basicspace (version 0.02)

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,verbose=FALSE,dims=1,minscale)

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. 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 in

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 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.}

item

• individuals
• fits
• SSE.explained
• percent
• SE
• singular
• Nrow
• Ncol
• Ndata
• Nmiss
• SS_mean
• dims

itemize

• SSE

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

'Issues1980', 'summary.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
result <- blackbox(Issues1980,missing=c(0,8,9),verbose=FALSE,dims=3,minscale=8)
summary(result)