Test the adequacy of simple choice, logistic, or Thurstonian scaling.
Given a matrix of choices and a vector of scale values, how well do the scale values capture the choices? That is, what is size of the squared residuals given the model versus the size of the squared choice values?
scaling.fits(model, data, test = "logit", digits = 2, rowwise = TRUE)
model: A vector of scale valuesdata: A matrix or dataframe of choice frequenciestest: "choice", "logistic", "normal"digits: Precision of answerrowwise: Are the choices ordered by column over row (TRUE) or row over column False)How well does a model fit the data is the classic problem of all of statistics. One fit statistic for scaling is the just the size of the residual matrix compared to the original estimates.
GF: Goodness of fit of the model
original: Sum of squares for original data
resid: Sum of squares for residuals given the data and the model
residual: Residual matrix
Revelle, W. (in preparation) Introduction to psychometric theory with applications in R, Springer. https://personality-project.org/r/book/
William Revelle
Mainly for demonstration purposes for a course on psychometrics
thurstone, vegetables