boot_aldmck: Bootstrap of Aldrich-McKelvey Scaling

In basicspace: Recovering a Basic Space from Issue Scales

Description

boot_aldmck is a function automates the non-parametric bootstrapping of aldmck. 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_aldmck function.

Usage

  boot_aldmck(data, respondent = 0, missing=NULL, polarity, iter=100)

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.
respondent	integer, specifies the column in the data matrix of the stimuli that contains the respondent's self-placement on the scale. Setting respondent = 0 specifies that the self-placement data is not available. Self-placement data is not required to estimate the locations of the stimuli, but is required if recovery of the respondent ideal points, or distortion parameters is desired. Note that no distortion parameters are estimated in AM without self-placements because they are not needed, see equation (24) in Aldrich and McKelvey (1977) for proof.
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.
polarity	integer, specifies the column in the data matrix of the stimuli that is to be set on the left side (generally this means a liberal)
iter	integer, is the number of iterations the bootstrap should run for.

Value

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

Author(s)

Keith Poole [e-mail]

Howard Rosenthal [e-mail]

Jeffrey Lewis [e-mail]

James Lo [e-mail]

Royce Carroll [e-mail]

References

Keith Poole, Jeffrey Lewis, Howard Rosenthal, James Lo, 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

John H. Aldrich and Richard D. McKelvey (1977) “A Method of Scaling with Applications to the 1968 and 1972 Presidential Elections.” American Political Science Review. 71(1), 111-130.

Thomas R. Palfrey and Keith T. Poole (1987) “The Relationship between Information, Ideology, and Voting Behavior.” American Journal of Political Science. 31(3), 511-530.

Keith Poole. http://voteview.com

See Also

'LC1980', 'summary.aldmck', 'plot.aldmck', 'plot.cdf'.

Examples

data(LC1980)

result <- boot_aldmck(data=LC1980, polarity=2, respondent=1, missing=c(0,8,9), iter=30)
plot(result)

basicspace documentation built on Jan. 11, 2020, 9:32 a.m.