lnre.goodness.of.fit {UCS}R Documentation

Perform Goodness-of-Fit Evaluation of LNRE Model


Evaluate the goodness-of-fit of a LNRE model with a multivariate chi-squared test (Baayen, 2001, Sec. 3.3).


lnre.goodness.of.fit(model, m.max=15)


model an object representing a LNRE model whose parameters have been estimated from observed word frequency data. Currently, the Zipf-Mandelbrot (ZM, class "zm") and the finite Zipf-Mandelbrot (fZM, class "fzm") models are supported.
m.max highest frequency rank to be included in the evaluation (limited by the number of ranks stored in the model object).


This function performs a multivariate chi-squared test to evaluate the goodness-of-fit of an LNRE model (Baayen 2001, p. 119-122).

All LNRE models that follow the UCS/R conventions are supported. In particular, they must specify the number of parameters estimated from the observed data (in the n.param component), and they must provide appropriate implementations of the EV, EVm, and VV methods. Currently available LNRE models are objects of class "zm" or "fzm". The model object must include observed frequency data (in components N, V, and spc), which is usually achieved by estimating the model parameters from the observed frequency spectrum.


A data frame with one row and three columns:
X2 the value of the multi-variate χ^2 test statistic
df the degrees of freedom of the approximate χ^2 distribution of the test statistic under the null hypothesis
p the p-value for the test


Baayen, R. Harald (2001). Word Frequency Distributions. Kluwer, Dordrecht.

See Also

zm, fzm

[Package UCS version 0.5 Index]