discrimARTs (version 0.1)

mix.mle: Find maximum likelihood estimate (MLE) of 5 parameters given input vector of observations, initial parameter values, and a method specifying distributions.

Description

Description goes here??

Usage

mix.mle(input, method = "normal", mix.prob = NULL, 
    dist1.par1 = NULL, dist1.par2 = NULL, 
    dist2.par1 = NULL, dist2.par2 = NULL, 
    lower = NULL, upper = NULL, distlist = NULL,
    optim.lower = rep(0, 5), optim.upper = c(1, rep(NA, 4)), ...)

Arguments

input

Vector of (numeric) trait observations. Observations should be strictly positive.

method

Either "normal" or "facing.gamma". Describe??

Initial parameter estimation is implemented for "normal", but is only appropriate when mix.prob is close to 0.5.

mix.prob

The initial probability of mixture distributions, specifying the proportion of the upper distribution.

dist1.par1

First parameter of the lower distribution. This specifies the mean for method="normal", and the shape for method="facing.gamma".

dist1.par2

Second parameter of the lower distribution. This specifies the standard deviation for method="normal", and the scale for method="facing.gamma".

dist2.par1

First parameter of the upper distribution, specifying parameters as in dist1.par1.

dist2.par2

Second parameter of the upper distribution, specifying parameters as in dist1.par2.

distlist

Optionally, the user can specify their own list containing a lower and upper distribution (PDF) function. Each density function must take observations and 2 positional parameters, as per dnorm. Distributions taking >2 parameters may be used

lower

For method="facing.gamma", the lower bound?? Otherwise ignored.

upper

For method="facing.gamma", the upper bound?? Otherwise ignored.

optim.lower, optim.upper

Numeric vector. Lower and upper bounds used by optim, specifying: c(mix.prob, dist1.par1, dist1.par2, dist2.par1, dist2.par2). By default, mix.prob is bounded between 0 and 1, and distribution parameters are

...

Additional arguments are passed to optim. In particular, control=list(trace=5, REPORT=1) displays information about the optimization process.

Value

• An object of class discrimARTs, which is a list containing the original input data and parameter values, as well as output from optim.
• neglogLik
  Numeric. Negative log likelihood of final optimized parameters.
• MLE.est
  Named vector containing the final optimized parameter values.

Details

Details??

References

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See Also

mix.loglik, optim, x_gideon, o_taurus, mix.synthetic

Examples

data(x_gideon)

## Assuming normal, estimate initial conditions from data
fit.default.gideon <- mix.mle(x_gideon$horn)

## Estimation of mixture of normals, explicitly specifying method and parameters
fit.gideon <- mix.mle(x_gideon$horn, method='normal', 
    mix.prob=0.5, dist1.par1=100, dist1.par2=10, dist2.par1=300, dist2.par2=10)

## Default printing and plotting methods
print(fit.gideon)

## Compare results
layout(1:2)
plot(fit.gideon)
plot(fit.default.gideon)