12 de setiembre de 2013
# 12 de setiembre de 2013
library(survey)
## Attaching package: 'survey'
##
## The following object(s) are masked from 'package:graphics':
##
## dotchart
options(OutDec = ",")
library(survey)
# load('~/Dropbox/odontologia/maestria
# licet/julio_2013/datos_licet_25072013.RData')
# load('C:/Users/usuario/Dropbox/odontologia/maestria
# licet/julio_2013/datos_licet_25072013.RData')
load("~/Dropbox/odontologia/maestria licet/julio_2013/datos_licet_25072013.RData")
library(car)
## Loading required package: MASS Loading required package: nnet
diseniopost1$variables$erodentina.rec <- as.factor(diseniopost1$variables$erodentina.rec)
modelo_ero_1.logit <- svyglm(erodentina.rec ~ Sexo, design = diseniopost1, family = quasibinomial())
summary(modelo_ero_1.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ Sexo, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,627 0,344 -10,54 7,8e-13 ***
## SexoM 0,979 0,469 2,09 0,044 *
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,9818)
##
## Number of Fisher Scoring iterations: 6
confint(modelo_ero_1.logit)
## 2,5 % 97,5 %
## (Intercept) -4,30105 -2,952
## SexoM 0,06025 1,898
modelo_ero_2.logit <- svyglm(erodentina.rec ~ UsoDentifrico3.rec, design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_2.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ UsoDentifrico3.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,018 0,224 -13,48 4,6e-16 ***
## UsoDentifrico3.rec2-No 0,215 0,884 0,24 0,81
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,9781)
##
## Number of Fisher Scoring iterations: 5
confint(modelo_ero_2.logit)
## 2,5 % 97,5 %
## (Intercept) -3,457 -2,579
## UsoDentifrico3.rec2-No -1,519 1,948
modelo_ero_3.logit <- svyglm(erodentina.rec ~ Nivel.Educativo.de.la.Madre1.rec,
design = diseniopost1, family = quasibinomial())
summary(modelo_ero_3.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ Nivel.Educativo.de.la.Madre1.rec,
## design = diseniopost1, family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -1,923 0,480 -4,00
## Nivel.Educativo.de.la.Madre1.rec2-Basic -1,229 0,521 -2,36
## Nivel.Educativo.de.la.Madre1.rec3-Medium -1,445 0,640 -2,26
## Nivel.Educativo.de.la.Madre1.rec4-High -1,082 0,652 -1,66
## Pr(>|t|)
## (Intercept) 0,0003 ***
## Nivel.Educativo.de.la.Madre1.rec2-Basic 0,0239 *
## Nivel.Educativo.de.la.Madre1.rec3-Medium 0,0301 *
## Nivel.Educativo.de.la.Madre1.rec4-High 0,1058
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,9559)
##
## Number of Fisher Scoring iterations: 5
confint(modelo_ero_3.logit)
## 2,5 % 97,5 %
## (Intercept) -2,865 -0,9817
## Nivel.Educativo.de.la.Madre1.rec2-Basic -2,251 -0,2073
## Nivel.Educativo.de.la.Madre1.rec3-Medium -2,699 -0,1912
## Nivel.Educativo.de.la.Madre1.rec4-High -2,360 0,1963
modelo_ero_4.logit <- svyglm(erodentina.rec ~ FrCepDenti.rec, design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_4.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ FrCepDenti.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,0515 0,4123 -7,40 8,3e-09
## FrCepDenti.rec2- 2 veces al dia -0,0996 0,6101 -0,16 0,87
## FrCepDenti.rec3-3 o mas veces al dia 0,1836 0,4928 0,37 0,71
##
## (Intercept) ***
## FrCepDenti.rec2- 2 veces al dia
## FrCepDenti.rec3-3 o mas veces al dia
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,956)
##
## Number of Fisher Scoring iterations: 5
confint(modelo_ero_4.logit)
## 2,5 % 97,5 %
## (Intercept) -3,8596 -2,243
## FrCepDenti.rec2- 2 veces al dia -1,2954 1,096
## FrCepDenti.rec3-3 o mas veces al dia -0,7823 1,149
modelo_ero_5.logit <- svyglm(erodentina.rec ~ IGS.rec, design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_5.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ IGS.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4,80 1,06 -4,54 5,9e-05 ***
## IGS.rec2-De 45 a 60 1,74 1,16 1,49 0,144
## IGS.rec3 -Menos o igual a 45 1,87 1,03 1,81 0,079 .
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,9818)
##
## Number of Fisher Scoring iterations: 7
confint(modelo_ero_5.logit)
## 2,5 % 97,5 %
## (Intercept) -6,8772 -2,727
## IGS.rec2-De 45 a 60 -0,5432 4,019
## IGS.rec3 -Menos o igual a 45 -0,1580 3,892
modelo_ero_6.logit <- svyglm(erodentina.rec ~ MedResp.rec, design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_6.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ MedResp.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2,950 0,223 -13,25 8,1e-16 ***
## MedResp.rec2-Yes -0,525 0,596 -0,88 0,38
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,9635)
##
## Number of Fisher Scoring iterations: 6
confint(modelo_ero_6.logit)
## 2,5 % 97,5 %
## (Intercept) -3,387 -2,5137
## MedResp.rec2-Yes -1,693 0,6437
modelo_ero_7.logit <- svyglm(erodentina.rec ~ AlterGastrica.rec, design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_7.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ AlterGastrica.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2,955 0,218 -13,6 3,8e-16 ***
## AlterGastrica.rec2-Yes -15,737 0,205 -76,7 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,9127)
##
## Number of Fisher Scoring iterations: 17
confint(modelo_ero_7.logit)
## 2,5 % 97,5 %
## (Intercept) -3,382 -2,528
## AlterGastrica.rec2-Yes -16,140 -15,335
diseniopost1$variables$Consumo_bebidas_cola <- as.factor(diseniopost1$variables$Consumo_bebidas_cola)
modelo_ero_8.logit <- svyglm(erodentina.rec ~ RefrCola., design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_8.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ RefrCola., design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,09202 0,24102 -12,83 2,2e-15 ***
## RefrCola. 0,01660 0,00969 1,71 0,095 .
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,9806)
##
## Number of Fisher Scoring iterations: 5
confint(modelo_ero_8.logit)
## 2,5 % 97,5 %
## (Intercept) -3,564413 -2,61963
## RefrCola. -0,002384 0,03559
modelo_ero_9.logit <- svyglm(erodentina.rec ~ Consitencia_Cepillo.rec, design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_9.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ Consitencia_Cepillo.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2,5258 0,5797 -4,36 0,00013 ***
## Consitencia_Cepillo.rec2 0,0785 0,7204 0,11 0,91391
## Consitencia_Cepillo.rec3 -0,9167 0,5895 -1,56 0,12976
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,7872)
##
## Number of Fisher Scoring iterations: 6
confint(modelo_ero_9.logit)
## 2,5 % 97,5 %
## (Intercept) -3,662 -1,3896
## Consitencia_Cepillo.rec2 -1,333 1,4905
## Consitencia_Cepillo.rec3 -2,072 0,2387
modelo_ero_10.logit <- svyglm(erodentina.rec ~ Natac2vec.rec, design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_10.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ Natac2vec.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,041 0,287 -10,61 3,6e-12 ***
## Natac2vec.rec2-Yes 0,117 0,460 0,25 0,8
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,8127)
##
## Number of Fisher Scoring iterations: 5
confint(modelo_ero_10.logit)
## 2,5 % 97,5 %
## (Intercept) -3,6035 -2,479
## Natac2vec.rec2-Yes -0,7844 1,018
modelo_ero_11.logit <- svyglm(erodentina.rec ~ BuchTragar.rec, design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_11.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ BuchTragar.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,124 0,291 -10,72 2,7e-12 ***
## BuchTragar.rec2-Yes 0,760 0,395 1,92 0,063 .
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,7982)
##
## Number of Fisher Scoring iterations: 5
confint(modelo_ero_11.logit)
## 2,5 % 97,5 %
## (Intercept) -3,69493 -2,553
## BuchTragar.rec2-Yes -0,01391 1,534
modelo_ero_12.logit <- svyglm(erodentina.rec ~ FormBeber.rec, design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_12.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ FormBeber.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,056 0,640 -4,78 2,8e-05 ***
## FormBeber.rec2-Por el pico -0,454 0,858 -0,53 0,60
## FormBeber.rec3-Con vaso 0,120 0,736 0,16 0,87
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,9754)
##
## Number of Fisher Scoring iterations: 6
confint(modelo_ero_12.logit)
## 2,5 % 97,5 %
## (Intercept) -4,310 -1,802
## FormBeber.rec2-Por el pico -2,135 1,228
## FormBeber.rec3-Con vaso -1,322 1,563
modelo_ero_13.logit <- svyglm(erodentina.rec ~ Nivel.Socieconomico.rec, design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_13.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ Nivel.Socieconomico.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,1238 0,2965 -10,54 1,1e-12 ***
## Nivel.Socieconomico.rec2-MEDIO -0,0405 0,4247 -0,10 0,92
## Nivel.Socieconomico.rec3-ALTO 0,4249 0,3972 1,07 0,29
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,9713)
##
## Number of Fisher Scoring iterations: 5
confint(modelo_ero_13.logit)
## 2,5 % 97,5 %
## (Intercept) -3,7050 -2,5426
## Nivel.Socieconomico.rec2-MEDIO -0,8728 0,7919
## Nivel.Socieconomico.rec3-ALTO -0,3536 1,2034
modelo_ero_14.logit <- svyglm(erodentina.rec ~ Tipo.de.Escuela.rec, design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_14.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ Tipo.de.Escuela.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,734 0,599 -6,24 2,7e-07 ***
## Tipo.de.Escuela.rec2-Private 0,884 0,644 1,37 0,18
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,9818)
##
## Number of Fisher Scoring iterations: 6
confint(modelo_ero_14.logit)
## 2,5 % 97,5 %
## (Intercept) -4,9072 -2,560
## Tipo.de.Escuela.rec2-Private -0,3783 2,146
modelo_ero_15.logit <- svyglm(erodentina.rec ~ Yogurt.rec, design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_15.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ Yogurt.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2,177 0,614 -3,55 0,0011 **
## Yogurt.rec2-Todos los dias -0,985 0,622 -1,58 0,1220
## Yogurt.rec3-Nunca o raramente -0,742 0,528 -1,41 0,1681
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,9633)
##
## Number of Fisher Scoring iterations: 5
confint(modelo_ero_15.logit)
## 2,5 % 97,5 %
## (Intercept) -3,380 -0,9734
## Yogurt.rec2-Todos los dias -2,204 0,2347
## Yogurt.rec3-Nunca o raramente -1,776 0,2924
modelo_ero_16.logit <- svyglm(erodentina.rec ~ Bruxismo.rec, design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_16.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ Bruxismo.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2,991 0,234 -12,80 2,4e-15 ***
## Bruxismo.rec2-Yes -0,151 0,408 -0,37 0,71
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,964)
##
## Number of Fisher Scoring iterations: 5
confint(modelo_ero_16.logit)
## 2,5 % 97,5 %
## (Intercept) -3,4490 -2,5328
## Bruxismo.rec2-Yes -0,9509 0,6489
modelo_ero_17.logit <- svyglm(erodentina.rec ~ bedeportediario.rec, design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_17.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ bedeportediario.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,111 0,332 -9,38 1,1e-10 ***
## bedeportediario.rec2-Gatorade 0,882 0,863 1,02 0,31
## bedeportediario.rec3-other -1,364 1,113 -1,23 0,23
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,8143)
##
## Number of Fisher Scoring iterations: 6
confint(modelo_ero_17.logit)
## 2,5 % 97,5 %
## (Intercept) -3,7609 -2,460
## bedeportediario.rec2-Gatorade -0,8093 2,573
## bedeportediario.rec3-other -3,5448 0,817
modelo_ero_19.logit <- svyglm(erodentina.rec ~ jugodiario.nodiario, design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_19.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ jugodiario.nodiario, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,0748 0,3351 -9,18 3,5e-11 ***
## jugodiario.nodiario2-Diario 0,0596 0,5045 0,12 0,91
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,9706)
##
## Number of Fisher Scoring iterations: 5
confint(modelo_ero_19.logit)
## 2,5 % 97,5 %
## (Intercept) -3,7315 -2,418
## jugodiario.nodiario2-Diario -0,9292 1,049
modelo_ero_20.logit <- svyglm(erodentina.rec ~ JugFrutas.rec, design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_20.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ JugFrutas.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2,649 0,785 -3,38 0,0017
## JugFrutas.rec2-Todos los dias -0,569 0,808 -0,70 0,4859
## JugFrutas.rec3-Mas de 3 veces al dia 0,101 0,884 0,11 0,9093
##
## (Intercept) **
## JugFrutas.rec2-Todos los dias
## JugFrutas.rec3-Mas de 3 veces al dia
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,9706)
##
## Number of Fisher Scoring iterations: 5
confint(modelo_ero_20.logit)
## 2,5 % 97,5 %
## (Intercept) -4,188 -1,111
## JugFrutas.rec2-Todos los dias -2,152 1,015
## JugFrutas.rec3-Mas de 3 veces al dia -1,630 1,833
modelo_ero_21.logit <- svyglm(erodentina.rec ~ bedeportediario.rec, design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_21.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ bedeportediario.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,111 0,332 -9,38 1,1e-10 ***
## bedeportediario.rec2-Gatorade 0,882 0,863 1,02 0,31
## bedeportediario.rec3-other -1,364 1,113 -1,23 0,23
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,8143)
##
## Number of Fisher Scoring iterations: 6
confint(modelo_ero_21.logit)
## 2,5 % 97,5 %
## (Intercept) -3,7609 -2,460
## bedeportediario.rec2-Gatorade -0,8093 2,573
## bedeportediario.rec3-other -3,5448 0,817
modelo_ero_22.logit <- svyglm(erodentina.rec ~ Bebidas_energizantes.rec, design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_22.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ Bebidas_energizantes.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -3,342 0,302 -11,08
## Bebidas_energizantes.rec2-Todos los dias 0,412 0,278 1,48
## Bebidas_energizantes.recMas de 3 veces al dia 2,052 0,670 3,06
## Pr(>|t|)
## (Intercept) 2,6e-13 ***
## Bebidas_energizantes.rec2-Todos los dias 0,1470
## Bebidas_energizantes.recMas de 3 veces al dia 0,0041 **
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,9451)
##
## Number of Fisher Scoring iterations: 6
confint(modelo_ero_22.logit)
## 2,5 % 97,5 %
## (Intercept) -3,9339 -2,7509
## Bebidas_energizantes.rec2-Todos los dias -0,1333 0,9581
## Bebidas_energizantes.recMas de 3 veces al dia 0,7381 3,3659
diseniopost1$variables$RefrCola.rec <- as.factor(diseniopost1$variables$RefrCola.)
diseniopost1$variables$RefrCola.rec <- recode(diseniopost1$variables$RefrCola.rec,
"1='1-Nunca o raramente';2='2-Todos los dias';3='3-Mas de 3 veces al dia';99=NA")
modelo_ero_23.logit <- svyglm(erodentina.rec ~ RefrCola.rec, design = diseniopost1,
family = quasibinomial())
summary(modelo_ero_23.logit)
##
## Call:
## svyglm(formula = erodentina.rec ~ RefrCola.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,624 0,565 -6,42 1,7e-07
## RefrCola.rec2-Todos los dias 0,463 0,508 0,91 0,37
## RefrCola.rec3-Mas de 3 veces al dia 0,986 0,723 1,36 0,18
##
## (Intercept) ***
## RefrCola.rec2-Todos los dias
## RefrCola.rec3-Mas de 3 veces al dia
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,9703)
##
## Number of Fisher Scoring iterations: 6
confint(modelo_ero_23.logit)
## 2,5 % 97,5 %
## (Intercept) -4,7307 -2,517
## RefrCola.rec2-Todos los dias -0,5322 1,457
## RefrCola.rec3-Mas de 3 veces al dia -0,4310 2,402
#
# modelomulti_ero.logit<-svyglm(erodentina.rec~Sexo+Nivel.Educativo.de.la.Madre1+Nivel.Socieconomico+Tipo.de.Escuela+BuchTragar,
# design=diseniopost1, family=quasibinomial())
#
# summary(modelomulti_ero.logit)
#
# confint(modelomulti_ero.logit)