# #StackBounty: #r #hypothesis-testing #generalized-linear-model #linear-model #meta-analysis Testing complex hypotheses involving glm/li…

### Bounty: 50

I have one independent variable (X) and three dependent variables (W, Y and Z). I am fitting a generalized linear model to each:

\$Y = g(alpha X + epsilon)\$

\$Z = g(beta X + epsilon)\$

\$W = g(gamma X + epsilon)\$

I solve the above in R and get estimates for \$alpha, beta, gamma\$ along with p-values that they are not zero.

However, what I am really interested in is a complex hypothesis such as the following:
\$ H_0 : |alpha – gamma| < |beta – gamma|\$ and \$(alpha – gamma)(beta – gamma) > 0\$

\$H_1 : |alpha – gamma| > |beta – gamma|\$ if \$(alpha – gamma)(beta – gamma) > 0 \$ or \$(alpha – gamma)(beta – gamma) < 0 \$

1. I’d like to get a p-value for the above hypothesis, and if possible
2. an “effect size” for something like the estimate of

\$s = (alpha – beta)\$ if \$H_1\$, 0 otherwise

How would I go about doing this? If this is too complicated to explain, how would I test any non-trivial hypothesis involving glm coefficients? Could we use the estimates and their distributions? Or could we transform it into an equivalent model whose coefficient has the same p-value?

ps. I am actually solving the glms in LIMMA.

Get this bounty!!!

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