Description Usage Arguments Details Value Note References See Also Examples
Algorithm computes maximum likelihood estimates of the parameters of either the normal mixtuture distribution or the facing gamma distribution model for a dimorphic trait.
1 2 3 4 5 |
input |
Vector of (numeric) trait observations. Observations should be strictly positive. |
method |
Either Initial parameter estimation is implemented for |
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 |
dist1.par2 |
Second parameter of the lower distribution. This specifies the standard deviation for |
dist2.par1 |
First parameter of the upper distribution, specifying parameters as in |
dist2.par2 |
Second parameter of the upper distribution, specifying parameters as in |
distlist |
Optionally, the user can specify their own list containing a lower and upper distribution (PDF) function. |
lower |
For |
upper |
For |
optim.lower, optim.upper |
Numeric vector. lower and upper bounds used by |
... |
Additional arguments are passed to optim. In particular, |
Computes maximum likelihood estimates of the parameters of either the normal mixture distribution or the facing gamma distribution model for a dimorphic trait.
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 2 x log likelihood of final optimized parameters. |
MLE.est |
Named vector containing the final optimized parameter values. |
In general, method="normal"
is converges well for a range of initial values.
method="facing.gamma"
requires more care.
Rowland JM, Qualls CR. 2005. Likelihood models for discriminating alternative phenotypes in morphologically dimorphic species.
Evolutionary Ecology Research 7: 421-434.
mix.loglik
,
optim
,
x_gideon
,
o_taurus
,
mix.synthetic
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | 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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.