psych (version 1.0-58)

scaling.fits: Test the adequacy of simple choice, logistic, or Thurstonian scaling.

Description

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?

Usage

scaling.fits(model, data, test = "logit", digits = 2, rowwise = TRUE)

Arguments

model

A vector of scale values

data

A matrix or dataframe of choice frequencies

test

"choice", "logistic", "normal"

digits

Precision of answer

rowwise

Are the choices ordered by column over row (TRUE) or row over column False)

Value

• 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

References

Revelle, W. (in preparation) Introduction to psychometric theory with applications in R, Springer. http://personality-project.org/r/book

See Also

thurstone, vegetables