#
# 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
library(car)
## Loading required package: MASS
## Loading required package: nnet
disenio.post$variables$tabaco.rec <- recode(disenio.post$variables$tabaco, "1=0;2:4=1")
disenio.post$variables$tabaco.rec <- as.factor(disenio.post$variables$tabaco.rec)
disenio.post$variables$Sit_protesis.rec <- recode(disenio.post$variables$Sit_protesis,
"'Completa'='2-Completa-PPR';'PPR'='2-Completa-PPR';'Resto'='1-Resto';'Sin_prote'='0-Sin_prote'")
levels(disenio.post$variables$Sit_protesis.rec)
## [1] "0-Sin_prote" "1-Resto" "2-Completa-PPR"
disenio.post$variables$alcohol.rec <- recode(disenio.post$variables$alcohol,
"1=0;2:4=1")
#Genero, Edad,INSE,Tabaco,mate,alcohol, frutasyverduras,protesis
library(survey)
svychisq(~Prev.lesio.rec + sexo, disenio.post, statistic = "Wald")
##
## Design-based Wald test of association
##
## data: svychisq(~Prev.lesio.rec + sexo, disenio.post, statistic = "Wald")
## F = 1.285, ndf = 1, ddf = 1475, p-value = 0.2571
modelo.bin1 <- svyglm(factor(Prev.lesio.rec) ~ sexo, design = disenio.post,
family = quasibinomial())
summary(modelo.bin1)
##
## Call:
## svyglm(formula = factor(Prev.lesio.rec) ~ sexo, design = disenio.post,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio, ~mue_eta_sex, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.104 0.145 -14.47 <2e-16 ***
## sexoM -0.265 0.239 -1.11 0.27
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.001)
##
## Number of Fisher Scoring iterations: 4
confint(modelo.bin1)
## 2.5 % 97.5 %
## (Intercept) -2.3890 -1.8192
## sexoM -0.7334 0.2026
exp(modelo.bin1$coefficients)
## (Intercept) sexoM
## 0.1220 0.7669
reporte <- data.frame(modelo.bin1$coefficients, exp(modelo.bin1$coefficients),
exp(confint(modelo.bin1)))
colnames(reporte) <- c("coef", "OR", "LIIC_OR", "LSIC_OR")
reporte
## coef OR LIIC_OR LSIC_OR
## (Intercept) -2.1041 0.1220 0.09172 0.1622
## sexoM -0.2654 0.7669 0.48028 1.2245
svychisq(~Prev.lesio.rec + tramo_eta, disenio.post, statistic = "Wald")
##
## Design-based Wald test of association
##
## data: svychisq(~Prev.lesio.rec + tramo_eta, disenio.post, statistic = "Wald")
## F = 15.1, ndf = 2, ddf = 1475, p-value = 3.211e-07
modelo.bin2 <- svyglm(factor(Prev.lesio.rec) ~ tramo_eta, design = disenio.post,
family = quasibinomial())
summary(modelo.bin2)
##
## Call:
## svyglm(formula = factor(Prev.lesio.rec) ~ tramo_eta, design = disenio.post,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio, ~mue_eta_sex, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.007 0.232 -12.97 < 2e-16 ***
## tramo_etaE2 1.032 0.324 3.19 0.0015 **
## tramo_etaE3 1.347 0.273 4.94 8.7e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.001)
##
## Number of Fisher Scoring iterations: 5
confint(modelo.bin2)
## 2.5 % 97.5 %
## (Intercept) -3.4615 -2.553
## tramo_etaE2 0.3976 1.667
## tramo_etaE3 0.8127 1.881
exp(modelo.bin2$coefficients)
## (Intercept) tramo_etaE2 tramo_etaE3
## 0.04943 2.80773 3.84616
reporte <- data.frame(modelo.bin2$coefficients, exp(modelo.bin2$coefficients),
exp(confint(modelo.bin2)))
colnames(reporte) <- c("coef", "OR", "LIIC_OR", "LSIC_OR")
reporte
## coef OR LIIC_OR LSIC_OR
## (Intercept) -3.007 0.04943 0.03138 0.07785
## tramo_etaE2 1.032 2.80773 1.48831 5.29685
## tramo_etaE3 1.347 3.84616 2.25394 6.56314
svychisq(~Prev.lesio.rec + INSE1, disenio.post, statistic = "Wald")
##
## Design-based Wald test of association
##
## data: svychisq(~Prev.lesio.rec + INSE1, disenio.post, statistic = "Wald")
## F = 13.75, ndf = 2, ddf = 1475, p-value = 1.21e-06
modelo.bin3 <- svyglm(factor(Prev.lesio.rec) ~ INSE1, design = disenio.post,
family = quasibinomial())
summary(modelo.bin3)
##
## Call:
## svyglm(formula = factor(Prev.lesio.rec) ~ INSE1, design = disenio.post,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio, ~mue_eta_sex, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.897 0.160 -11.89 <2e-16 ***
## INSE12-MEDIO -0.599 0.234 -2.56 0.0106 *
## INSE13-ALTO -3.344 1.027 -3.26 0.0012 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.001)
##
## Number of Fisher Scoring iterations: 7
confint(modelo.bin3)
## 2.5 % 97.5 %
## (Intercept) -2.210 -1.584
## INSE12-MEDIO -1.057 -0.140
## INSE13-ALTO -5.357 -1.331
exp(modelo.bin3$coefficients)
## (Intercept) INSE12-MEDIO INSE13-ALTO
## 0.1500 0.5495 0.0353
reporte <- data.frame(modelo.bin3$coefficients, exp(modelo.bin3$coefficients),
exp(confint(modelo.bin3)))
colnames(reporte) <- c("coef", "OR", "LIIC_OR", "LSIC_OR")
reporte
## coef OR LIIC_OR LSIC_OR
## (Intercept) -1.8972 0.1500 0.109703 0.2051
## INSE12-MEDIO -0.5987 0.5495 0.347364 0.8693
## INSE13-ALTO -3.3440 0.0353 0.004717 0.2641
svychisq(~Prev.lesio.rec + tabaco.rec, disenio.post, statistic = "Wald")
##
## Design-based Wald test of association
##
## data: svychisq(~Prev.lesio.rec + tabaco.rec, disenio.post, statistic = "Wald")
## F = 1.094, ndf = 1, ddf = 1475, p-value = 0.2958
modelo.bin4 <- svyglm(factor(Prev.lesio.rec) ~ factor(tabaco.rec), design = disenio.post,
family = quasibinomial())
summary(modelo.bin4)
##
## Call:
## svyglm(formula = factor(Prev.lesio.rec) ~ factor(tabaco.rec),
## design = disenio.post, family = quasibinomial())
##
## Survey design:
## postStratify(disenio, ~mue_eta_sex, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.151 0.131 -16.47 <2e-16 ***
## factor(tabaco.rec)1 -0.291 0.298 -0.98 0.33
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9934)
##
## Number of Fisher Scoring iterations: 4
confint(modelo.bin4)
## 2.5 % 97.5 %
## (Intercept) -2.4071 -1.895
## factor(tabaco.rec)1 -0.8749 0.292
exp(modelo.bin4$coefficients)
## (Intercept) factor(tabaco.rec)1
## 0.1164 0.7472
reporte <- data.frame(modelo.bin4$coefficients, exp(modelo.bin4$coefficients),
exp(confint(modelo.bin4)))
colnames(reporte) <- c("coef", "OR", "LIIC_OR", "LSIC_OR")
reporte
## coef OR LIIC_OR LSIC_OR
## (Intercept) -2.1511 0.1164 0.09007 0.1503
## factor(tabaco.rec)1 -0.2914 0.7472 0.41691 1.3391
svychisq(~Prev.lesio.rec + n5consumem, disenio.post, statistic = "Wald")
##
## Design-based Wald test of association
##
## data: svychisq(~Prev.lesio.rec + n5consumem, disenio.post, statistic = "Wald")
## F = 3.167, ndf = 1, ddf = 1475, p-value = 0.07532
modelo.bin5 <- svyglm(factor(Prev.lesio.rec) ~ n5consumem, design = disenio.post,
family = quasibinomial())
summary(modelo.bin5)
##
## Call:
## svyglm(formula = factor(Prev.lesio.rec) ~ n5consumem, design = disenio.post,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio, ~mue_eta_sex, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.133 0.133 -16.06 <2e-16 ***
## n5consumem2-No toma mate -0.456 0.277 -1.65 0.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9977)
##
## Number of Fisher Scoring iterations: 5
confint(modelo.bin5)
## 2.5 % 97.5 %
## (Intercept) -2.3933 -1.8728
## n5consumem2-No toma mate -0.9984 0.0871
exp(modelo.bin5$coefficients)
## (Intercept) n5consumem2-No toma mate
## 0.1185 0.6340
reporte <- data.frame(modelo.bin5$coefficients, exp(modelo.bin5$coefficients),
exp(confint(modelo.bin5)))
colnames(reporte) <- c("coef", "OR", "LIIC_OR", "LSIC_OR")
reporte
## coef OR LIIC_OR LSIC_OR
## (Intercept) -2.1330 0.1185 0.09133 0.1537
## n5consumem2-No toma mate -0.4556 0.6340 0.36847 1.0910
svychisq(~Prev.lesio.rec + alcohol.rec, disenio.post, statistic = "Wald")
##
## Design-based Wald test of association
##
## data: svychisq(~Prev.lesio.rec + alcohol.rec, disenio.post, statistic = "Wald")
## F = 4.024, ndf = 1, ddf = 1475, p-value = 0.04504
modelo.bin6 <- svyglm(factor(Prev.lesio.rec) ~ alcohol.rec, design = disenio.post,
family = quasibinomial())
summary(modelo.bin6)
##
## Call:
## svyglm(formula = factor(Prev.lesio.rec) ~ alcohol.rec, design = disenio.post,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio, ~mue_eta_sex, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.879 0.188 -9.97 <2e-16 ***
## alcohol.rec1 -0.523 0.241 -2.17 0.03 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9952)
##
## Number of Fisher Scoring iterations: 4
confint(modelo.bin6)
## 2.5 % 97.5 %
## (Intercept) -2.248 -1.5097
## alcohol.rec1 -0.996 -0.0503
exp(modelo.bin6$coefficients)
## (Intercept) alcohol.rec1
## 0.1528 0.5927
reporte <- data.frame(modelo.bin6$coefficients, exp(modelo.bin6$coefficients),
exp(confint(modelo.bin6)))
colnames(reporte) <- c("coef", "OR", "LIIC_OR", "LSIC_OR")
reporte
## coef OR LIIC_OR LSIC_OR
## (Intercept) -1.8789 0.1528 0.1056 0.2210
## alcohol.rec1 -0.5231 0.5927 0.3694 0.9509
svychisq(~Prev.lesio.rec + frutas_verduras.rec1, disenio.post, statistic = "Wald")
##
## Design-based Wald test of association
##
## data: svychisq(~Prev.lesio.rec + frutas_verduras.rec1, disenio.post, statistic = "Wald")
## F = 0.0757, ndf = 1, ddf = 1475, p-value = 0.7833
modelo.bin7 <- svyglm(factor(Prev.lesio.rec) ~ frutas_verduras.rec1, design = disenio.post,
family = quasibinomial())
summary(modelo.bin7)
##
## Call:
## svyglm(formula = factor(Prev.lesio.rec) ~ frutas_verduras.rec1,
## design = disenio.post, family = quasibinomial())
##
## Survey design:
## postStratify(disenio, ~mue_eta_sex, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.327 0.419 -5.56 3.2e-08 ***
## frutas_verduras.rec1<5 0.115 0.435 0.26 0.79
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9897)
##
## Number of Fisher Scoring iterations: 4
confint(modelo.bin7)
## 2.5 % 97.5 %
## (Intercept) -3.1479 -1.5070
## frutas_verduras.rec1<5 -0.7383 0.9685
exp(modelo.bin7$coefficients)
## (Intercept) frutas_verduras.rec1<5
## 0.09754 1.12199
reporte <- data.frame(modelo.bin7$coefficients, exp(modelo.bin7$coefficients),
exp(confint(modelo.bin7)))
colnames(reporte) <- c("coef", "OR", "LIIC_OR", "LSIC_OR")
reporte
## coef OR LIIC_OR LSIC_OR
## (Intercept) -2.3275 0.09754 0.04294 0.2216
## frutas_verduras.rec1<5 0.1151 1.12199 0.47794 2.6340
svychisq(~Prev.lesio.rec + Sit_protesis.rec, disenio.post, statistic = "Wald")
##
## Design-based Wald test of association
##
## data: svychisq(~Prev.lesio.rec + Sit_protesis.rec, disenio.post, statistic = "Wald")
## F = 17.94, ndf = 2, ddf = 1475, p-value = 1.996e-08
modelo.bin8 <- svyglm(factor(Prev.lesio.rec) ~ Sit_protesis.rec, design = disenio.post,
family = quasibinomial())
summary(modelo.bin8)
##
## Call:
## svyglm(formula = factor(Prev.lesio.rec) ~ Sit_protesis.rec, design = disenio.post,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio, ~mue_eta_sex, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.967 0.200 -14.86 < 2e-16 ***
## Sit_protesis.rec1-Resto 0.573 0.587 0.98 0.33
## Sit_protesis.rec2-Completa-PPR 1.772 0.254 6.98 4.6e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9928)
##
## Number of Fisher Scoring iterations: 5
confint(modelo.bin8)
## 2.5 % 97.5 %
## (Intercept) -3.359 -2.576
## Sit_protesis.rec1-Resto -0.578 1.724
## Sit_protesis.rec2-Completa-PPR 1.274 2.270
exp(modelo.bin8$coefficients)
## (Intercept) Sit_protesis.rec1-Resto
## 0.05144 1.77390
## Sit_protesis.rec2-Completa-PPR
## 5.88194
reporte <- data.frame(modelo.bin8$coefficients, exp(modelo.bin8$coefficients),
exp(confint(modelo.bin8)))
colnames(reporte) <- c("coef", "OR", "LIIC_OR", "LSIC_OR")
reporte
## coef OR LIIC_OR LSIC_OR
## (Intercept) -2.9674 0.05144 0.03477 0.07609
## Sit_protesis.rec1-Resto 0.5732 1.77390 0.56100 5.60916
## Sit_protesis.rec2-Completa-PPR 1.7719 5.88194 3.57522 9.67695
disenio.post$variables$perio.rec1 <- recode(disenio.post$variables$perio.rec,
"'1-sano'='1-sano';'2-leve-moderada'='2-Enf.Perio';'3-grave'='2-Enf.Perio';'4-desdentada'='3-desdentada'")
table(disenio.post$variables$perio.rec, disenio.post$variables$perio.rec1)
##
## 1-sano 2-Enf.Perio 3-desdentada
## 1-sano 827 0 0
## 2-leve-moderada 0 237 0
## 3-grave 0 75 0
## 4-desdentada 0 0 346
svychisq(~Prev.lesio.rec + perio.rec1, disenio.post, statistic = "Wald")
##
## Design-based Wald test of association
##
## data: svychisq(~Prev.lesio.rec + perio.rec1, disenio.post, statistic = "Wald")
## F = 12.59, ndf = 2, ddf = 1475, p-value = 3.772e-06
modelo.bin9 <- svyglm(factor(Prev.lesio.rec) ~ perio.rec1, design = disenio.post,
family = quasibinomial())
summary(modelo.bin9)
##
## Call:
## svyglm(formula = factor(Prev.lesio.rec) ~ perio.rec1, design = disenio.post,
## family = quasibinomial())
##
## Survey design:
## postStratify(disenio, ~mue_eta_sex, tabla.pob)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.7563 0.1884 -14.63 <2e-16 ***
## perio.rec12-Enf.Perio 0.0419 0.3651 0.11 0.91
## perio.rec13-desdentada 1.6038 0.2584 6.21 7e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.001)
##
## Number of Fisher Scoring iterations: 5
confint(modelo.bin9)
## 2.5 % 97.5 %
## (Intercept) -3.1257 -2.3870
## perio.rec12-Enf.Perio -0.6737 0.7574
## perio.rec13-desdentada 1.0973 2.1102
exp(modelo.bin9$coefficients)
## (Intercept) perio.rec12-Enf.Perio perio.rec13-desdentada
## 0.06352 1.04276 4.97168
reporte <- data.frame(modelo.bin9$coefficients, exp(modelo.bin9$coefficients),
exp(confint(modelo.bin9)))
colnames(reporte) <- c("coef", "OR", "LIIC_OR", "LSIC_OR")
reporte
## coef OR LIIC_OR LSIC_OR
## (Intercept) -2.75634 0.06352 0.04391 0.09191
## perio.rec12-Enf.Perio 0.04187 1.04276 0.50984 2.13273
## perio.rec13-desdentada 1.60376 4.97168 2.99616 8.24976