Description Usage Arguments Details Value Author(s) References See Also Examples
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.
1 2 3 | ModelStatistics(dat, fitfreq, model, coeff, CoefficientDimensions = "Automatic",
Labels = "Automatic", Method = "ML", ShowCoefficients = TRUE, ShowParameters = FALSE,
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 |
coeff |
list of coefficients, can be obtained using |
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 |
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).
A subset of the output of MarginalModelFit
.
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | # Below an example where ModelStatistics can be used to get output for coefficients
# not given by MarginalModelFit
# Marginal homogeneity (MH) in a 3x3 table AB
# Note that MH is equivalent to independence in the 2x3 table of marginals IR, in which the
# row with I=1 gives the A marginal, and the row with I=2 gives the B marginal
n <- 1 : 9
at <- MarginalMatrix(c("A", "B"), list(c("A"), c("B")), c(3,3))
bt <- ConstraintMatrix(c("I", "R"), list(c("I"), c("R")), c(2,3))
model <- list( bt, "log", at)
#The "coefficients" for the model are the log marginal probabilities for table IR
fit <- MarginalModelFit(dat = n, model = model,
CoefficientDimensions = c(2, 3), Labels = c("I", "R"))
#to get output for the marginal probabilities, ModelStatistics can be used as follows
spec <- SpecifyCoefficient("ConditionalProbabilities",list(c("I","R"),c("I"),c(2,3)))
coeff <- list(spec, at)
stats <- ModelStatistics(dat = n, fitfreq = fit$FittedFrequencies,
model = model, coeff = coeff, CoefficientDimensions = c(2, 3),
Labels = c("I", "R"))
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