- #1
solar42
- 1
- 0
I have measurements of some response of a gene, and two factors: the gene, g=1...G and whether the patient/subject has a certain disease, t=1,2.
the full model is
<br /> y_{gtk}=\mu+\alpha_g+\beta_t +(\alpha\beta)_{gt}+\epsilon_{gtk}<br /> <br />
I know that to see if genes have any connection at all with the disease, I just fit the reduced model without the (\alpha\beta)_{gt} interaction and compare the two, but if I want to see if, say, gene number g=25 has anything to do with the disease... I know that the null hypothesis is H_0 : (\alpha\beta)_{25,t}=0, \textrm{ for all } t, but how do I test this hypothesis? I am confused at what to do when I can't drop the whole factor and compare.
the full model is
<br /> y_{gtk}=\mu+\alpha_g+\beta_t +(\alpha\beta)_{gt}+\epsilon_{gtk}<br /> <br />
I know that to see if genes have any connection at all with the disease, I just fit the reduced model without the (\alpha\beta)_{gt} interaction and compare the two, but if I want to see if, say, gene number g=25 has anything to do with the disease... I know that the null hypothesis is H_0 : (\alpha\beta)_{25,t}=0, \textrm{ for all } t, but how do I test this hypothesis? I am confused at what to do when I can't drop the whole factor and compare.
