SampleStatistics {cmm} | R Documentation |
Gives sample values, standard errors and z-scores of one or more coefficients.
SampleStatistics(dat, coeff, CoefficientDimensions="Automatic", Labels="Automatic", ShowCoefficients=TRUE, ShowParameters=FALSE, ParameterCoding="Effect", ShowCorrelations=FALSE, Title="")
dat |
observed data as a list of frequencies or as a data frame |
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 |
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
data(BodySatisfaction) ## Table 2.6 in Bergsma, Croon and Hagenaars (2009). Loglinear parameters for marginal table IS ## We provide two to obtain the parameters dat <- BodySatisfaction[,2:8] # omit first column corresponding to gender # matrix producing 1-way marginals, ie the 7x5 table IS at75 <- MarginalMatrix( c(1, 2, 3, 4, 5, 6, 7), list(c(1),c(2),c(3),c(4),c(5),c(6),c(7)), c(5, 5, 5, 5, 5, 5, 5) ) # First method: the "coefficients" are the log-probabilities, from which all the (loglinear) parameters are calculated SampleStatistics(dat, list("log",at75), CoefficientDimensions=c(7,5),Labels=c("I","S"),ShowCoefficients=FALSE,ShowParameters=TRUE) # Second method: the "coefficients" are explicitly specified as being the (highest-order) loglinear parameters loglinpar75 <- SpecifyCoefficient("LoglinearParameters", c(7,5) ) SampleStatistics(dat, list(loglinpar75, at75), CoefficientDimensions=c(7,5), Labels=c("I","S"))