Prevalencia (prevere) y Extensión (cpodbere) por ciudad, con IC y p valor de Wald test, creo que para extensión solo miramos los intervalos de confianza.
Regresión Logística Uni-variada para prevere: 1) país 2) sexo 3) Escolmaerecat23 4) tipoesc 5) freqescovacat 6) usocreme 7) fluorprof Regresión Poisson Uni-variada para cpodbere: 1) país 2) sexo 3) Escolmaerecat23 4) tipoesc 5) freqescovacat 6) usocreme 7) fluorprof
Si todo es parecido prevoms y cpodoms La Multi-variada en ambos modelos irían: 1) país 3) escmaerecat23 4) tipoesc 5) freqescovat Van todas las que queden <0,10 en la uni-variada. Por eso va variable país. Espero, que puedas el finde…..Besos Anun
# 3 de Noviembre 2013
#
# load('~/Dropbox/odontologia/maestria_anunziatta/BancoUnidoBrasUru/BRASURU(28102013).RData')
# load('~/Dropbox/odontologia/maestria_anunziatta/BancoUnidoBrasUru/BRASURU(02112013).RData')
load("~/Dropbox/odontologia/maestria_anunziatta/BancoUnidoBrasUru/BRASURU(07112013).RData")
library(survey)
##
## Attaching package: 'survey'
##
## The following object(s) are masked from 'package:graphics':
##
## dotchart
library(car)
## Loading required package: MASS
## Loading required package: nnet
modelo1_multi.bin <- svyglm(prevbere ~ pais + escolmaerecat23cat + tipoesc +
freqescovacat, disenio_urubra, family = quasibinomial())
summary(modelo1_multi.bin)
##
## Call:
## svyglm(formula = prevbere ~ pais + escolmaerecat23cat + tipoesc +
## freqescovacat, disenio_urubra, family = quasibinomial())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.065 0.165 0.39 0.6949
## paisUru 0.307 0.130 2.36 0.0210
## escolmaerecat23cat2-high school 0.309 0.208 1.49 0.1414
## escolmaerecat23cat3-elementary school 0.712 0.247 2.89 0.0051
## tipoesc2-Publica 0.459 0.185 2.48 0.0153
## freqescovacat2-veces al d\xeda -0.338 0.143 -2.37 0.0203
## freqescovacat3-veces al d\xeda -0.422 0.139 -3.05 0.0032
##
## (Intercept)
## paisUru *
## escolmaerecat23cat2-high school
## escolmaerecat23cat3-elementary school **
## tipoesc2-Publica *
## freqescovacat2-veces al d\xeda *
## freqescovacat3-veces al d\xeda **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.002)
##
## Number of Fisher Scoring iterations: 4
confint(modelo1_multi.bin)
## 2.5 % 97.5 %
## (Intercept) -0.25846 0.38842
## paisUru 0.05186 0.56231
## escolmaerecat23cat2-high school -0.09862 0.71741
## escolmaerecat23cat3-elementary school 0.22855 1.19541
## tipoesc2-Publica 0.09657 0.82118
## freqescovacat2-veces al d\xeda -0.61759 -0.05853
## freqescovacat3-veces al d\xeda -0.69377 -0.15069
R POISSON UNIVARIADA
modelo1.poi <- svyglm(cpodbere ~ pais + escolmaerecat23cat + tipoesc + freqescovacat,
disenio_urubra, family = quasipoisson())
summary(modelo1.poi)
##
## Call:
## svyglm(formula = cpodbere ~ pais + escolmaerecat23cat + tipoesc +
## freqescovacat, disenio_urubra, family = quasipoisson())
##
## Survey design:
## svydesign(id = ~numer_esc, strata = ~pais, weights = ~round(weight.rec,
## 1), data = brasuru, nest = TRUE)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2107 0.1072 1.96 0.0531
## paisUru 0.4542 0.0752 6.04 5.5e-08
## escolmaerecat23cat2-high school 0.1893 0.1683 1.12 0.2644
## escolmaerecat23cat3-elementary school 0.3439 0.2039 1.69 0.0958
## tipoesc2-Publica 0.4052 0.1264 3.20 0.0020
## freqescovacat2-veces al d\xeda -0.1218 0.0795 -1.53 0.1297
## freqescovacat3-veces al d\xeda -0.2095 0.0679 -3.08 0.0029
##
## (Intercept) .
## paisUru ***
## escolmaerecat23cat2-high school
## escolmaerecat23cat3-elementary school .
## tipoesc2-Publica **
## freqescovacat2-veces al d\xeda
## freqescovacat3-veces al d\xeda **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.967)
##
## Number of Fisher Scoring iterations: 5
confint(modelo1.poi)
## 2.5 % 97.5 %
## (Intercept) 0.0005247 0.42080
## paisUru 0.3068496 0.60161
## escolmaerecat23cat2-high school -0.1406371 0.51916
## escolmaerecat23cat3-elementary school -0.0557032 0.74348
## tipoesc2-Publica 0.1573577 0.65296
## freqescovacat2-veces al d\xeda -0.2775747 0.03400
## freqescovacat3-veces al d\xeda -0.3426286 -0.07638