.poi)
## 2.5 % 97.5 %
## (Intercept) 0.4921 0.88686
## GolosinasB.rec2-A veces -0.4637 -0.03004
## GolosinasB.rec3-Nunca o raramente -0.5845 -0.04154
modelo19.poi <- svyglm(WHODMFT ~ Masas.DulcesB.rec, diseniopost1, family = quasipoisson())
summary(modelo19.poi)
##
## Call:
## svyglm(formula = WHODMFT ~ Masas.DulcesB.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7464 0.0958 7.79 2.6e-09
## Masas.DulcesB.rec2-A veces -0.2944 0.0957 -3.08 0.0039
## Masas.DulcesB.rec3-Nunca o raramente -0.3590 0.1484 -2.42 0.0206
##
## (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 2.203)
##
## Number of Fisher Scoring iterations: 5
confint(modelo19.poi)
## 2.5 % 97.5 %
## (Intercept) 0.5587 0.9341
## Masas.DulcesB.rec2-A veces -0.4820 -0.1068
## Masas.DulcesB.rec3-Nunca o raramente -0.6498 -0.0682
modelo20.poi <- svyglm(WHODMFT ~ UltmVisita1.rec, diseniopost1, family = quasipoisson())
summary(modelo20.poi)
##
## Call:
## svyglm(formula = WHODMFT ~ UltmVisita1.rec, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.4665 0.0610 7.65
## UltmVisita1.rec2- años atrás 0.0484 0.2605 0.19
## UltmVisita1.rec3-Nunca fue al dentista 0.2057 0.1332 1.54
## Pr(>|t|)
## (Intercept) 3.9e-09 ***
## UltmVisita1.rec2- años atrás 0.85
## UltmVisita1.rec3-Nunca fue al dentista 0.13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.232)
##
## Number of Fisher Scoring iterations: 5
confint(modelo20.poi)
## 2.5 % 97.5 %
## (Intercept) 0.34699 0.5861
## UltmVisita1.rec2- años atrás -0.46213 0.5590
## UltmVisita1.rec3-Nunca fue al dentista -0.05545 0.4668
modelo21.poi <- svyglm(WHODMFT ~ IGS.rec1, diseniopost1, family = quasipoisson())
summary(modelo21.poi)
##
## Call:
## svyglm(formula = WHODMFT ~ IGS.rec1, diseniopost1, family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4418 0.0926 4.77 2.9e-05 ***
## IGS.rec12-De 20 a 60 0.0881 0.1162 0.76 0.453
## IGS.rec13-Mas de 60 0.2161 0.0926 2.33 0.025 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.247)
##
## Number of Fisher Scoring iterations: 5
confint(modelo21.poi)
## 2.5 % 97.5 %
## (Intercept) 0.26019 0.6233
## IGS.rec12-De 20 a 60 -0.13957 0.3158
## IGS.rec13-Mas de 60 0.03469 0.3975
modelo22.poi <- svyglm(WHODMFT ~ Nive.Educativo.de.la.Madre2.rec, diseniopost1,
family = quasipoisson())
summary(modelo22.poi)
##
## Call:
## svyglm(formula = WHODMFT ~ Nive.Educativo.de.la.Madre2.rec, diseniopost1,
## family = quasipoisson())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.139 0.159
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL 0.361 0.167
## Nive.Educativo.de.la.Madre2.rec3-ELEMENTARY SCHOOL 0.646 0.192
## t value Pr(>|t|)
## (Intercept) 0.87 0.3898
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL 2.17 0.0367 *
## Nive.Educativo.de.la.Madre2.rec3-ELEMENTARY SCHOOL 3.36 0.0018 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.174)
##
## Number of Fisher Scoring iterations: 6
confint(modelo22.poi)
## 2.5 % 97.5 %
## (Intercept) -0.17377 0.4513
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL 0.03455 0.6875
## Nive.Educativo.de.la.Madre2.rec3-ELEMENTARY SCHOOL 0.26871 1.0229