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