Regresion de poisson multiple para muestra unificada (19 de octubre)
# 28 de octubre 2013
load("~/Dropbox/odontologia/maestria_anunziatta/BancoUnidoBrasUru/BRASURU(28102013).RData")
library(survey)
## Attaching package: 'survey'
##
## The following object(s) are masked from 'package:graphics':
##
## dotchart
levels(disenio_urubra$variables$prevoms)
## [1] "1-SinCavi" "2-conCavi"
table(disenio_urubra$variables$cpodoms, disenio_urubra$variables$prevoms)
##
## 1-SinCavi 2-conCavi
## 0 1105 0
## 1 0 495
## 2 0 404
## 3 0 259
## 4 0 217
## 5 0 108
## 6 0 38
## 7 0 22
## 8 0 14
## 9 0 12
## 10 0 3
## 11 0 3
## 14 0 1
## 15 0 1
modelo2multi.poi <- svyglm(cpodoms ~ pais + socioecon4cat + escolmaerecat23cat +
visidentcatonde + visiquando + freqescovacat + isg45 + tipoesc, disenio_urubra,
family = quasipoisson())
summary(modelo2multi.poi)
##
## Call:
## svyglm(formula = cpodoms ~ pais + socioecon4cat + escolmaerecat23cat +
## visidentcatonde + visiquando + freqescovacat + isg45 + tipoesc,
## 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
## (Intercept) -0.5952 0.1992 -2.99
## paisUru 0.3970 0.0808 4.91
## socioecon4cat2-medio-alto 0.3867 0.2835 1.36
## socioecon4cat3-medio-bajo 0.5319 0.2637 2.02
## socioecon4cat4-bajo 0.4832 0.2769 1.75
## escolmaerecat23cat2-high school 0.0923 0.1746 0.53
## escolmaerecat23cat3-elementary school 0.2322 0.1953 1.19
## visidentcatonde2-Publico 0.1547 0.0733 2.11
## visidentcatonde3-Nunca fue al dentista -0.2213 0.4941 -0.45
## visiquando2- m\xe1s de 2 a\xf1os -0.1427 0.1317 -1.08
## visiquando3- nunca fue 0.1103 0.4826 0.23
## freqescovacat2-veces al d\xeda -0.0632 0.0773 -0.82
## freqescovacat3-veces al d\xeda -0.1981 0.0772 -2.57
## isg452- 45 a 60 0.0686 0.0718 0.96
## isg453- >=60 0.3136 0.0652 4.81
## tipoesc2-Publica 0.3237 0.1036 3.12
## Pr(>|t|)
## (Intercept) 0.0039 **
## paisUru 6.2e-06 ***
## socioecon4cat2-medio-alto 0.1771
## socioecon4cat3-medio-bajo 0.0478 *
## socioecon4cat4-bajo 0.0856 .
## escolmaerecat23cat2-high school 0.5987
## escolmaerecat23cat3-elementary school 0.2388
## visidentcatonde2-Publico 0.0386 *
## visidentcatonde3-Nunca fue al dentista 0.6557
## visiquando2- m\xe1s de 2 a\xf1os 0.2824
## visiquando3- nunca fue 0.8200
## freqescovacat2-veces al d\xeda 0.4167
## freqescovacat3-veces al d\xeda 0.0125 *
## isg452- 45 a 60 0.3429
## isg453- >=60 9.2e-06 ***
## tipoesc2-Publica 0.0026 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.047)
##
## Number of Fisher Scoring iterations: 6
anova(modelo2multi.poi)
## Anova table: (Rao-Scott LRT)
## svyglm(formula = cpodoms ~ pais, disenio_urubra, family = quasipoisson())
## stats DEff df ddf p
## pais 25.8 8.36 1.00 80 0.08532 .
## socioecon4cat 331.2 8.13 3.00 77 2.1e-05 ***
## escolmaerecat23cat 161.1 6.94 2.00 75 0.00043 ***
## visidentcatonde 61.2 3.23 2.00 73 0.00028 ***
## visiquando 52.1 5.54 2.00 71 0.02327 *
## freqescovacat 136.1 4.27 2.00 69 1.8e-05 ***
## isg45 52.5 2.30 2.00 67 8.0e-05 ***
## tipoesc 39.0 3.83 1.00 66 0.00230 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
confint(modelo2multi.poi)
## 2.5 % 97.5 %
## (Intercept) -0.98559 -0.20482
## paisUru 0.23860 0.55539
## socioecon4cat2-medio-alto -0.16883 0.94231
## socioecon4cat3-medio-bajo 0.01497 1.04874
## socioecon4cat4-bajo -0.05946 1.02589
## escolmaerecat23cat2-high school -0.24980 0.43443
## escolmaerecat23cat3-elementary school -0.15065 0.61502
## visidentcatonde2-Publico 0.01106 0.29825
## visidentcatonde3-Nunca fue al dentista -1.18982 0.74720
## visiquando2- m\xe1s de 2 a\xf1os -0.40090 0.11540
## visiquando3- nunca fue -0.83567 1.05625
## freqescovacat2-veces al d\xeda -0.21467 0.08832
## freqescovacat3-veces al d\xeda -0.34936 -0.04683
## isg452- 45 a 60 -0.07212 0.20926
## isg453- >=60 0.18574 0.44143
## tipoesc2-Publica 0.12067 0.52683
exp(modelo2multi.poi$coefficients)
## (Intercept)
## 0.5514
## paisUru
## 1.4874
## socioecon4cat2-medio-alto
## 1.4722
## socioecon4cat3-medio-bajo
## 1.7021
## socioecon4cat4-bajo
## 1.6213
## escolmaerecat23cat2-high school
## 1.0967
## escolmaerecat23cat3-elementary school
## 1.2614
## visidentcatonde2-Publico
## 1.1673
## visidentcatonde3-Nunca fue al dentista
## 0.8015
## visiquando2- m\xe1s de 2 a\xf1os
## 0.8670
## visiquando3- nunca fue
## 1.1166
## freqescovacat2-veces al d\xeda
## 0.9388
## freqescovacat3-veces al d\xeda
## 0.8203
## isg452- 45 a 60
## 1.0710
## isg453- >=60
## 1.3683
## tipoesc2-Publica
## 1.3823
modelo3multi.poi <- svyglm(cpodoms ~ pais + escolmaerecat23cat + freqescovacat +
isg45, disenio_urubra, family = quasipoisson())
summary(modelo3multi.poi)
##
## Call:
## svyglm(formula = cpodoms ~ pais + escolmaerecat23cat + freqescovacat +
## isg45, 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) -0.1605 0.1602 -1.00 0.31976
## paisUru 0.3987 0.0654 6.09 4.5e-08
## escolmaerecat23cat2-high school 0.3580 0.1310 2.73 0.00783
## escolmaerecat23cat3-elementary school 0.6005 0.1496 4.01 0.00014
## freqescovacat2-veces al d\xeda -0.1024 0.0792 -1.29 0.20017
## freqescovacat3-veces al d\xeda -0.2203 0.0752 -2.93 0.00451
## isg452- 45 a 60 0.1042 0.0727 1.43 0.15571
## isg453- >=60 0.3526 0.0703 5.02 3.5e-06
##
## (Intercept)
## paisUru ***
## escolmaerecat23cat2-high school **
## escolmaerecat23cat3-elementary school ***
## freqescovacat2-veces al d\xeda
## freqescovacat3-veces al d\xeda **
## isg452- 45 a 60
## isg453- >=60 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.121)
##
## Number of Fisher Scoring iterations: 6
confint(modelo3multi.poi)
## 2.5 % 97.5 %
## (Intercept) -0.4746 0.15355
## paisUru 0.2705 0.52689
## escolmaerecat23cat2-high school 0.1013 0.61473
## escolmaerecat23cat3-elementary school 0.3073 0.89364
## freqescovacat2-veces al d\xeda -0.2576 0.05286
## freqescovacat3-veces al d\xeda -0.3677 -0.07291
## isg452- 45 a 60 -0.0382 0.24663
## isg453- >=60 0.2148 0.49029
exp(modelo3multi.poi$coefficients)
## (Intercept)
## 0.8517
## paisUru
## 1.4899
## escolmaerecat23cat2-high school
## 1.4305
## escolmaerecat23cat3-elementary school
## 1.8230
## freqescovacat2-veces al d\xeda
## 0.9027
## freqescovacat3-veces al d\xeda
## 0.8023
## isg452- 45 a 60
## 1.1098
## isg453- >=60
## 1.4227
anova(modelo3multi.poi)
## Anova table: (Rao-Scott LRT)
## svyglm(formula = cpodoms ~ pais, disenio_urubra, family = quasipoisson())
## stats DEff df ddf p
## pais 25.8 8.36 1.00 80 0.085 .
## escolmaerecat23cat 353.4 5.69 2.00 78 2.8e-08 ***
## freqescovacat 143.6 4.64 2.00 76 2.4e-05 ***
## isg45 60.4 2.50 2.00 74 4.2e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(modelo3multi.poi, modelo2multi.poi)
## Working (Rao-Scott+F) LRT for socioecon4cat visidentcatonde visiquando tipoesc
## in svyglm(formula = cpodoms ~ pais + socioecon4cat + escolmaerecat23cat +
## visidentcatonde + visiquando + freqescovacat + isg45 + tipoesc,
## disenio_urubra, family = quasipoisson())
## Working 2logLR = 46.52 p= 0.00074
## (scale factors: 3 1.8 1.2 0.66 0.5 0.39 0.26 0.19 ); denominator df= 66