I am running a latent class analysis in R and using the Entropy function. I wanted to understand why in the output, it produces a result for lower nclasses and then NaN for higher Nclasses.
I am a beginner to the software!
For reference here, is the output and code:
> entropy<-function (p) sum(-p*log(p))
> error_prior <- entropy(France_2class$P) # Class proportions
> error_post <- mean(apply(France_2class$posterior, 1, entropy))
> R2_entropy <- (error_prior - error_post) / error_prior
> R2_entropy
[1] 0.8121263
>
> entropy<-function (p) sum(-p*log(p))
> error_prior <- entropy(France_3class$P) # Class proportions
> error_post <- mean(apply(France_3class$posterior, 1, entropy))
> R2_entropy <- (error_prior - error_post) / error_prior
> R2_entropy
[1] 0.8139903
>
> entropy<-function (p) sum(-p*log(p))
> error_prior <- entropy(France_4class$P) # Class proportions
> error_post <- mean(apply(France_4class$posterior, 1, entropy))
> R2_entropy <- (error_prior - error_post) / error_prior
> R2_entropy
[1] NaN
>
> entropy<-function (p) sum(-p*log(p))
> error_prior <- entropy(France_5class$P) # Class proportions
> error_post <- mean(apply(France_5class$posterior, 1, entropy))
> R2_entropy <- (error_prior - error_post) / error_prior
> R2_entropy
[1] NaN
>
> entropy<-function (p) sum(-p*log(p))
> error_prior <- entropy(France_6class$P) # Class proportions
> error_post <- mean(apply(France_6class$posterior, 1, entropy))
> R2_entropy <- (error_prior - error_post) / error_prior
> R2_entropy
[1] NaN
Can anyone help? Thank you
I guess the problem comes from the definition of entropy. More precisely, if 0 is contained in p, then you will obtain NaN, e.g.,
> entropy(p1)
[1] 1.279854
> entropy(p2)
[1] NaN
> entropy(p3)
[1] 0.5004024
To fix it, you can add na.omit to function entropy like below
entropy<-function(p) sum(na.omit(-p*log(p)))
then you can see
> entropy(p1)
[1] 1.279854
> entropy(p2)
[1] 0.5004024
> entropy(p3)
[1] 0.5004024
DATA
p1 <- c(0.1,0.2,0.3,0.4)
p2 <- c(0,0.2,0.8)
p3 <- c(0.2,0.8)