Results of bootstrap reliable? I am using the bootstrap algorithm to compute standard errors of the estimates of my normalmixEM output. I am not really sure if they are reliable?
My code is (data here):
# load package
install.packages("mixtools")
library(mixtools)


B = 1000 # Number of bootstrap samples
mu1sample <- mu2sample <- sigma1sample <- sigma2sample <- lambdasample <- vector()

# Bootstrap
for(i in 1:B){
  print(i)
  subsample = sample(mydatatest,rep=T)
  normalmix <- normalmixEM(subsample, mu=c(-0.002294,0.002866),sigma=c(0.00836,0.02196), lambda=c(0.6746903,(1-0.6746903)),k=2, fast=FALSE, maxit=10000, epsilon = 1e-16, maxrestarts=1000)
  mu1sample[i]    = normalmix$mu[1]      # $
  mu2sample[i]    = normalmix$mu[2]      # $
  sigma1sample[i] = normalmix$sigma[1]   # $
  sigma2sample[i] = normalmix$sigma[2]   # $
  lambdasample[i] = normalmix$lambda[1]  # $
}
# standard errors

sd(mu1sample)
sd(mu2sample)
sd(sigma1sample)
sd(sigma2sample)
sd(lambdasample)

# show distribution of the bootstrap samples
hist(mu1sample)
hist(mu2sample)
hist(sigma1sample)
hist(sigma2sample)
hist(lambdasample)

This gives the following pictures:
mu1

mu2

sigma1

sigma2

lambda

EDIT:
If you look at my variable, the mydatatest and use a KD to show the distribution with the following code
plot(density(mydatatest),col="red",main="",lwd=2,cex.axis=1.2,cex.lab=1.2)

it looks like

2nd EDIT:
I now included the mus and sigmas to be fixed. I updated the code and the pictures. Now again my question, what do you think about it?
 A: As explained by Nick Cox and an anonymous user, what you think of as instability is just what the mixture models do: they don't care about labels unless you make it very clear that you know what your modes look like, roughly. 
In terms of what you can do about fixing the labels where you need them to be, you would want to feed the full sample estimates of everything (both $\mu$s, both $\sigma$s, not just the $\lambda$ that you are feeding in now) as starting values. One can argue that this violates the spirit of maximum likelihood, but that may be the best you can do. If that does not really work, you may have to force even more information in, like insisting that $\mu_1 < \mu_2 - \delta$ and $\sigma_1 < \sigma_2 - \Delta$ and $\lambda > \frac12$. If normalmixEM() does not support that kind of cruelty to the parameter space, you would need to write your own likelihood with your own parameterization that accounts for such relations.
A: My hunch is that your approach might not be reliable due to label switching, that is, each time you fit the mixture model, it's possible that the roles of the two normal distributions has been reversed.
That is, for different runs of the EM algorithm (mu1, sigma1) and (mu2, sigma2) might be switching roles.
It looks like the boot.se function provided by mixtools tries to account for this issue.
