blackbox_transpose {basicspace}R Documentation

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,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. 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.
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 kpoole@ucsd.edu

Howard Rosenthal rosentha@princeton.edu

Jeffrey Lewis jblewis@ucla.edu

James Lo jameslo@ucla.edu

Royce Carroll rcarroll@rice.edu

References

Keith T. Poole (1998) ``Recovering a Basic Space From a Set of Issue Scales.'' American Journal of Political Science. 42(3), 954-993.

See Also

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

Examples


### Loads and scales the Liberal-Conservative scales from the 1980 NES.
data(LC1980)
LCdat=LC1980[,-1]       #Dump the column of self-placements
result <- blackbox_transpose(LCdat,missing=c(0,8,9),dims=3,minscale=5,verbose=TRUE)
plot(result)
par(ask=T)
plotcdf.blackbt(result)
summary(result)
[Package basicspace version 0.01 Index]