mle error: non-finite finite-difference value [1] - r

Question

I'm trying to fit neutral model parameter using MLE in R.
I cannot get this error, and how can I solve it?
I've got the following error:
Error in optim(start, f, method = method, hessian = TRUE, ...) :
non-finite finite-difference value [1]
These are the codes that I used.
#Load packages
library(minpack.lm)
library(Hmisc)
library(stats4)
#Calculate the number of individuals per community
N <- mean(apply(otu_f_h, 1, sum))
#Calculate the average relative abundance of each taxa across communities
p.m <- apply(otu_f_h, 2, mean)
p.m <- p.m[p.m != 0]
p <- p.m/N
#Calculate the occurrence frequency of each taxa across communities
spp.bi <- 1*(otu_f_h>0)
freq <- apply(spp.bi, 2, mean)
freq <- freq[freq != 0]
#Combine
C <- merge(p, freq, by=0)
C <- C[order(C[,2]),]
C <- as.data.frame(C)
C.0 <- C[!(apply(C, 1, function(y) any(y == 0))),] #Removes rows with any zero (absent in either source pool or local communities)
p <- C.0[,2]
freq <- C.0[,3]
names(p) <- C.0[,1]
names(freq) <- C.0[,1]
#Calculate the limit of detection
d = 1/N
##Fit model parameter m (or Nm) using Non-linear least squares (NLS)
m.fit <- nlsLM(freq ~ pbeta(d, N*m*p, N*m*(1-p), lower.tail=FALSE), start=list(m=0.1))
m.ci <- confint(m.fit, 'm', level=0.95)
##Fit neutral model parameter m (or Nm) using Maximum likelihood estimation (MLE)
sncm.LL <- function(m, sigma){
R = freq - pbeta(d, N*m*p, N*m*(1-p), lower.tail=FALSE)
R = dnorm(R, 0, sigma)
-sum(log(R))
}
m.mle <- mle(sncm.LL, start=list(m=0.1, sigma=0.1), nobs=length(p))
This is the sample table that I used (otu_f_h)
SampleID
OTU01
OTU02
OTU03
OTU04
OTU05
OTU06
OTU07
OTU08
R01
8
0
8
0
0
0
0
257
R02
0
0
0
0
0
0
0
0
R05
0
0
0
0
51
0
0
0
R06
0
0
0
0
9
0
0
0
R09
0
0
0
0
0
0
0
0
R10
0
0
0
0
0
0
6
0
R39
0
0
0
0
0
0
0
28
R40
0
0
0
0
0
0
0
0
R51
0
0
0
0
0
0
0
0
S01
24
0
4
5
0
0
6
0
S02
0
0
37
0
0
0
0
0
S05
0
0
15
28
0
0
0
0
S06
0
2
0
0
7
0
0
0
S09
0
0
0
0
24
0
0
0
S10
0
0
0
0
0
0
0
0
S39
0
0
0
0
0
0
0
0
S40
0
0
0
0
0
0
0
0
S51
0
0
0
0
0
0
0
0
S52
0
0
46
79
0
0
35
0
structure(list(SampleID = c("R01", "R02", "R05", "R06", "R09",
"R10", "R39", "R40", "R51", "S01", "S02", "S05", "S06", "S09",
"S10", "S39", "S40", "S51", "S52"), OTU01 = c(8, 0, 0, 0, 0,
0, 0, 0, 0, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0), OTU02 = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0), OTU03 = c(8,
0, 0, 0, 0, 0, 0, 0, 0, 4, 37, 15, 0, 0, 0, 0, 0, 0, 46), OTU04 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 28, 0, 0, 0, 0, 0, 0, 79), OTU05 = c(0,
0, 51, 9, 0, 0, 0, 0, 0, 0, 0, 0, 7, 24, 0, 0, 0, 0, 0), OTU06 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), OTU07 = c(0,
0, 0, 0, 0, 6, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 35), OTU08 = c(257,
0, 0, 0, 0, 0, 28, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = 2:20, class = "data.frame")