bridge.linearmap: Bridging Two Ideal Point Estimates with Linear Transformation...
In gentok/ipbridging: Bridging Ideal Point Estimates

Description Usage Arguments Value Author(s)

Bridging Two Ideal Point Estimates with Linear Transformation Method

bridge.linearmap(
  ip1,
  ip2,
  anchorrows.ip1,
  anchorrows.ip2,
  method = "procrustes",
  trans.ip2 = TRUE,
  opt = FALSE,
  opt.iter.n = 10000,
  opt.sample.n = 30,
  opt.th.inline = 0.5,
  blend = TRUE,
  blend.th1 = 0.05,
  blend.th2 = 0.15
)

`ip1`	Matrix or data.frame of 'reference' ideal points (i.e., `ip2` ideal points will be transformed and mapped to `ip1` space). Rows are resepondents and columns are ideal point dimensions. The current code only allows 2 dimensions.
`ip2`	Matrix or data.frame of ideal points to be transformed.
`anchorrows.ip1`	Vector of row number of anchoring respondents in `ip1`.
`anchorrows.ip2`	Vector of row number of anchoring respondents in `ip2`. Must be the same length as `anchorrows.ip1`.
`method`	Method of bridging. Currently, following methods are aviailable: `"procrustes"` (default): Procrustes transformation method. Based on anchor cases, this method provides restricted non-parametric procedure to find optimal transformation matrix to bridge ideal point estimates. `"homography"`: Homography transformation method. Based on anchor cases, this method provides non-parametric procedure to find optimal transformation matrix to bridge ideal point estimates. `"olsmap"`: OLS mapping method, Based on anchor cases, use OLS regression to map `d2` ideal point coordinates on `d1` ideal point space.
`trans.ip2`	If `TRUE` (default), transform `ip2` to map them on `ip1` space. If `FALSE`, transform `ip1` to map them on `ip2` space.
`opt`	If `TRUE`, conduct optimization of transformation through RANSAC (random sample consensus). The default is `FALSE`.
`opt.iter.n`	Number of iteration in the optimization of transformation matrix.
`opt.sample.n`	Size of anchoring respondents to be sub-sampled at each iteration of optimization.
`opt.th.inline`	Upper bound to determine inline respondents at each iteration of optimization. A respondent is considered 'inline' if the distance between transformed `ip2` and `ip1` goes below this threshold.
`blend`	If `FALSE`, do not use blending procedure. The default is `TRUE`.
`blend.th1`	If `blend==TRUE`, first threshold used in 'blending' procedure. If minimum difference between transformed `ip2` and `ip1` goes below this threshold, the transformed `ip2` is replaced with the closest `ip1`.
`blend.th2`	If `blend==TRUE`, second threshold used in 'blending' procedure. If minimum difference between transformed `ip2` and `ip1` goes below this threshold (but above `blend.th1`), the transformed `ip2` is replaced with the value that intrapolates `ip1` that falls within this threshold.