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., Gross, M., Schmid, T. and Tzavidis, N. (2017). Estimation of Linear and Non-Linear Indicators using Interval Censored Income Data. School of Business & Economics, Discussion Paper.
smicd
, lm
, lmer
,
r.squaredGLMM