An object of class "sem" that represents the estimated model
parameters and standard errors.
Objects of this class have methods for the generic functions
print
, plot
and summary
.
An object of class "sem" is a list containing the following components. Some parameters are only estimated for liner mixed regression models (and vice versa).
pseudo.y
a matrix containing the pseudo samples of the interval-censored variable from each iteration step
coef
the estimated regression coefficients (fixed effects)
ranef
the estimated regression random effects
sigmae
estimated variance \(\sigma_e\)
VaVoc
estimated covariance matrix of the random effects
se
bootstrapped standard error of the coefficients
ci
bootstrapped 95% confidence interval of the coefficients
lambda
estimated lambda for the Box-Cox transformation
bootstraps
number of bootstrap iterations for the estimation of the standard errors
r2
estimated coefficient of determination
r2m
estimated marginal coefficient of determination for
generalized mixed-effect models, as in r.squaredGLMM
r2c
estimated conditional coefficient of determination for
generalized mixed-effect models, as in r.squaredGLMM
icc
estimated interclass correlation coefficient
adj.r2
estimated adjusted coefficient of determination
formula
transformation
the specified transformation "log" for logarithmic and "bc" for Box-Cox
n.classes
the number of classes, the dependent variable is censored to
conv.coef
estimated coefficients for each iteration step of the SEM-algorithm
conv.sigmae
estimated variance \(\sigma_e\) for each iteration step of the SEM-algorothm
conv.VaCov
estimated covariance matrix of the random effects for each iteration step of the SEM-algorithm
conv.lambda
estimated lambda for the Box-Cox transformation for each iteration step of the SEM-algorithm
b.lambda
the number of burn-in iteration the SEM-algorithm used to estimate lambda
m.lambda
the number of additional iteration the SEM-algorithm used to estimate lambda
burnin
the number of burn-in iterations of the SEM-algorithm
samples
the number of additional iterations of the SEM-algorithm
classes
specified intervals
original.y
the dependent variable of the regression model measured on an interval-censored scale
call
the function call
Walter, P. (2019). A Selection of Statistical Methods for Interval-Censored Data with Applications to the German Microcensus, PhD thesis, Freie Universitaet Berlin