MCgof.methods: Methods for MCgof Objects

In secr: Spatially Explicit Capture-Recapture

Methods for MCgof Objects

Description

Plot, summary and print methods for MCgof objects.

Usage

## S3 method for class 'MCgof'
plot(x, counts = 'all', overlay = NULL, maxT = NULL, 
    main = NULL, cex = 0.9, ...)

## S3 method for class 'MCgof'
hist(x, counts = 'all', maxT = NULL, main = NULL, 
    cex = 0.9, ...)

## S3 method for class 'MCgof'
summary(object, ...) 

## S3 method for class 'MCgof'
print(x, ...) 

Arguments

x	MCgof object
counts	character vector of marginal counts for which statistics are to be plotted
overlay	MCgof object
maxT	numeric maximum plotted value of statistic
main	character vector of labels (see Details)
cex	numeric size of labels and points
...	other arguments passed by the plot method to points for plotting overlay
object	MCgof object

Details

We start with a 3-D capthist array with dimensions corresponding to individuals (i), occasions (j) and detectors (k). The possible marginal counts for the default ‘statfn’ in MCgof are designated –

Count	Margin	Cell value
yik	individual x detector	y_{ik} = \sum_j y_{ijk}
yi	individual	y_i = \sum_j \sum_k y_{ijk}
yk	detector	y_k = \sum_j \sum_i y_{ijk}

The plot method displays a scatterplot of discrepancies for observed and simulated data (one point per replicate) (Gelman et al. 1996).

If ‘overlay’ is provided then the results are overlaid on the initial plot. Points should be distinguished by specifying a different colour (col) or symbol (pch) with the ... argument.

‘main’ is a vector of labels used as headers; the names should include all components of ‘statfn’. Setting main = "" suppresses headers.

The hist method displays a histogram of the ratio Tobs/Tsim.

Value

The summary method returns a matrix of values in which the columns correspond to the different statistics (default yik, yi, yk) and the rows are

• median discrepancy Tobs

• median discrepancy Tsim

• proportion Tobs>Tsim

• number of valid results

References

Gelman, A., Meng, X.-L., and Stern, H. (1996) Posterior predictive assessment of model fitness via realized discrepancies. Statistica Sinica 6, 733–807.

See Also

MCgof

secr documentation built on Nov. 4, 2024, 9:06 a.m.