basicspace (version 0.25)

boot_blackbt: Bootstrap of Blackbox Transpose Scaling

Description

boot_blackbt is a function automates the non-parametric bootstrapping of blackbox_transpose. The original function takes a matrix of perceptual data, such as liberal-conservative rankings of various stimuli, and recovers the true location of those stimuli in a spatial model. The bootstrap simply applies this estimator across multiple resampled data sets and stores the results of each iteration in a matrix. These results can be used to estimate uncertainty for various parameters of interest, and can be plotted using the plot.boot_blackbt function.

Usage

boot_blackbt(data, missing=NULL, dims=1, dim.extract=dims, minscale, 
    iter=100, verbose=FALSE)

Value

An object of class boot_blackbt. This is simply a matrix of dimensions iter x number of stimuli. Each row stores the estimated stimuli locations for each iteration.

Arguments

data

matrix of numeric values, containing the perceptual data. Respondents should be organized on rows, and stimuli on columns. It is helpful, though not necessary, to include row names and column names.

missing

vector or matrix of numeric values, sets the missing values for the data. NA values are always treated as missing regardless of what is set here. Observations with missing data are discarded before analysis. If input is a vector, then the vector is assumed to contain the missing value codes for all the data. If the input is a matrix, it must be of dimension p x q, where p is the maximum number of missing values and q is the number of columns in the data. Each column of the inputted matrix then specifies the missing data values for the respective variables in data. If null (default), no missing values are in the data other than the standard NA value.

dims

integer, specifies the number of dimensions to be estimated.

dim.extract

integer, specifies which dimension to extract results for the bootstrap from.

minscale

integer, specifies the minimum number of responses a respondent needs needs to provide to be used in the scaling.

iter

integer, number of iterations the bootstrap should run for.

verbose

logical, indicates whether the progress of blackbox_transpose (at each 10th iteration) should be printed to the screen.

Author

Keith Poole

Howard Rosenthal

Jeffrey Lewis

James Lo

Royce Carroll

Christopher Hare

References

David A. Armstrong II, Ryan Bakker, Royce Carroll, Christopher Hare, Keith T. Poole, and Howard Rosenthal. 2021. Analyzing Spatial Models of Choice and Judgment. 2nd ed. Statistics in the Social and Behavioral Sciences Series. Boca Raton, FL: Chapman & Hall/CRC. doi: 10.1201/9781315197609

Keith T. Poole, Jeffrey B. Lewis, Howard Rosenthal, James Lo, and Royce Carroll. 2016. ``Recovering a Basic Space from Issue Scales in R.'' Journal of Statistical Software 69(7): 1-21. doi:10.18637/jss.v069.i07

Keith T. Poole. 1998. ``Recovering a Basic Space From a Set of Issue Scales.'' American Journal of Political Science 42(3): 954-993. doi: 10.2307/2991737

See Also

'blackbox_transpose', 'plot.boot_blackbt'.

Examples

  ### Loads the Liberal-Conservative scales from the 1980 ANES.
  data(LC1980)
  LCdat <- LC1980[,-1]	#Dump the column of self-placements

  # \donttest{ 
    bootbbt <- boot_blackbt(LCdat, missing=c(0,8,9), dims=1, 
      minscale=8, iter=10, verbose=FALSE)
  # }
  ### 'LC1980_bbt' can be retrieved quickly with: 
  data(bootbbt)

  plot.boot_blackbt(bootbbt)