ecov_long: Estimate Action Expected-Outcome Covariance Elements

In Dustin-Wood/funfield: Functional Field Functions

Estimate Action Expected-Outcome Covariance Elements

Description

Compute all possible products of effects for all features from weighted ESJT data, wFFdata. The average of the dAdB elements will ultimately equal the covariances forming the expectancy matrix used to estimate functional field models. Note #1: this function a more generalized version of function ecov_big, which provides flexibility of allowing multiple actions per scenario Note #2: this function assumes that the features have a "meaningful zero point." That is a 0 should mean "no effect", and negative values should mean "negative effect", and positive values should mean "positive effect", with greater deviations from zero meaning "stronger effect." If data is not prepared this way, the results are not very likely to be meaningful.

Note: data can be weighted using sweighteddata function

Usage

ecov_long(
  eData,
  p = "p",
  s = "s",
  i = "i",
  fcols = c(4:ncol(eData)),
  addDidi = T
)

Arguments

eData	expected effect data
p	variable identifying 'person' (or respondent) - give in "quotemarks"
s	variable identifying 'situation' (or scenario) - give in "quotemarks"
i	variable identifying 'action' (or response) - give in "quotemarks"
fcols	columns with variables for field matrix - defaults to c(4:ncol(eData)) assuming a c(p,s,i,(etc)) data structure
addDidi	should you add the 'did_i' variable as element for effect covariance matrix, if not already present? Defaults to TRUE.

Details

Usage notes: eData should typically have format of p, s, i, as first three columns, followed by effect ratings, and end in Likelihood. If there is a preferred ordering of columns, you should put eData into that ordering *before* running this code.

Value

Effect covariance elements

Dustin-Wood/funfield documentation built on July 20, 2023, 7:10 a.m.