bootCI {fitPS}

Bootstrap confidence intervals or regions

Description

Use boostrapping to generate confidence intervals, or confidence regions in the case of the zero-inflated model.

Usage

bootCI(x, ...)

## Default S3 method:
bootCI(
  x,
  level = 0.95,
  B = 2000,
  model = c("zeta", "ziz"),
  returnBootValues = FALSE,
  silent = FALSE,
  plot = FALSE,
  parallel = TRUE,
  progressBar = FALSE,
  pbopts = list(type = "txt"),
  ...
)

## S3 method for class 'psData'
bootCI(x, ...)

## S3 method for class 'psFit'
bootCI(x, ...)

Arguments

x

a object either of class psData—see readData for more details—or of class psFit.

...

other arguments.

level

the confidence level required—restricted to [0.75, 1). This may be a vector, in which case multiple intervals, or confidence regions will be returned.

B

the number of bootstrap samples to take.

model

which model to fit to the data, either "zeta" or "ziz". Maybe abbreviated to "z" and "zi". Default is "zeta".

returnBootValues

if TRUE then the vector (or data.frame) of bootstrapped values is returned. This can be useful for debugging or understanding the results. Default is FALSE.

silent

if TRUE, then no output will be displayed whilst the bootstrapping is being undertaken. plot if TRUE then the contours for the confidence region will be plotted. This only works if model = "ziz". It is ignored otherwise. parallel if TRUE then the bootstrapping is performed in parallel.

plot

if TRUE and model == "ziz", then a plot of the bootstrapped values will be produced and confidence contour lines will be drawn for each value in level.

parallel

if TRUE, then the package will attempt to use multiple cores to speed up computation.

progressBar

if TRUE, then progress bars will be displayed to show progress on the bootstrapping.

pbopts

a list of arguments for the pboptions function that affect the progress bars. Ignored if progressBar = FALSE.

Details

This function uses bootstrapping to compute a confidence interval for the shape parameter in the case of the zeta model and a confidence region in the case of the zero-inflated zeta model. A smoothed bootstrap approach is taken rather than a simple percentile method. The kernel density estimation is performed by the ks package using a smoothed cross-validated bandwidth selection procedure.

Value

If returnBootVals == TRUE then the results are returned in a list with elements named ci and bootVals for the zeta model and confRegion and bootVals for the zero-inflated zeta model. The structure of ci and confregion is described below. If model == "zeta", then either a vector or a data.frame with elements/columns named "lower" and "upper" representing the lower and upper bounds of the confidence interval(s). Multiple bounds are returned in a data.frame when level has more than one value. If model == "ziz ", then a list with length equal to the length of level is returned. The name of each element in the list is the level with list has a single element named "95%". It is possible for there to be multiple contours for the confidence region for a given level. If there is only one contour for each value of level, then each element of the list consists of a list with elements named pi and shape which specify the coordinates of the contour(s) for that level. There is a third element named level which gives the height of the kernel density estimate at that contour. If there are multiple contours for a given value of level then each list element is a list of lists with the structure given above (level, pi, and shape). NOTE: it is quite possible that there are multiple contours for a given height. If you want a way of thinking about this consider a mountain range with two mountains of equal height. If you draw the contours for (almost) any elevation, then you would expect to capture a region from each mountain.

Methods (by class)

• bootCI(default): Bootstrap confidence intervals or regions

• bootCI(psData): Bootstrap confidence intervals or regions

• bootCI(psFit): Bootstrap confidence intervals or regions

Examples

## Not run: 
data(Psurveys)
roux = Psurveys$roux
confRegion = bootCI(roux, model = "ziz", parallel = FALSE, plot = TRUE)

## This will not work unless you have the sp package installed
## Count how many of the points lie within the 95% confidence region
lapply(confRegion, function(cr){
  table(sp::point.in.polygon(fit$pi,fit$shape, cr$pi, cr$shape))
. })

## End(Not run)