basicspace (version 0.25)

blackbox: Blackbox Scaling

Description

blackbox is a function that takes a matrix of survey data in which individuals place themselves on continuous scales across multiple issues, and locates those citizens in a spatial model of voting. Mathematically, this function generalizes the singular value of a matrix to cases in which there is missing data in the matrix. Scales generated using perceptual data (i.e. scales of legislator locations using liberal-conservative rankings by survey respondents) should instead use the blackbox_transpose function in this package instead.

Usage

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

Value

An object of class blackbox.

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 provided a response to this stimulus.

c

Stimulus intercept.

w1

Estimate of the stimulus weight on the first dimension. If viewing the results for a higher dimension, higher dimension results will appear as w2, w3, 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:

c1

Estimate of the individual intercept on the first dimension. If viewing the results for a higher dimension, higher dimension results will appear as c2, c3, etc.

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 on pg. 973 of 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.

Arguments

data

matrix of numeric values containing the issue scale 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.

Author

Keith Poole

Howard Rosenthal

Jeffrey Lewis

James Lo

Royce Carroll

Christopher Hare

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

'Issues1980', 'summary.blackbox', 'plot.blackbox'.

Examples

  ### Loads issue scales from the 1980 NES.
  data(Issues1980)
  Issues1980[Issues1980[,"abortion1"]==7,"abortion1"] <- 8	#missing recode
  Issues1980[Issues1980[,"abortion2"]==7,"abortion2"] <- 8	#missing recode

  # \donttest{ 
  Issues1980_bb <- blackbox(Issues1980, missing=c(0,8,9), verbose=FALSE, 
    dims=3, minscale=8)
  # }
  ### 'Issues1980_bb' can be retrieved quickly with: 
  data(Issues1980_bb)

  summary(Issues1980_bb)