| ModelStatistics {cmm} | R Documentation |
If fitted frequencies under a model have been calculated, this procedure can be used to give sample values, fitted values, estimated standard errors, z-scores and adjusted residuals of one or more coefficients.
ModelStatistics(dat, fitfreq, model, coeff, CoefficientDimensions="Automatic",
Labels="Automatic",ShowCoefficients=TRUE,ShowParameters=FALSE,Method="ML", ParameterCoding="Effect", ShowCorrelations=FALSE, Title="")
dat |
observed data as a list of frequencies or as a data frame |
fitfreq |
vector of fitted frequencies for each cell of full table (including latent variables, if any) |
model |
list specified eg as list(bt,coeff,at) |
coeff |
list of coefficients, can be obtained using SpecifyCoefficient |
CoefficientDimensions |
numeric vector of dimensions of the table in which the coefficient vector is to be arranged |
Labels |
list of characters or numbers indicating labels for dimensions of table in which the coefficient vector is to be arranged |
ShowCoefficients |
boolean, indicating whether or not the coefficients are to be displayed |
ShowParameters |
boolean, indicating whether or not the parameters (computed from the coefficients) are to be displayed |
Method |
character, choice of "ML" for maximum likelihood or "GSK" for the GSK method |
ParameterCoding |
Coding to be used for parameters, choice of "Effect", "Dummy" and "Polynomial" |
ShowCorrelations |
boolean, indicating whether or not to show the correlation matrix for the estimated coefficients |
Title |
title of computation to appear at top of screen output |
The data can be a data frame or vector of frequencies. MarginalModelFit converts a data frame dat using c(t(ftable(dat))).
For ParameterCoding, the default for "Dummy"
is that the first cell in the table is the reference cell. Cell
(i, j, k, ...) can be made reference cell using
list("Dummy",c(i,j,k,...)). For "Polynomial" the
default is to use centralized scores based on equidistant (distance
1) linear scores, for example, if for i=1,2,3,4,
mu_i = alpha + q_i beta + r_i gamma + s_i delta
where beta is a quadratic, gamma a cubic and delta a
quartic effect, then q_i takes the values (-1.5, -.5, .5, 1.5),
r_i takes the values (1, -1, -1, 1)
(centralized squares of the q_i), and s_i takes the values
(-3.375, -.125, .125, 3.375) (cubes of the q_i).
NA. Only output to the screen is provided
W. P. Bergsma w.p.bergsma@lse.ac.uk
Bergsma, W. P. (1997). Marginal models for categorical data. Tilburg, The Netherlands: Tilburg University Press. http://stats.lse.ac.uk/bergsma/pdf/bergsma_phdthesis.pdf
Bergsma, W. P., Croon, M. A., & Hagenaars, J. A. P. (2009). Marginal models for dependent, clustered, and longitudunal categorical data. Berlin: Springer.
ModelStatistics,
MarginalModelFit