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I estimate probit model: \begin{align*} P(y=1|x_1, x_2, x_3) = \Phi(\alpha_0 + \alpha_1 x_1 + \alpha_2 x_2+ \alpha_3 x_3) \end{align*} using: probitMain <- glm(y~x1+x2+x3, family=binomial(link="probit"), data=MD).
I want to test nonlinear hypothesis $f(\alpha_0, \alpha_1, \alpha_2, \alpha_3) = 0$, where $f$ is a rational function $f(\alpha_0,\alpha_1, \alpha_2, \alpha_3) = \frac{\alpha_2 + \alpha_0 \frac{\alpha_3}{\alpha_1}}{1+\frac{\alpha_3}{\alpha_1}}$. I have tried to use nlWaldTest package for nonlinear Wald test, but it doesn't seem to work with glm objects.

Is there any other R package that can test nonlinear hypothesis for glm?

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  • $\begingroup$ What's the rational function? Some functions at least will allow manipulations that simplify the problem. $\endgroup$
    – Glen_b
    Feb 3, 2016 at 1:39
  • $\begingroup$ Thanks. I have included the explicit definition of the function in the question. $\endgroup$
    – Nidjsi
    Feb 4, 2016 at 6:50
  • $\begingroup$ If $a_1\neq 0$ then you can multiply numerator and denominator by $a_1$ and get: $f = \frac{a_2a_1 + a_0a_3}{a_1 + a_3}$. Now if $(a_1+a_3)\neq 0$ then $f=0$ if $a_2a_1 + a_0a_3 = 0$. That's still nonlinear, but it is at least a little simpler. $\endgroup$
    – Glen_b
    Feb 4, 2016 at 8:19
  • $\begingroup$ Thanks. In the end I have manipulated the likelihood function for probit so it can be written in the suitable form and estimated parameters using max likelihood and retrieved the appropriate standard errors. $\endgroup$
    – Nidjsi
    Feb 10, 2016 at 13:48
  • $\begingroup$ Do you think what you did might work as a kind of answer for your question? I'm thinking mainly that if there are other people with a similar problem describing what you ended up doing to solve the problem might be useful. $\endgroup$
    – Glen_b
    Feb 11, 2016 at 0:20

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I solve the problem like this. Write the probit equation as: \begin{align} P(y=1|x_1, x_2, x_3)=\Phi \left(\gamma_1 + \gamma_2 x_1 + (\gamma_3 + \gamma_3 \gamma_4 -\gamma_1)x_2 + \gamma_2 \gamma_4 x_3 \right) \end{align} I estimate this equation using ML with respect to $\gamma's$ (with function mle2 from bbmle package). Note that $\gamma_2$ is exactly the parameter I was originally interested in. From Hessian of the ML estimation I can retrieve standard errors.

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