blackbox_transpose: Blackbox transpose Scaling
In basicspace: Recovering a Basic Space from Issue Scales

Description Usage Arguments Value Author(s) References See Also Examples

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.

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

`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.

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:

NNumber of respondents who ranked this stimulus.
coord1DLocation of the stimulus in the first dimension. If viewing the results for a higher dimension, higher dimension results will appear as coord2D, coord3D, etc.
R2The 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:

cEstimate of the individual intercept.
w1Estimate of the individual slope. If viewing the results for a higher dimension, higher dimension results will appear as w2, w3, etc.
R2The 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:

SSESum of squared errors.
SSE.explainedExplained sum of squared error.
percentPercentage of total variance explained.
SEStandard error of the estimate, with formula provided in the article cited below.
singularSingluar 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.

Keith Poole ktpoole@uga.edu

Howard Rosenthal hr31@nyu.edu

Jeffrey Lewis jblewis@ucla.edu

James Lo lojames@usc.edu

Royce Carroll rcarroll@rice.edu

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

Keith T. Poole (1998) “Recovering a Basic Space From a Set of Issue Scales.” American Journal of Political Science. 42(3), 954-993.

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

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


### This command conducts estimates, which we instead load using data()

#LC1980_bbt <- blackbox_transpose(LCdat,missing=c(0,8,9),dims=3,minscale=5,verbose=TRUE)
data(LC1980_bbt)
plot(LC1980_bbt)

par(ask=TRUE)
plotcdf.blackbt(LC1980_bbt)
summary(LC1980_bbt)

Loading required package: tools

## BASIC SPACE SCALING PACKAGE
## 2009 - 2021
## Keith Poole, Howard Rosenthal, Jeffrey Lewis, James Lo, and Royce Carroll
## Support provided by the U.S. National Science Foundation
## NSF Grant SES-0611974



SUMMARY OF BLACKBOX TRANSPOSE OBJECT
----------------------------------
              N coord1D    R2
Carter      768   0.241 0.563
Reagan      765  -0.582 0.822
Kennedy     754   0.476 0.648
Anderson    689   0.061 0.230
Republicans 771  -0.519 0.757
Democrats   774   0.321 0.651

              N coord1D coord2D    R2
Carter      768   0.238  -0.407 0.720
Reagan      765  -0.580  -0.101 0.839
Kennedy     754   0.481   0.013 0.680
Anderson    689   0.059   0.864 0.946
Republicans 771  -0.518  -0.117 0.767
Democrats   774   0.321  -0.252 0.718

              N coord1D coord2D coord3D    R2
Carter      768   0.191  -0.261  -0.663 0.918
Reagan      765   0.216   0.556   0.141 0.856
Kennedy     754   0.162  -0.510   0.697 0.981
Anderson    689  -0.911   0.053  -0.002 1.000
Republicans 771   0.210   0.498   0.055 0.780
Democrats   774   0.131  -0.335  -0.228 0.765

	Dimensions Estimated: 3
	Number of Rows: 6
	Number of Columns: 775
	Total Number of Data Entries: 4521
	Number of Missing Entries: 129
	Percent Missing Data: 2.77%
	Sum of Squares (Grand Mean): 12683.93