I have the following formula:
glm1 <- glm(y ~ g1 + g2, data = fish, family = binomial)
y is a binomial variable: num 1 1 0 0 1 0 0 0 0 1 ... #There is (1) or isn't (0) fish. g1 : Factor w/ 4 levels "1","2","3","4": 1 2 1 1 4 1 2 1 1 3 ... #River's level. g2 : num 808 1110 754 421 568 535 570 690 668 556 ... #Precipitation (mL).
length(y)
>150
length(g1)
>150
length(g2)
>150
To test homoscedasticity, I tried:
bartlett.test(split(y, list(g1,g2))) #Then I get this:
>Error in bartlett.test.default(split(y, list(g1, :
> there must be at least 2 observations in each group. #I think it's strange, because I've 3 vectors at the formula.
But my formula is y ~ g1+g2, then:
bartlett.test(y ~ g1+g2, data=fish) #Then I get this:
>Error in fligner.test.formula(y ~ g1 + :
> 'formula' should be of the form response ~ group
I also tried other tests:
leveneTest(y ~ g1+g2) #Then I get this:
>Error in leveneTest.formula(y ~ g1 + g2, data = fish) :
> Levene's test is not appropriate with quantitative explanatory variables.
Finally, I tried this:
fligner.test(split(y, list(g1,g2)))
>Error in fligner.test.default(split(y, list(g1, :
> all groups must contain data #Again, I think it's strange, because I've 3 vectors at the formula
1. Is it compulsory that both g1 and g2 have to be factors (categorical variable) to test homoscedasticity? 2. How homoscedasticity can be tested in a formula like mine (y-num ~ g1-factor + g2-num) with Bartlett's, Levene's and Fligner's?
I thank you in advance for your help.