Cuadros nuevos 22122013
load("~/Dropbox/odontologia/relevamiento/casnati/muestra_global/muestra_global.RData")
#
# load('C:/Users/usuario/Dropbox/odontologia/relevamiento/casnati/muestra_global/muestra_global.RData')
library(survey)
##
## Attaching package: 'survey'
##
## The following object(s) are masked from 'package:graphics':
##
## dotchart
tabla18 <- svyby(~Prev.lesio, ~INSE1, disenio.post, svymean, na.rm = TRUE, deff = FALSE)
round(ftable(tabla18) * 100, 1)
## Prev.lesiono Prev.lesiosi
## INSE1
## 1-BAJO svymean 87.0 13.0
## SE 1.8 1.8
## 2-MEDIO svymean 92.4 7.6
## SE 1.2 1.2
## 3-ALTO svymean 99.5 0.5
## SE 0.5 0.5
tabla19 <- svyby(~Prev.lesio, ~as.factor(tabaco.rec), disenio.post, svymean,
na.rm = TRUE, deff = TRUE)
round(tabla19[, 2:4] * 100, 1)
## Prev.lesiono Prev.lesiosi se.Prev.lesiono
## 0 89.6 10.4 1.2
## 1 92.0 8.0 2.0
round(confint(tabla19) * 100, 2)
## 2.5 % 97.5 %
## 0:Prev.lesiono 87.19 91.97
## 1:Prev.lesiono 88.16 95.84
## 0:Prev.lesiosi 8.03 12.81
## 1:Prev.lesiosi 4.16 11.84
svychisq(~Prev.lesio + tabaco.rec, disenio.post, statistic = "Wald")
##
## Design-based Wald test of association
##
## data: svychisq(~Prev.lesio + tabaco.rec, disenio.post, statistic = "Wald")
## F = 1.094, ndf = 1, ddf = 1475, p-value = 0.2958
tabla20 <- svyby(~Prev.lesio, ~as.factor(alcohol.rec), disenio.post, svymean,
na.rm = TRUE, deff = TRUE)
round(tabla20[, 2:4] * 100, 1)
## Prev.lesiono Prev.lesiosi se.Prev.lesiono
## 0 86.7 13.3 2.2
## 1 91.7 8.3 1.1
round(confint(tabla20) * 100, 2)
## 2.5 % 97.5 %
## 0:Prev.lesiono 82.50 90.99
## 1:Prev.lesiono 89.46 93.94
## 0:Prev.lesiosi 9.01 17.50
## 1:Prev.lesiosi 6.06 10.54
svychisq(~Prev.lesio + alcohol.rec, disenio.post, statistic = "Wald")
##
## Design-based Wald test of association
##
## data: svychisq(~Prev.lesio + alcohol.rec, disenio.post, statistic = "Wald")
## F = 4.024, ndf = 1, ddf = 1475, p-value = 0.04504
tabla21 <- svyby(~Prev.lesio, ~as.factor(frutas_verduras.rec1), disenio.post,
svymean, na.rm = TRUE, deff = TRUE)
round(tabla21[, 2:4] * 100, 1)
## Prev.lesiono Prev.lesiosi se.Prev.lesiono
## >=5 91.1 8.9 3.4
## <5 90.1 9.9 1.1
round(confint(tabla21) * 100, 2)
## 2.5 % 97.5 %
## >=5:Prev.lesiono 84.47 97.76
## <5:Prev.lesiono 88.01 92.26
## >=5:Prev.lesiosi 2.24 15.53
## <5:Prev.lesiosi 7.74 11.99
svychisq(~Prev.lesio + frutas_verduras.rec1, disenio.post, statistic = "Wald")
##
## Design-based Wald test of association
##
## data: svychisq(~Prev.lesio + frutas_verduras.rec1, disenio.post, statistic = "Wald")
## F = 0.0757, ndf = 1, ddf = 1475, p-value = 0.7833
tabla22 <- svyby(~Prev.lesio, ~as.factor(Sit_protesis.rec), disenio.post, svymean,
na.rm = TRUE, deff = TRUE)
round(tabla22[, 2:4] * 100, 1)
## Prev.lesiono Prev.lesiosi se.Prev.lesiono
## 0-Sin_prote 95.1 4.9 0.9
## 1-Resto 91.6 8.4 4.2
## 2-Completa-PPR 76.8 23.2 2.8
round(confint(tabla22) * 100, 2)
## 2.5 % 97.5 %
## 0-Sin_prote:Prev.lesiono 93.29 96.93
## 1-Resto:Prev.lesiono 83.36 99.92
## 2-Completa-PPR:Prev.lesiono 71.27 82.27
## 0-Sin_prote:Prev.lesiosi 3.07 6.71
## 1-Resto:Prev.lesiosi 0.08 16.64
## 2-Completa-PPR:Prev.lesiosi 17.73 28.73
svychisq(~Prev.lesio + Sit_protesis.rec, disenio.post, statistic = "Wald")
##
## Design-based Wald test of association
##
## data: svychisq(~Prev.lesio + Sit_protesis.rec, disenio.post, statistic = "Wald")
## F = 17.94, ndf = 2, ddf = 1475, p-value = 1.996e-08
tabla23 <- svyby(~Prev.lesio, ~as.factor(perio.rec1), disenio.post, svymean,
na.rm = TRUE, deff = TRUE)
round(tabla23[, 2:4] * 100, 1)
## Prev.lesiono Prev.lesiosi se.Prev.lesiono
## 1-sano 94.0 6.0 1.1
## 2-Enf.Perio 93.8 6.2 1.8
## 3-desdentada 76.0 24.0 3.2
round(confint(tabla23) * 100, 2)
## 2.5 % 97.5 %
## 1-sano:Prev.lesiono 91.95 96.10
## 2-Enf.Perio:Prev.lesiono 90.22 97.36
## 3-desdentada:Prev.lesiono 69.65 82.35
## 1-sano:Prev.lesiosi 3.90 8.05
## 2-Enf.Perio:Prev.lesiosi 2.64 9.78
## 3-desdentada:Prev.lesiosi 17.65 30.35
svychisq(~Prev.lesio + perio.rec1, disenio.post, statistic = "Wald")
##
## Design-based Wald test of association
##
## data: svychisq(~Prev.lesio + perio.rec1, disenio.post, statistic = "Wald")
## F = 12.59, ndf = 2, ddf = 1475, p-value = 3.772e-06
modelo.multi1 <- svyglm(factor(Prev.lesio.rec) ~ tramo_eta + INSE1 + alcohol.rec +
perio.rec1 + muestra, design = disenio.post, family = quasibinomial())
summary(modelo.multi1)
##
## Call:
## svyglm(formula = factor(Prev.lesio.rec) ~ tramo_eta + INSE1 +
## alcohol.rec + perio.rec1 + muestra, design = disenio.post,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio, ~mue_eta_sex, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.240 0.313 -7.15 1.4e-12 ***
## tramo_etaE2 0.781 0.335 2.33 0.0200 *
## tramo_etaE3 0.411 0.437 0.94 0.3471
## INSE12-MEDIO -0.546 0.253 -2.16 0.0312 *
## INSE13-ALTO -3.103 1.053 -2.95 0.0033 **
## alcohol.rec1 -0.207 0.250 -0.83 0.4089
## perio.rec12-Enf.Perio -0.282 0.362 -0.78 0.4362
## perio.rec13-desdentada 1.189 0.394 3.02 0.0026 **
## muestraMon -0.556 0.250 -2.23 0.0262 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.056)
##
## Number of Fisher Scoring iterations: 7
confint(modelo.multi1)
## 2.5 % 97.5 %
## (Intercept) -2.8539 -1.62570
## tramo_etaE2 0.1237 1.43817
## tramo_etaE3 -0.4456 1.26779
## INSE12-MEDIO -1.0430 -0.04989
## INSE13-ALTO -5.1669 -1.03992
## alcohol.rec1 -0.6976 0.28392
## perio.rec12-Enf.Perio -0.9903 0.42705
## perio.rec13-desdentada 0.4160 1.96170
## muestraMon -1.0460 -0.06653
exp(modelo.multi1$coefficients)
## (Intercept) tramo_etaE2 tramo_etaE3
## 0.1065 2.1835 1.5085
## INSE12-MEDIO INSE13-ALTO alcohol.rec1
## 0.5790 0.0449 0.8132
## perio.rec12-Enf.Perio perio.rec13-desdentada muestraMon
## 0.7546 3.2834 0.5733
reporte.multi <- data.frame(modelo.multi1$coefficients, exp(modelo.multi1$coefficients),
exp(confint(modelo.multi1)))
colnames(reporte.multi) <- c("coef", "OR", "LIIC_OR", "LSIC_OR")
round(reporte.multi, 3)
## coef OR LIIC_OR LSIC_OR
## (Intercept) -2.240 0.106 0.058 0.197
## tramo_etaE2 0.781 2.184 1.132 4.213
## tramo_etaE3 0.411 1.508 0.640 3.553
## INSE12-MEDIO -0.546 0.579 0.352 0.951
## INSE13-ALTO -3.103 0.045 0.006 0.353
## alcohol.rec1 -0.207 0.813 0.498 1.328
## perio.rec12-Enf.Perio -0.282 0.755 0.371 1.533
## perio.rec13-desdentada 1.189 3.283 1.516 7.111
## muestraMon -0.556 0.573 0.351 0.936