blackbox_transpose: Blackbox transpose Scaling

In basicspace: Recovering a Basic Space from Issue Scales

Blackbox transpose Scaling

Description

blackbox_transpose is a function that 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. It differs from procedures such as wnominate, which instead use preference data to estimate candidate and citizen positions. The procedure here generalizes the technique developed by John Aldrich and Richard McKelvey in 1977, which is also included in this package as the aldmck function.

Usage

  blackbox_transpose(data, missing=NULL, verbose=FALSE, dims=1, minscale)

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.
verbose	logical, indicates whether aldmck should print out detailed output when scaling the data.
dims	integer, specifies the number of dimensions to be estimated.
minscale	integer, specifies the minimum number of responses a respondent needs needs to provide to be used in the scaling.

Value

An object of class blackbt.

stimuli	vector of data frames of length dims. Each data frame presents results for estimates from that dimension (i.e. x$stimuli[[2]] presents results for dimension 2). Each row contains data on a separate stimulus, and each data frame includes the following variables: N Number of respondents who ranked this stimulus. coord1D Location of the stimulus in the first dimension. If viewing the results for a higher dimension, higher dimension results will appear as coord2D, coord3D, etc. R2 The percent variance explained for the stimulus. This increases as more dimensions are estimated.
individuals	vector of data frames of length dims. Each data frame presents results for estimates from that dimension (i.e. x$stimuli[[2]] presents results for dimension 2). Individuals that are discarded from analysis due to the minscale constraint are NA'd out. Each row contains data on a separate stimulus, and each data frame includes the following variables: c Estimate of the individual intercept. w1 Estimate of the individual slope. If viewing the results for a higher dimension, higher dimension results will appear as w2, w3, etc. R2 The percent variance explained for the respondent. This increases as more dimensions are estimated.
fits	A data frame of fit results, with elements listed as follows:

SSE

Sum of squared errors.

SSE.explained

Explained sum of squared error.

percent

Percentage of total variance explained.

SE

Standard error of the estimate, with formula provided in the article cited below.

singular

Singluar value for the dimension.

Nrow	Number of rows/stimuli.
Ncol	Number of columns used in estimation. This may differ from the data set due to columns discarded due to the minscale constraint.
Ndata	Total number of data entries.
Nmiss	Number of missing entries.
SS_mean	Sum of squares grand mean.
dims	Number of dimensions estimated.

Author(s)

Keith Poole [e-mail]

Howard Rosenthal [e-mail]

Jeffrey Lewis [e-mail]

James Lo [e-mail]

Royce Carroll [e-mail]

Christopher Hare [e-mail]

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

'plotcdf.blackbt', 'LC1980', 'plot.blackbt', 'summary.blackbt', 'LC1980_bbt'.

Examples

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

   
  LC1980_bbt <- blackbox_transpose(LCdat, missing=c(0,8,9), dims=3, 
    minscale=5, verbose=FALSE)
  
  ### 'LC1980_bbt' can be retrieved quickly with: 
  data(LC1980_bbt)

  summary(LC1980_bbt)
  plot(LC1980_bbt)

basicspace documentation built on April 4, 2025, 1:26 a.m.