[R-meta] R^2 in nonlinear model

[Cesar Terrer Moreno]

Dear all,

I am using a nonlinear meta-regression of the form y ~ p1 * exp(-p2*A):

nlfun <- function(x, p1, p2)
  p1 * exp(-p2*x)

# optimization function
llfun <- function(par, yi, vi, x, random=TRUE) {
  p1 <- par[1]
  p2 <- par[2]
  if (random) {
    tau2 <- exp(par[3])
  } else {
    tau2 <- 0
  }
  mu <- nlfun(x, p1, p2)
  -sum(dnorm(yi, mean=mu, sd=sqrt(vi + tau2), log=TRUE))
}

# optimize
res <- optim(par=c(8,0.4,log(.01)), llfun, yi=am.df$es, vi=am.df$var, x=am.df$CNr, hessian=TRUE) 

My question is: how can I compute something equivalent to R^2 that I can report to have an idea of the goodness of the fit?
Thanks


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