Aca se analiza si los valores promedios de conteo de n
Como se trata de variables de conteo la forma d ever si las diferencias de superficies erosionadas promedio por variables independientes son significativas, no es adecuado hacer prueba t ajustadas por diseño muestral, ya que existe mucha asimetría. Para eso se propone hacer modelos de regresion POisson usando como variables explicativas las usadas para evaluar promedios y la variable de respuesta, logaritmo de la cantidad media de erosiones
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/diciembre2013/datos_licet_23122013.RData")
Severas.poi6 <- svyglm(Severas ~ Sexo.rec, diseniopost1.sub, family = quasipoisson())
## Warning: observations with zero weight not used for calculating dispersion
## Warning: observations with zero weight not used for calculating dispersion
regTermTest(Severas.poi6, ~Sexo.rec)
## Wald test for Sexo.rec
## in svyglm(formula = Severas ~ Sexo.rec, diseniopost1.sub, family = quasipoisson())
## F = 2,633 on 1 and 18 df: p= 0,12
Severas.poi7 <- svyglm(Severas ~ Nivel.Socieconomico.rec, diseniopost1.sub,
family = quasipoisson())
## Warning: observations with zero weight not used for calculating dispersion
## Warning: observations with zero weight not used for calculating dispersion
regTermTest(Severas.poi7, ~Nivel.Socieconomico.rec)
## Wald test for Nivel.Socieconomico.rec
## in svyglm(formula = Severas ~ Nivel.Socieconomico.rec, diseniopost1.sub,
## family = quasipoisson())
## F = 1,688 on 2 and 16 df: p= 0,22
summary(Severas.poi7)
##
## Call:
## svyglm(formula = Severas ~ Nivel.Socieconomico.rec, diseniopost1.sub,
## family = quasipoisson())
##
## Survey design:
## subset(diseniopost1, diseniopost1$variables$Erosinbord == 1)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0,4969 0,1319 3,77 0,0017 **
## Nivel.Socieconomico.rec2-MEDIO 0,0436 0,1955 0,22 0,8262
## Nivel.Socieconomico.rec3-ALTO 0,3038 0,1790 1,70 0,1089
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0,5582)
##
## Number of Fisher Scoring iterations: 4
Severas.poi8 <- svyglm(Severas ~ Nivel.Educativo.de.la.Madre1.rec, diseniopost1.sub,
family = quasipoisson())
## Warning: observations with zero weight not used for calculating dispersion
## Warning: observations with zero weight not used for calculating dispersion
regTermTest(Severas.poi8, ~Nivel.Educativo.de.la.Madre1.rec)
## Wald test for Nivel.Educativo.de.la.Madre1.rec
## in svyglm(formula = Severas ~ Nivel.Educativo.de.la.Madre1.rec,
## diseniopost1.sub, family = quasipoisson())
## F = 0,4399 on 3 and 15 df: p= 0,73
summary(Severas.poi8)
##
## Call:
## svyglm(formula = Severas ~ Nivel.Educativo.de.la.Madre1.rec,
## diseniopost1.sub, family = quasipoisson())
##
## Survey design:
## subset(diseniopost1, diseniopost1$variables$Erosinbord == 1)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0,7122 0,1894 3,76
## Nivel.Educativo.de.la.Madre1.rec2-Basic -0,1481 0,1711 -0,87
## Nivel.Educativo.de.la.Madre1.rec3-Medium -0,1729 0,4170 -0,41
## Nivel.Educativo.de.la.Madre1.rec4-High 0,0894 0,2736 0,33
## Pr(>|t|)
## (Intercept) 0,0019 **
## Nivel.Educativo.de.la.Madre1.rec2-Basic 0,4003
## Nivel.Educativo.de.la.Madre1.rec3-Medium 0,6843
## Nivel.Educativo.de.la.Madre1.rec4-High 0,7484
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0,6379)
##
## Number of Fisher Scoring iterations: 5
Severas.poi8a <- svyglm(Severas ~ Nive.Educativo.de.la.Madre2.rec, diseniopost1.sub,
family = quasipoisson())
## Warning: observations with zero weight not used for calculating dispersion
## Warning: observations with zero weight not used for calculating dispersion
regTermTest(Severas.poi8a, ~Nive.Educativo.de.la.Madre2.rec)
## Wald test for Nive.Educativo.de.la.Madre2.rec
## in svyglm(formula = Severas ~ Nive.Educativo.de.la.Madre2.rec, diseniopost1.sub,
## family = quasipoisson())
## F = 0,03581 on 2 and 16 df: p= 0,96
summary(Severas.poi8a)
##
## Call:
## svyglm(formula = Severas ~ Nive.Educativo.de.la.Madre2.rec, diseniopost1.sub,
## family = quasipoisson())
##
## Survey design:
## subset(diseniopost1, diseniopost1$variables$Erosinbord == 1)
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0,62117 0,15149
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL 0,03502 0,15724
## Nive.Educativo.de.la.Madre2.rec3-COLLEGE-UNIVERSITY -0,00569 0,23847
## t value Pr(>|t|)
## (Intercept) 4,10 0,00084 ***
## Nive.Educativo.de.la.Madre2.rec2-HIGH SCHOOL 0,22 0,82656
## Nive.Educativo.de.la.Madre2.rec3-COLLEGE-UNIVERSITY -0,02 0,98125
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0,6372)
##
## Number of Fisher Scoring iterations: 5
Severas.poi9 <- svyglm(Severas ~ Tipo.de.Escuela.rec, diseniopost1.sub, family = quasipoisson())
## Warning: observations with zero weight not used for calculating dispersion
## Warning: observations with zero weight not used for calculating dispersion
regTermTest(Severas.poi9, ~Tipo.de.Escuela.rec)
## Wald test for Tipo.de.Escuela.rec
## in svyglm(formula = Severas ~ Tipo.de.Escuela.rec, diseniopost1.sub,
## family = quasipoisson())
## F = 4,024 on 1 and 18 df: p= 0,06
summary(Severas.poi9)
##
## Call:
## svyglm(formula = Severas ~ Tipo.de.Escuela.rec, diseniopost1.sub,
## family = quasipoisson())
##
## Survey design:
## subset(diseniopost1, diseniopost1$variables$Erosinbord == 1)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0,976 0,135 7,24 9,9e-07 ***
## Tipo.de.Escuela.rec2-Private -0,412 0,205 -2,01 0,06 .
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0,605)
##
## Number of Fisher Scoring iterations: 4
Severas.poi10 <- svyglm(Severas ~ FrCepDenti.rec, diseniopost1.sub, family = quasipoisson())
## Warning: observations with zero weight not used for calculating dispersion
## Warning: observations with zero weight not used for calculating dispersion
regTermTest(Severas.poi10, ~FrCepDenti.rec)
## Wald test for FrCepDenti.rec
## in svyglm(formula = Severas ~ FrCepDenti.rec, diseniopost1.sub,
## family = quasipoisson())
## F = 3,696 on 2 and 17 df: p= 0,046
summary(Severas.poi10)
##
## Call:
## svyglm(formula = Severas ~ FrCepDenti.rec, diseniopost1.sub,
## family = quasipoisson())
##
## Survey design:
## subset(diseniopost1, diseniopost1$variables$Erosinbord == 1)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0,611 0,229 2,66 0,016
## FrCepDenti.rec2- 2 veces al dia 0,371 0,251 1,48 0,158
## FrCepDenti.rec3-3 o mas veces al dia -0,234 0,276 -0,85 0,409
##
## (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 quasipoisson family taken to be 0,4933)
##
## Number of Fisher Scoring iterations: 4
Severas.poi11 <- svyglm(Severas ~ UsoDentifrico3.rec, diseniopost1.sub, family = quasipoisson())
## Warning: observations with zero weight not used for calculating dispersion
## Warning: observations with zero weight not used for calculating dispersion
regTermTest(Severas.poi11, ~UsoDentifrico3.rec)
## Wald test for UsoDentifrico3.rec
## in svyglm(formula = Severas ~ UsoDentifrico3.rec, diseniopost1.sub,
## family = quasipoisson())
## F = 2,802 on 1 and 18 df: p= 0,11
summary(Severas.poi11)
##
## Call:
## svyglm(formula = Severas ~ UsoDentifrico3.rec, diseniopost1.sub,
## family = quasipoisson())
##
## Survey design:
## subset(diseniopost1, diseniopost1$variables$Erosinbord == 1)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0,616 0,135 4,57 0,00024 ***
## UsoDentifrico3.rec2-No 0,504 0,301 1,67 0,11144
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0,6305)
##
## Number of Fisher Scoring iterations: 4
Severas.poi12 <- svyglm(Severas ~ Consitencia_Cepillo.rec, diseniopost1.sub,
family = quasipoisson())
## Warning: observations with zero weight not used for calculating dispersion
## Warning: observations with zero weight not used for calculating dispersion
regTermTest(Severas.poi12, ~Consitencia_Cepillo.rec)
## Wald test for Consitencia_Cepillo.rec
## in svyglm(formula = Severas ~ Consitencia_Cepillo.rec, diseniopost1.sub,
## family = quasipoisson())
## F = 3,66 on 2 and 14 df: p= 0,053
summary(Severas.poi12)
##
## Call:
## svyglm(formula = Severas ~ Consitencia_Cepillo.rec, diseniopost1.sub,
## family = quasipoisson())
##
## Survey design:
## subset(diseniopost1, diseniopost1$variables$Erosinbord == 1)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0,302 0,137 2,20 0,045 *
## Consitencia_Cepillo.rec2-Medium 0,127 0,103 1,23 0,239
## Consitencia_Cepillo.rec3-Hard 0,578 0,300 1,92 0,075 .
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0,3934)
##
## Number of Fisher Scoring iterations: 4
Severas.poi13 <- svyglm(Severas ~ IGS.rec, diseniopost1.sub, family = quasipoisson())
## Warning: observations with zero weight not used for calculating dispersion
## Warning: observations with zero weight not used for calculating dispersion
regTermTest(Severas.poi13, ~IGS.rec)
## Wald test for IGS.rec
## in svyglm(formula = Severas ~ IGS.rec, diseniopost1.sub, family = quasipoisson())
## F = 15,08 on 2 and 17 df: p= 0,00017
summary(Severas.poi13)
##
## Call:
## svyglm(formula = Severas ~ IGS.rec, diseniopost1.sub, family = quasipoisson())
##
## Survey design:
## subset(diseniopost1, diseniopost1$variables$Erosinbord == 1)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5,63e-16 8,68e-15 0,06 0,94907
## IGS.rec2-De 45 a 60 6,44e-01 2,34e-01 2,75 0,01372 *
## IGS.rec3 -Menos o igual a 45 6,37e-01 1,50e-01 4,25 0,00054 ***
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0,6348)
##
## Number of Fisher Scoring iterations: 4
Severas.poi14 <- svyglm(Severas ~ RefrCola.rec1, diseniopost1.sub, family = quasipoisson())
## Warning: observations with zero weight not used for calculating dispersion
## Warning: observations with zero weight not used for calculating dispersion
regTermTest(Severas.poi14, ~RefrCola.rec1)
## Wald test for RefrCola.rec1
## in svyglm(formula = Severas ~ RefrCola.rec1, diseniopost1.sub, family = quasipoisson())
## F = 0,7695 on 1 and 17 df: p= 0,39
summary(Severas.poi14)
##
## Call:
## svyglm(formula = Severas ~ RefrCola.rec1, diseniopost1.sub, family = quasipoisson())
##
## Survey design:
## subset(diseniopost1, diseniopost1$variables$Erosinbord == 1)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0,730 0,109 6,72 3,6e-06 ***
## RefrCola.rec12-Mas de 3 veces -0,220 0,251 -0,88 0,39
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0,58)
##
## Number of Fisher Scoring iterations: 4
Severas.poi15 <- svyglm(Severas ~ JugFrutas.rec, diseniopost1.sub, family = quasipoisson())
## Warning: observations with zero weight not used for calculating dispersion
## Warning: observations with zero weight not used for calculating dispersion
regTermTest(Severas.poi15, ~JugFrutas.rec)
## Wald test for JugFrutas.rec
## in svyglm(formula = Severas ~ JugFrutas.rec, diseniopost1.sub, family = quasipoisson())
## F = 13,86 on 2 and 16 df: p= 0,00032
summary(Severas.poi15)
##
## Call:
## svyglm(formula = Severas ~ JugFrutas.rec, diseniopost1.sub, family = quasipoisson())
##
## Survey design:
## subset(diseniopost1, diseniopost1$variables$Erosinbord == 1)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7,71e-17 3,85e-15 -0,02 0,98428
## JugFrutas.rec2-Todos los dias 7,36e-01 1,71e-01 4,31 0,00054
## JugFrutas.rec3-Mas de 3 veces al dia 4,51e-01 1,78e-01 2,53 0,02235
##
## (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 quasipoisson family taken to be 0,562)
##
## Number of Fisher Scoring iterations: 4
Severas.poi16 <- svyglm(Severas ~ Bebidas_energizantes.rec1, diseniopost1.sub,
family = quasipoisson())
## Warning: observations with zero weight not used for calculating dispersion
## Warning: observations with zero weight not used for calculating dispersion
regTermTest(Severas.poi16, ~Bebidas_energizantes.rec1)
## Wald test for Bebidas_energizantes.rec1
## in svyglm(formula = Severas ~ Bebidas_energizantes.rec1, diseniopost1.sub,
## family = quasipoisson())
## F = 1,3 on 1 and 14 df: p= 0,27
summary(Severas.poi16)
##
## Call:
## svyglm(formula = Severas ~ Bebidas_energizantes.rec1, diseniopost1.sub,
## family = quasipoisson())
##
## Survey design:
## subset(diseniopost1, diseniopost1$variables$Erosinbord == 1)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0,604 0,163 3,70
## Bebidas_energizantes.rec1Mas de 3 veces al dia 0,255 0,224 1,14
## Pr(>|t|)
## (Intercept) 0,0024 **
## Bebidas_energizantes.rec1Mas de 3 veces al dia 0,2733
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0,5544)
##
## Number of Fisher Scoring iterations: 4
Severas.poi17 <- svyglm(Severas ~ Yogurt.rec1, diseniopost1.sub, family = quasipoisson())
## Warning: observations with zero weight not used for calculating dispersion
## Warning: observations with zero weight not used for calculating dispersion
regTermTest(Severas.poi17, ~Yogurt.rec1)
## Wald test for Yogurt.rec1
## in svyglm(formula = Severas ~ Yogurt.rec1, diseniopost1.sub, family = quasipoisson())
## F = 0,1133 on 1 and 17 df: p= 0,74
summary(Severas.poi17)
##
## Call:
## svyglm(formula = Severas ~ Yogurt.rec1, diseniopost1.sub, family = quasipoisson())
##
## Survey design:
## subset(diseniopost1, diseniopost1$variables$Erosinbord == 1)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0,661 0,162 4,07 0,00079 ***
## Yogurt.rec12-Mas de 3 veces -0,145 0,432 -0,34 0,74056
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0,6133)
##
## Number of Fisher Scoring iterations: 4
Severas.poi18 <- svyglm(Severas ~ Bruxismo.rec, diseniopost1.sub, family = quasipoisson())
## Warning: observations with zero weight not used for calculating dispersion
## Warning: observations with zero weight not used for calculating dispersion
regTermTest(Severas.poi18, ~Bruxismo.rec)
## Wald test for Bruxismo.rec
## in svyglm(formula = Severas ~ Bruxismo.rec, diseniopost1.sub, family = quasipoisson())
## F = 2,395 on 1 and 18 df: p= 0,14
summary(Severas.poi18)
##
## Call:
## svyglm(formula = Severas ~ Bruxismo.rec, diseniopost1.sub, family = quasipoisson())
##
## Survey design:
## subset(diseniopost1, diseniopost1$variables$Erosinbord == 1)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0,678 0,145 4,69 0,00018 ***
## Bruxismo.rec2-Yes -0,331 0,214 -1,55 0,13916
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0,5971)
##
## Number of Fisher Scoring iterations: 4
Severas.poi19 <- svyglm(Severas ~ bedeportediario.rec, diseniopost1.sub, family = quasipoisson())
## Warning: observations with zero weight not used for calculating dispersion
## Warning: observations with zero weight not used for calculating dispersion
regTermTest(Severas.poi19, ~bedeportediario.rec)
## Wald test for bedeportediario.rec
## in svyglm(formula = Severas ~ bedeportediario.rec, diseniopost1.sub,
## family = quasipoisson())
## F = 2,99 on 2 and 15 df: p= 0,081
summary(Severas.poi19)
##
## Call:
## svyglm(formula = Severas ~ bedeportediario.rec, diseniopost1.sub,
## family = quasipoisson())
##
## Survey design:
## subset(diseniopost1, diseniopost1$variables$Erosinbord == 1)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0,692882 0,135060 5,13 0,00012 ***
## bedeportediario.rec2-Gatorade -0,459737 0,241094 -1,91 0,07587 .
## bedeportediario.rec3-other 0,000266 0,135060 0,00 0,99846
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0,4979)
##
## Number of Fisher Scoring iterations: 4
Severas.poi21 <- svyglm(Severas ~ BuchTragar.rec, diseniopost1.sub, family = quasipoisson())
## Warning: observations with zero weight not used for calculating dispersion
## Warning: observations with zero weight not used for calculating dispersion
regTermTest(Severas.poi21, ~BuchTragar.rec)
## Wald test for BuchTragar.rec
## in svyglm(formula = Severas ~ BuchTragar.rec, diseniopost1.sub,
## family = quasipoisson())
## F = 7,422 on 1 and 16 df: p= 0,015
summary(Severas.poi20)
## Error: object 'Severas.poi20' not found
Severas.poi22 <- svyglm(Severas ~ FormBeber.rec, diseniopost1.sub, family = quasipoisson())
## Warning: observations with zero weight not used for calculating dispersion
## Warning: observations with zero weight not used for calculating dispersion
regTermTest(Severas.poi22, ~FormBeber.rec)
## Wald test for FormBeber.rec
## in svyglm(formula = Severas ~ FormBeber.rec, diseniopost1.sub, family = quasipoisson())
## F = 0,2905 on 2 and 16 df: p= 0,75
summary(Severas.poi22)
##
## Call:
## svyglm(formula = Severas ~ FormBeber.rec, diseniopost1.sub, family = quasipoisson())
##
## Survey design:
## subset(diseniopost1, diseniopost1$variables$Erosinbord == 1)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0,369 0,335 1,10 0,29
## FormBeber.rec2-Por el pico 0,271 0,412 0,66 0,52
## FormBeber.rec3-Con vaso 0,285 0,379 0,75 0,46
##
## (Dispersion parameter for quasipoisson family taken to be 0,6355)
##
## Number of Fisher Scoring iterations: 4
Severas.poi23 <- svyglm(Severas ~ Natac2vec.rec, diseniopost1.sub, family = quasipoisson())
## Warning: observations with zero weight not used for calculating dispersion
## Warning: observations with zero weight not used for calculating dispersion
regTermTest(Severas.poi23, ~Natac2vec.rec)
## Wald test for Natac2vec.rec
## in svyglm(formula = Severas ~ Natac2vec.rec, diseniopost1.sub, family = quasipoisson())
## F = 2,261 on 1 and 15 df: p= 0,15
summary(Severas.poi23)
##
## Call:
## svyglm(formula = Severas ~ Natac2vec.rec, diseniopost1.sub, family = quasipoisson())
##
## Survey design:
## subset(diseniopost1, diseniopost1$variables$Erosinbord == 1)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0,644 0,176 3,65 0,0024 **
## Natac2vec.rec2-Yes -0,335 0,223 -1,50 0,1534
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0,5248)
##
## Number of Fisher Scoring iterations: 5
Severas.poi24 <- svyglm(Severas ~ MedResp.rec, diseniopost1.sub, family = quasipoisson())
## Warning: observations with zero weight not used for calculating dispersion
## Warning: observations with zero weight not used for calculating dispersion
regTermTest(Severas.poi24, ~MedResp.rec)
## Wald test for MedResp.rec
## in svyglm(formula = Severas ~ MedResp.rec, diseniopost1.sub, family = quasipoisson())
## F = 3,608 on 1 and 18 df: p= 0,074
summary(Severas.poi24)
##
## Call:
## svyglm(formula = Severas ~ MedResp.rec, diseniopost1.sub, family = quasipoisson())
##
## Survey design:
## subset(diseniopost1, diseniopost1$variables$Erosinbord == 1)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0,662 0,136 4,85 0,00013 ***
## MedResp.rec2-Yes -0,286 0,150 -1,90 0,07365 .
## ---
## Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0,6128)
##
## Number of Fisher Scoring iterations: 4