i´m new with R and i have a question to do. i´m doing a study on seed germination and i have trouble doing a priori contrasts for a GLM analysis. My response variable is proportion of seeds germinated and my explanatory variables are treatment with 3 levels (consumida, fruto, S/P) and species with 4 levels (chaqueña,guaviyu,mora,picazu).
My model is this: Modelo2 <- glm (Proporción ~ tratamiento*sp,family = quasibinomial,weights = semillas.por.maceta,data = Datos).
> summary(Modelo2)
Call:
glm(formula = Proporción ~ tratamiento * sp, family = quasibinomial,
data = Datos, weights = semillas.por.maceta)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.1963 -1.2417 -0.1850 0.7471 6.4915
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.9924 0.2921 -6.822 2.36e-11 ***
tratamientoFruto -1.9105 0.3565 -5.359 1.23e-07 ***
tratamientoS/P -0.2501 0.4349 -0.575 0.565516
spGuaviyu 1.5692 0.3541 4.432 1.13e-05 ***
spMora 0.5554 0.3381 1.642 0.101071
spPicazú 2.5678 0.3527 7.280 1.15e-12 ***
tratamientoFruto:spGuaviyu 1.5276 0.4559 3.350 0.000862 ***
tratamientoS/P:spGuaviyu -0.3727 0.5254 -0.709 0.478372
tratamientoFruto:spMora 0.9196 0.4697 1.958 0.050745 .
tratamientoS/P:spMora 1.1204 0.4875 2.298 0.021911 *
tratamientoFruto:spPicazú -17.2014 609.9876 -0.028 0.977513
tratamientoS/P:spPicazú -0.6810 0.5152 -1.322 0.186726
---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for quasibinomial family taken to be 2.252043)
Null deviance: 2671.7 on 566 degrees of freedom
Residual deviance: 1311.0 on 555 degrees of freedom
AIC: NA
Number of Fisher Scoring iterations: 16
My problem is that i dont know how to do a priori constrasts. I want to contrast the 3 treatments (consumida vs fruto, consumida vs S/P, and fruto vs S/P) for each one of the 4 species (i need to do 12 contrasts).
i tried to do a new model with the interaction between species and treatment, and a matrix for planned contrast like is detailed after, but it didn´t work. 3 coefficients were not defined and 1 contrast (the last one) didn´t appear on summary.
Datos$interaccion<- with(Datos, interaction(tratamiento,sp))
modelo3<-glm(Proporción ~ interaccion,family = quasibinomial,weights = semillas.por.maceta,data = Datos)
> summary(modelo3)
Call:
glm(formula = Proporción ~ interaccion, family = quasibinomial,
data = Datos, weights = semillas.por.maceta)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.1963 -1.2417 -0.1850 0.7471 6.4915
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.9924 0.2921 -6.822 2.36e-11 ***
interaccionFruto.Chaqueña -1.9105 0.3565 -5.359 1.23e-07 ***
interaccionS/P.Chaqueña -0.2501 0.4349 -0.575 0.565516
interaccionconsumida.Guaviyu 1.5692 0.3541 4.432 1.13e-05 ***
interaccionFruto.Guaviyu 1.1863 0.3550 3.342 0.000888 ***
interaccionS/P.Guaviyu 0.9465 0.3635 2.604 0.009466 **
interaccionconsumida.Mora 0.5554 0.3381 1.642 0.101071
interaccionFruto.Mora -0.4355 0.3870 -1.125 0.260971
interaccionS/P.Mora 1.4257 0.3237 4.404 1.28e-05 ***
interaccionconsumida.Picazú 2.5678 0.3527 7.280 1.15e-12 ***
interaccionFruto.Picazú -16.5441 609.9876 -0.027 0.978372
interaccionS/P.Picazú 1.6367 0.3500 4.677 3.67e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for quasibinomial family taken to be 2.252043)
Null deviance: 2671.7 on 566 degrees of freedom
Residual deviance: 1311.0 on 555 degrees of freedom
AIC: NA
Number of Fisher Scoring iterations: 16
c1<- c(1,-1,0,0,0,0,0,0,0,0,0,0)
c2<- c(1,0,-1,0,0,0,0,0,0,0,0,0)
c3<- c(0,1,-1,0,0,0,0,0,0,0,0,0)
c4<- c(0,0,0,1,-1,0,0,0,0,0,0,0)
c5<- c(0,0,0,1,0,-1,0,0,0,0,0,0)
c6<- c(0,0,0,0,1,-1,0,0,0,0,0,0)
c7<- c(0,0,0,0,0,0,1,-1,0,0,0,0)
c8<- c(0,0,0,0,0,0,1,0,-1,0,0,0)
c9<- c(0,0,0,0,0,0,0,1,-1,0,0,0)
c10<- c(0,0,0,0,0,0,0,0,0,1,-1,0)
c11<- c(0,0,0,0,0,0,0,0,0,1,0,-1)
c12<- c(0,0,0,0,0,0,0,0,0,0,1,-1)
contrasts(Datos$interaccion)<-cbind(c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12)
modelo3b<-glm(Proporción ~ interaccion,family = quasibinomial,weights = semillas.por.maceta,data = Datos)
summary(modelo3b)
> summary(modelo3b)
Call:
glm(formula = Proporción ~ interaccion, family = quasibinomial,
data = Datos, weights = semillas.por.maceta)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.2284 -1.3449 -0.2286 0.8360 6.9048
Coefficients: (3 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.66256 0.08245 -20.164 < 2e-16 ***
interaccionc1 1.60228 0.15779 10.154 < 2e-16 ***
interaccionc2 -0.74367 0.18759 -3.964 8.31e-05 ***
interaccionc3 NA NA NA NA
interaccionc4 -0.00522 0.24453 -0.021 0.9830
interaccionc5 0.47006 0.26586 1.768 0.0776 .
interaccionc6 NA NA NA NA
interaccionc7 1.09714 0.21891 5.012 7.25e-07 ***
interaccionc8 -1.00737 0.15757 -6.393 3.44e-10 ***
interaccionc9 NA NA NA NA
interaccionc10 3.58896 0.34767 10.323 < 2e-16 ***
interaccionc11 -1.32834 0.23259 -5.711 1.83e-08 ***
---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for quasibinomial family taken to be 2.985274)
Null deviance: 2671.7 on 566 degrees of freedom
Residual deviance: 1563.5 on 558 degrees of freedom
AIC: NA
Number of Fisher Scoring iterations: 5
Can somebody help me please? i ll be thankful
