MASS packages' "fitdistr": Error when dealing with manipulated random data - r

Question

Background:
Below I have generated some random beta data using R and manipulate the shape of the data a bit to arrive at what I call "Final" in my code. And I histogram "Final" in my code.
Question:
I'm wondering why when trying to fit a "beta" distribution to "Final" data using MASS packages' "fitdistr" function, I get the following error (Any suggestion how to avoid this error)?
Error in stats::optim(x = c(0.461379379270288, 0.0694261016478062, 0.76934266883081, :
initial value in 'vmmin' is not finite
Here is my R code:
require(MASS)
## Generate some data and manipulate it
set.seed(47)
Initial = rbeta(1e5, 2, 3)
d <- density(Initial)
b.5 <- dbeta(seq(0, 1, length.out = length(d$y)), 50, 50)
b.5 <- b.5 / (max(b.5) / max(d$y)) # Scale down to max of original density
b.6 <- dbeta(seq(0, 1, length.out = length(d$y)), 60, 40)
b.6 <- b.6 / (max(b.6) / max(d$y))
# Collect maximum densities at each x to use as sample probability weights
p <- pmax(d$y, b.5, b.6)
Final <- sample(d$x, 1e4, replace = TRUE, prob = p) ## THIS IS MY FINAL DATA
hist(Final, freq = F, ylim = c(0, 2)) ## HERE IS A HISTOGRAM
m <- MASS::fitdistr(Final, "beta", ## RUN THIS TO SEE HOW THE ERROR COMES UP
start = list(shape1 = 1, shape2 = 1))

Answer 1

Here is the code.
It is the same with your code, I just removed the negative beta values.
library(MASS)
set.seed(47)
Initial = rbeta(1e5, 2, 3)
d <- density(Initial)
b.5 <- dbeta(seq(0, 1, length.out = length(d$y)), 50, 50)
b.5 <- b.5 / (max(b.5) / max(d$y)) # Scale down to max of original
density
b.6 <- dbeta(seq(0, 1, length.out = length(d$y)), 60, 40)
b.6 <- b.6 / (max(b.6) / max(d$y))
# Collect maximum densities at each x to use as sample probability weights
p <- pmax(d$y, b.5, b.6)
Final <- sample(d$x, 1e4, replace = TRUE, prob = p) ## THIS IS MY FINAL DATA
hist(Final, freq = F, ylim = c(0, 2)) ## HERE IS A HISTOGRAM
# replace negative beta values with smallest value > 0
Final[Final<= 0] <- min(Final[Final>0])
hist(Final, freq = F, ylim = c(0, 2))
m <- MASS::fitdistr(x = Final, densfun = "beta",
start = list(shape1 = 1, shape2 = 1))
Here are the shape parameters:
> m
shape1 shape2
1.99240852 2.90219720
(0.02649853) (0.04010168)
Take note that it gives some warnings.