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.
blackbox_transpose(data,missing=NULL,verbose=FALSE,dims=1,minscale)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.
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.
logical, indicates whether aldmck should print out detailed
output when scaling the data.
integer, specifies the number of dimensions to be estimated.
integer, specifies the minimum number of responses a respondent needs needs to provide to be used in the scaling.
An object of class blackbt.
vector of data frames of length dims. Each data frame presents results for estimates from that dimension (i.e. x$stimuli[[2]] presents results for dimension 2). Each row contains data on a separate stimulus, and each data frame includes the following variables:
NNumber of respondents who ranked this stimulus.
coord1DLocation of the stimulus in the first dimension. If viewing
the results for a higher dimension, higher dimension results will appear as
coord2D, coord3D, etc.
R2The percent variance explained for the stimulus. This increases as
more dimensions are estimated.
vector of data frames of length dims. Each data frame presents results for estimates from that dimension (i.e. x$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.
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.
Number of rows/stimuli.
Number of columns used in estimation. This may differ from the data set due to columns discarded due to the minscale constraint.
Total number of data entries.
Number of missing entries.
Sum of squares grand mean.
Number of dimensions estimated.
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'.
# NOT RUN {
### 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)
# }
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