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)A vector of scale values
A matrix or dataframe of choice frequencies
"choice", "logistic", "normal"
Precision of answer
Are the choices ordered by column over row (TRUE) or row over column False)
Goodness of fit of the model
Sum of squares for original data
Sum of squares for residuals given the data and the model
Residual matrix
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.
Revelle, W. (in preparation) Introduction to psychometric theory with applications in R, Springer. https://personality-project.org/r/book