fitmix: Mixture Model Distance Detection Function Fitting
In dill/mmds: Mixture Model Distance Sampling (mmds)

Description Usage Arguments Value Details Author(s) References See Also Examples

Fits a mixture of half-normals as a detection function to distance sampling data collected via either line or point transects, possibly with covariates.

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fitmix(data, width, mix.terms = 1, pt = FALSE, model.formula = "~1",
  initialvalues = NULL, showit = 0, ctrl.options = c(maxit = 10000),
  opt.method = "BFGS+SANN", usegrad = TRUE, ftype = "hn")

`data`	`data.frame` containing the distances and covariates to be used in the analusis (see Details).
`width`	Truncation distance.
`mix.terms`	Number of mixture components to use. Defaults to 1 (ie. CDS).
`pt`	Is the data from point transects? Default FALSE.
`model.formula`	Formula to be used for the covariates. Defaults to "~1" (ie. no covariates).
`initialvalues`	User supplied initialvalues if needed. Defaults to `NULL`. See `mmds.pars` for more information.
`showit`	Debugging level from 0 to 3, with 3 being most verbose. Defaults to 0.
`ctrl.options`	Options to give to the `optim`. Defaults to `c(maxit=10000)`.
`opt.method`	Optimisation method to use, one of "BFGS", "BFGS+SANN" or "EM". Defaults to "BFGS+SANN", see Details.
`usegrad`	Should analytic derivatives be used in the optimisation? Default TRUE.
`ftype`	Function type to be used as the detection function, currently only "hn".

a ds.mixture model object.

This is the main routine that fits mixture model detection functions.

data should be a data.frame with (at least) a column named distance. Any covariates given in model.formula should be named in data. Note that rows with distance greater than width will be discarded.

See step.ds.mixture for AIC selection for the number of mixture components.

David L. Miller

Miller, D.L. and L. Thomas (in prep.). Mixture model distance sampling detection functions.

summary.ds.mixture step.ds.mixture plot.ds.mixture sim.mix mmds.gof ds.mixture mmds.gof fitmix mmds.pars step.ds.mixture

library(mmds)
set.seed(0)
## simulate some line transect data from a 2 point mixture
sim.dat<-sim.mix(c(-0.223,-1.897,inv.reparam.pi(0.3)),2,100,1)
## fit the model
fit.sim.dat<-fitmix(sim.dat,1,2)
## what happened?
summary(fit.sim.dat)