SampleStatistics {cmm}R Documentation



Gives sample values, standard errors and z-scores of one or more coefficients. SampleStatistics(dat,coeff) gives exactly the same output as ModelStatistics(dat,dat,"SaturatedModel",coeff).


SampleStatistics(dat, coeff, CoefficientDimensions = "Automatic", 
 Labels = "Automatic", ShowCoefficients = TRUE, ParameterCoding = "Effect", 
 ShowParameters = FALSE, ShowCorrelations = FALSE, Title = "", ShowSummary = TRUE)



observed data as a list of frequencies or as a data frame


list of coefficients, can be obtained using SpecifyCoefficient, or a predefined function such as "log"


numeric vector of dimensions of the table in which the coefficient vector is to be arranged


list of characters or numbers indicating labels for dimensions of table in which the coefficient vector is to be arranged


boolean, indicating whether or not the coefficients are to be displayed


boolean, indicating whether or not the parameters (computed from the coefficients) are to be displayed


Coding to be used for parameters, choice of "Effect", "Dummy" and "Polynomial"


boolean, indicating whether or not to show the correlation matrix for the estimated coefficients


Title of computation to appear at top of screen output


Show summary on the screen


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,


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).


A subset of the output of MarginalModelFit.


W. P. Bergsma


Bergsma, W. P. (1997). Marginal models for categorical data. Tilburg, The Netherlands: Tilburg University Press.

Bergsma, W. P., Croon, M. A., & Hagenaars, J. A. P. (2009). Marginal models for dependent, clustered, and longitudunal categorical data. Berlin: Springer.

See Also

ModelStatistics, MarginalModelFit


## Not run: 

## 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(var = c(1, 2, 3, 4, 5, 6, 7), 
 marg = list(c(1),c(2),c(3), c(4),c(5),c(6),c(7)), dim = c(5, 5, 5, 5, 5, 5, 5))

# First method: the "coefficients" are the log-probabilities, from which all the 
# (loglinear) parameters are calculated
stats <- SampleStatistics(dat = dat, coeff = 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))
stats <- SampleStatistics(dat = dat, coeff = list(loglinpar75, at75), 
 CoefficientDimensions = c(7,5), Labels = c("I","S"))

## End(Not run)

[Package cmm version 1.0 Index]