MLE error in R: initial value in 'vmmin' is not finite

Question

Suppose I have 2 data.frame objects:

df1 <- data.frame(x = 1:100)
df1$y <- 20 + 0.3 * df1$x + rnorm(100)
df2 <- data.frame(x = 1:200000)
df2$y <- 20 + 0.3 * df2$x + rnorm(200000)

I want to do MLE. With df1 everything is ok:

LL1 <- function(a, b, mu, sigma) {
    R = dnorm(df1$y - a- b * df1$x, mu, sigma) 
    -sum(log(R))
}
library(stats4)
mle1 <- mle(LL1, start = list(a = 20, b = 0.3,  sigma=0.5),
        fixed = list(mu = 0))

> mle1
Call:
mle(minuslogl = LL1, start = list(a = 20, b = 0.3, sigma = 0.5), 
fixed = list(mu = 0))

Coefficients:
      a           b          mu       sigma 
23.89704180  0.07408898  0.00000000  3.91681382 

But if I would do the same task with df2 I would receive an error:

LL2 <- function(a, b, mu, sigma) {
    R = dnorm(df2$y - a- b * df2$x, mu, sigma) 
    -sum(log(R))
}
mle2 <- mle(LL2, start = list(a = 20, b = 0.3,  sigma=0.5),
              fixed = list(mu = 0))
Error in optim(start, f, method = method, hessian = TRUE, ...) : 
  initial value in 'vmmin' is not finite

How can I overcome it?

Answer 1

I had the same problem when minimizin a log-likelihood function. After some debugging I found that the problem was in my starting values. They caused one specific matrix to have a determinant = 0, which caused an error when a log was taken of it. Therefore, it could not find any "finite" value, but that was because the function returned an error to optim.

Bottomline: consider if your function is not returning an error when you run it using the starting values.

PS.: Marius Hofert is completely right. Never suppress warnings.