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.
boot_aldmck(data, respondent = 0, missing=NULL, polarity, iter=100)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.
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.
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.
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.
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)
integer, is the number of iterations the bootstrap should run for.
Keith Poole ktpoole@uga.edu
Howard Rosenthal rosentha@princeton.edu
Jeffrey Lewis jblewis@ucla.edu
James Lo lojames@usc.edu
Royce Carroll rcarroll@rice.edu
Christopher Hare cdhare@ucdavis.edu
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. doi: 10.2307/1956957
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
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. doi: 10.2307/2111281
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
'LC1980', 'summary.aldmck', 'plot.aldmck', 'plot.cdf'.
### Loads the Liberal-Conservative scales from the 1980 ANES.
data(LC1980)
result <- boot_aldmck(data=LC1980, polarity=2, respondent=1, missing=c(0,8,9), iter=30)
plot(result)
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