Aca se analiza si la prevalencia de severidad ajustada se asocia con las variables de riesgo
library(survey)
## Attaching package: 'survey'
## The following object(s) are masked from 'package:graphics':
##
## dotchart
options(OutDec = ",")
library(car)
## Loading required package: MASS
## Loading required package: nnet
load("~/Dropbox/odontologia/maestria licet/octubre 2013/datos_licet_03112013.RData")
rm(list = ls(pattern = "modelo"))
diseniopost1$variables$Erosinbord <- as.factor(diseniopost1$variables$Erosinbord)
Erosinbord
Modelo_ero_1.logit <- svyglm(Erosinbord ~ Sexo, design = diseniopost1, family = quasibinomial())
summary(Modelo_ero_1.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ Sexo, design = diseniopost1, family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,731 0,369 -10,12 2,5e-12 ***
## SexoM 1,045 0,480 2,18 0,036 *
## ---
## 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) -4,4543 -3,009
## SexoM 0,1036 1,987
Modelo_ero_2.logit <- svyglm(Erosinbord ~ UsoDentifrico3.rec, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_2.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ UsoDentifrico3.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,076 0,231 -13,31 6,9e-16 ***
## UsoDentifrico3.rec2-No 0,272 0,882 0,31 0,76
## ---
## 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) -3,528 -2,623
## UsoDentifrico3.rec2-No -1,456 2,001
Modelo_ero_3.logit <- svyglm(Erosinbord ~ Nivel.Educativo.de.la.Madre1.rec,
design = diseniopost1, family = quasibinomial())
summary(Modelo_ero_3.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ 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 -1,303 0,543 -2,40
## 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 0,0218 *
## 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: 6
confint(Modelo_ero_3.logit)
## 2,5 % 97,5 %
## (Intercept) -2,865 -0,9817
## Nivel.Educativo.de.la.Madre1.rec2-Basic -2,368 -0,2380
## Nivel.Educativo.de.la.Madre1.rec3-Medium -2,699 -0,1912
## Nivel.Educativo.de.la.Madre1.rec4-High -2,360 0,1963
Modelo_ero_3a.logit <- svyglm(Erosinbord ~ Nive.Educativo.de.la.Madre2, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_3a.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ Nive.Educativo.de.la.Madre2, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -3,508 0,771 -4,55
## Nive.Educativo.de.la.Madre2COLLEGE-UNIVERSITY 0,632 0,660 0,96
## Nive.Educativo.de.la.Madre2ELEMENTARY SCHOOL 0,709 0,871 0,81
## Nive.Educativo.de.la.Madre2HIGH SCHOOL 0,172 0,826 0,21
## Pr(>|t|)
## (Intercept) 5,8e-05 ***
## Nive.Educativo.de.la.Madre2COLLEGE-UNIVERSITY 0,34
## Nive.Educativo.de.la.Madre2ELEMENTARY SCHOOL 0,42
## Nive.Educativo.de.la.Madre2HIGH SCHOOL 0,84
## ---
## 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_3a.logit)
## 2,5 % 97,5 %
## (Intercept) -5,0185 -1,998
## Nive.Educativo.de.la.Madre2COLLEGE-UNIVERSITY -0,6610 1,925
## Nive.Educativo.de.la.Madre2ELEMENTARY SCHOOL -0,9976 2,416
## Nive.Educativo.de.la.Madre2HIGH SCHOOL -1,4479 1,791
Modelo_ero_4.logit <- svyglm(Erosinbord ~ FrCepDenti.rec, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_4.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ FrCepDenti.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,2219 0,3824 -8,43 3,9e-10
## FrCepDenti.rec2- 2 veces al dia 0,0495 0,6087 0,08 0,94
## FrCepDenti.rec3-3 o mas veces al dia 0,3132 0,4705 0,67 0,51
##
## (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) -3,971 -2,472
## FrCepDenti.rec2- 2 veces al dia -1,144 1,243
## FrCepDenti.rec3-3 o mas veces al dia -0,609 1,235
levels(diseniopost1$variables$FrCepDenti.rec)
## [1] "1-1 vez al dia o menos" "2- 2 veces al dia"
## [3] "3-3 o mas veces al dia"
diseniopost1$variables$FRCEPDENTI.rec <- recode(diseniopost1$variables$FrCepDenti.rec,
"'1-1 vez al dia o menos'='3 1 vez al dia o menos';'2- 2 veces al dia'='2 2 veces al dia';'3-3 o mas veces al dia'='1 3 o mas veces al dia'")
Modelo_ero_4a.logit <- svyglm(Erosinbord ~ FRCEPDENTI.rec, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_4a.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ FRCEPDENTI.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2,909 0,268 -10,84 4,8e-13
## FRCEPDENTI.rec2 2 veces al dia -0,264 0,489 -0,54 0,59
## FRCEPDENTI.rec3 1 vez al dia o menos -0,313 0,471 -0,67 0,51
##
## (Intercept) ***
## FRCEPDENTI.rec2 2 veces al dia
## FRCEPDENTI.rec3 1 vez al dia o menos
## ---
## 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_4a.logit)
## 2,5 % 97,5 %
## (Intercept) -3,435 -2,3828
## FRCEPDENTI.rec2 2 veces al dia -1,223 0,6955
## FRCEPDENTI.rec3 1 vez al dia o menos -1,235 0,6090
table(diseniopost1$variables$FrCepDenti.rec, diseniopost1$variables$FRCEPDENTI.rec)
##
## 1 3 o mas veces al dia 2 2 veces al dia
## 1-1 vez al dia o menos 0 0
## 2- 2 veces al dia 0 377
## 3-3 o mas veces al dia 496 0
##
## 3 1 vez al dia o menos
## 1-1 vez al dia o menos 238
## 2- 2 veces al dia 0
## 3-3 o mas veces al dia 0
Modelo_ero_5.logit <- svyglm(Erosinbord ~ IGS.rec, design = diseniopost1, family = quasibinomial())
summary(Modelo_ero_5.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ 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 1,80 1,04 1,74 0,091 .
## ---
## 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 -0,2307 3,833
Modelo_ero_6.logit <- svyglm(Erosinbord ~ MedResp.rec, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_6.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ MedResp.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,013 0,231 -13,05 1,3e-15 ***
## MedResp.rec2-Yes -0,462 0,596 -0,78 0,44
## ---
## 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) -3,466 -2,5608
## MedResp.rec2-Yes -1,629 0,7056
Modelo_ero_7.logit <- svyglm(Erosinbord ~ AlterGastrica.rec, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_7.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ AlterGastrica.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,012 0,225 -13,4 6,2e-16 ***
## AlterGastrica.rec2-Yes -15,680 0,212 -74,0 < 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) -3,454 -2,57
## AlterGastrica.rec2-Yes -16,095 -15,26
Modelo_ero_8.logit <- svyglm(Erosinbord ~ RefrCola., design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_8.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ RefrCola., design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,15275 0,25087 -12,57 4,2e-15 ***
## RefrCola. 0,01729 0,00971 1,78 0,083 .
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,9802)
##
## Number of Fisher Scoring iterations: 5
confint(Modelo_ero_8.logit)
## 2,5 % 97,5 %
## (Intercept) -3,644455 -2,66105
## RefrCola. -0,001746 0,03632
Modelo_ero_9.logit <- svyglm(Erosinbord ~ Consitencia_Cepillo.rec, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_9.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ 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) -2,4473 0,2857 -8,57 8,7e-10 ***
## Consitencia_Cepillo.rec2-Medium -1,0137 0,2924 -3,47 0,0015 **
## Consitencia_Cepillo.rec3-Hard -0,0785 0,7204 -0,11 0,9139
## ---
## 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) -3,007 -1,8873
## Consitencia_Cepillo.rec2-Medium -1,587 -0,4406
## Consitencia_Cepillo.rec3-Hard -1,490 1,3335
Modelo_ero_10.logit <- svyglm(Erosinbord ~ Natac2vec.rec, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_10.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ Natac2vec.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,0415 0,2868 -10,61 3,6e-12 ***
## Natac2vec.rec2-Yes 0,0789 0,4669 0,17 0,87
## ---
## 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 -2,4794
## Natac2vec.rec2-Yes -0,8361 0,9939
Modelo_ero_11.logit <- svyglm(Erosinbord ~ BuchTragar.rec, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_11.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ BuchTragar.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,134 0,294 -10,66 3,2e-12 ***
## BuchTragar.rec2-Yes 0,770 0,397 1,94 0,061 .
## ---
## 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) -3,710152 -2,557
## BuchTragar.rec2-Yes -0,007202 1,547
Modelo_ero_12.logit <- svyglm(Erosinbord ~ FormBeber.rec, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_12.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ FormBeber.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,0562 0,6398 -4,78 2,8e-05 ***
## FormBeber.rec2-Por el pico -0,7785 0,7794 -1,00 0,32
## FormBeber.rec3-Con vaso 0,0866 0,7446 0,12 0,91
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0,9754)
##
## Number of Fisher Scoring iterations: 6
confint(Modelo_ero_12.logit)
## 2,5 % 97,5 %
## (Intercept) -4,310 -1,8022
## FormBeber.rec2-Por el pico -2,306 0,7491
## FormBeber.rec3-Con vaso -1,373 1,5459
Modelo_ero_13.logit <- svyglm(Erosinbord ~ Nivel.Socieconomico.rec, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_13.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ 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) -3,1878 0,3146 -10,13 3,2e-12 ***
## Nivel.Socieconomico.rec2-MEDIO 0,0235 0,4332 0,05 0,96
## Nivel.Socieconomico.rec3-ALTO 0,3825 0,4219 0,91 0,37
## ---
## 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) -3,8044 -2,5712
## Nivel.Socieconomico.rec2-MEDIO -0,8254 0,8725
## Nivel.Socieconomico.rec3-ALTO -0,4445 1,2095
Modelo_ero_14.logit <- svyglm(Erosinbord ~ Tipo.de.Escuela.rec, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_14.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ 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 0,819 0,647 1,26 0,21
## ---
## 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 -0,4502 2,087
levels(diseniopost1$variables$Yogurt.rec)
## [1] "1-Mas de 3 veces al dia" "2-Todos los dias"
## [3] "3-Nunca o raramente"
Modelo_ero_15.logit <- svyglm(Erosinbord ~ Yogurt.rec, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_15.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ 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 -0,942 0,490 -1,92 0,0624 .
## ---
## 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 -0,97338
## Yogurt.rec2-Todos los dias -2,204 0,23472
## Yogurt.rec3-Nunca o raramente -1,903 0,01886
Modelo_ero_16.logit <- svyglm(Erosinbord ~ Bruxismo.rec, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_16.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ Bruxismo.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,057 0,247 -12,4 6,8e-15 ***
## Bruxismo.rec2-Yes -0,085 0,427 -0,2 0,84
## ---
## 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) -3,5415 -2,5725
## Bruxismo.rec2-Yes -0,9215 0,7515
Modelo_ero_17.logit <- svyglm(Erosinbord ~ bedeportediario.rec, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_17.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ bedeportediario.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,121 0,334 -9,34 1,2e-10 ***
## bedeportediario.rec2-Gatorade 0,892 0,864 1,03 0,31
## bedeportediario.rec3-other -1,354 1,114 -1,22 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) -3,7755 -2,4656
## bedeportediario.rec2-Gatorade -0,8013 2,5845
## bedeportediario.rec3-other -3,5365 0,8284
Modelo_ero_19.logit <- svyglm(Erosinbord ~ jugodiario.nodiario, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_19.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ jugodiario.nodiario, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,195 0,347 -9,20 3,3e-11 ***
## jugodiario.nodiario2-Diario 0,180 0,504 0,36 0,72
## ---
## 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) -3,8755 -2,515
## jugodiario.nodiario2-Diario -0,8085 1,168
Modelo_ero_20.logit <- svyglm(Erosinbord ~ JugFrutas.rec, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_20.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ JugFrutas.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,717 0,955 -3,89 0,0004
## JugFrutas.rec2-Todos los dias 0,489 0,957 0,51 0,6120
## JugFrutas.rec3-Mas de 3 veces al dia 1,169 1,030 1,14 0,2637
##
## (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: 6
confint(Modelo_ero_20.logit)
## 2,5 % 97,5 %
## (Intercept) -5,5887 -1,845
## JugFrutas.rec2-Todos los dias -1,3857 2,365
## JugFrutas.rec3-Mas de 3 veces al dia -0,8498 3,188
Modelo_ero_21.logit <- svyglm(Erosinbord ~ bedeportediario.rec, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_21.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ bedeportediario.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3,121 0,334 -9,34 1,2e-10 ***
## bedeportediario.rec2-Gatorade 0,892 0,864 1,03 0,31
## bedeportediario.rec3-other -1,354 1,114 -1,22 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) -3,7755 -2,4656
## bedeportediario.rec2-Gatorade -0,8013 2,5845
## bedeportediario.rec3-other -3,5365 0,8284
Modelo_ero_22.logit <- svyglm(Erosinbord ~ Bebidas_energizantes.rec, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_22.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ 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 0,277 0,286 0,97
## 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 0,3396
## 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 -0,2843 0,8386
## Bebidas_energizantes.recMas de 3 veces al dia 0,7381 3,3659
Modelo_ero_23.logit <- svyglm(Erosinbord ~ RefrCola.rec, design = diseniopost1,
family = quasibinomial())
summary(Modelo_ero_23.logit)
##
## Call:
## svyglm(formula = Erosinbord ~ RefrCola.rec, design = diseniopost1,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio1, ~categor.rec + Sexo, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4,109 0,753 -5,45 3,4e-06
## RefrCola.rec2-Todos los dias 0,908 0,740 1,23 0,23
## RefrCola.rec3-Mas de 3 veces al dia 1,451 0,883 1,64 0,11
##
## (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) -5,5849 -2,632
## RefrCola.rec2-Todos los dias -0,5413 2,358
## RefrCola.rec3-Mas de 3 veces al dia -0,2790 3,182