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psych (version 1.1.11)

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

  • GFGoodness of fit of the model
  • originalSum of squares for original data
  • residSum of squares for residuals given the data and the model
  • residualResidual matrix

Details

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.

References

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

See Also

thurstone, vegetables