monte: Generate Objects of Class "'monte'"

In sampSurf: Sampling Surface Simulation for Areal Sampling Methods

Description

This is the generic definition for generating objects of class "monte." There are currently several methods corresponding to this generic whose documentation may be found in monte-methods.

Usage

monte(object, ...)

Arguments

object	Signature argument, which differs for each method. This specifies the population from which samples will be drawn.
...	See methods.

Details

The methods associated with this generic should be used to construct objects of class "monte." These objects are specifically designed to hold information about Monte Carlo experiments where one resamples from a known population to infer efficiency and perhaps locate any bias in different sampling estimators. The constructor methods can be used to look at traditional normal theory and bootstrap confidence intervals in terms of nominal catch rates for the population mean.

Value

A valid object of class "monte."

Author(s)

Jeffrey H. Gove

References

The ‘“monte”: When is n Sufficiently Large?’ vignette.

See Also

monte, monteSample

Examples

#
# these examples are commented-out because they consume 
# cpu time when checking the package--just copy and paste
# them if you want to try them out...
#
# from a sampSurf object...
#
## Not run: 
smTract = Tract(c(x=30,y=30), cellSize=0.5)
smbuffTr = bufferedTract(8,smTract)
ss.sa = sampSurf(10, smbuffTr, 'sausageIZ', plotRadius=3, estimate='Length')
m.sa = monte(ss.sa, n=c(10,20))
hist(m.sa)

## End(Not run)

#
# simple population...
#
## Not run: 
mp = montePop(rnorm(100), n=c(10,30))
mt = monte(mp, mcSamples=250, R=150)     #takes n from mp object
mt

## End(Not run)

Example output

Loading required package: sp
Loading required package: raster
Loading required package: rasterVis
Loading required package: lattice
Loading required package: latticeExtra
Loading required package: RColorBrewer
Loading required package: boot

Attaching package: 'boot'

The following object is masked from 'package:lattice':

    melanoma

sampSurf version 0.7-3 (2015-04-14)

Number of logs in collection = 10
Heaping log: 1,2,3,4,5,6,7,8,9,10,
Estimate attribute = NA

Population...
  Mean = 0.07615892
  Variance = 0.9692357
  Standard Deviation = 0.9844977
  Total = 7.615892
  Size (N) = 100
  Zero-truncated = FALSE
  Sample sizes (n) = 10, 30
  Finite population corrections = 0.9, 0.7
  Variance of the mean = 0.08723121, 0.0226155
  Standard error of the mean = 0.2953493, 0.1503845

Normal theory results...
Number of Monte Carlo samples = 250
Sample sizes: n = 10, 30
Sample summary statistics (mean values)...
               n.10        n.30
mean     0.09925229  0.07770412
var      0.92624171  0.95105161
stDev    0.94074848  0.96854912
VarMean  0.08336175  0.02219120
stErr    0.28222454  0.14794832
lowerCI -0.53918399 -0.22488417
upperCI  0.73768856  0.38029241

Percentage of confidence intervals (95%) that caught the population mean...
n.10 n.30 
92.4 91.6 

Bootstrap results...
Number of bootstrap samples = 150
Number of Monte Carlo samples = 250
Sample sizes: n = 10, 30
Sample summary statistics (mean values)...
               n.10        n.30
mean     0.07042534  0.08920588
var      0.98961541  0.97497794
stDev    0.97362650  0.98179008
varMean  0.08906539  0.02274949
stErr    0.29208795  0.14997091
lowerCI -0.52228790 -0.26590135
upperCI  0.67317306  0.44468066

Percentage of confidence intervals (95%) that caught the population mean...
n.10 n.30 
90.0 95.6 

sampSurf documentation built on March 5, 2021, 3:01 p.m.