Description Value References See Also
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).

a matrix containing the pseudo samples of the interval censored variable from each iteration step 

the estimated regression coefficients (fixed effects) 

the estimated regression random effects 

estimated variance σ_e 

estimated covariance matrix of the random effects 

bootstrapped standard error of the coefficients 

bootstrapped 95% confidence interval of the coefficients 

estimated lambda for the BoxCox transformation 

number of bootstrap iterations for the estimation of the standard errors 

estimated coefficient of determination 

estimated marginal coefficient of determination for
generalized mixedeffect models, as in 

estimated conditional coefficient of determination for
generalized mixedeffect models, as in 

estimated interclass correlation coefficient 

estimated adjusted coefficient of determination 

an object of class 

the specified transformation "log" for logarithmic and "bc" for BoxCox 

the number of classes, the dependent variable is censored to 

estimated coefficients for each iteration step of the SEMalgorithm 

estimated variance σ_e for each iteration step of the SEMalgorothm 

estimated covariance matrix of the random effects for each iteration step of the SEMalgorithm 

estimated lambda for the BoxCox transformation for each iteration step of the SEMalgorithm 

the number of burnin iteration the SEMalgorithm used to estimate lambda 

the number of additional iteration the SEMalgorithm used to estimate lambda 

the number of burnin iterations of the SEMalgorithm 

the number of additional iterations of the SEMalgorithm 

specified intervals 

the dependent variable of the regression model measured on an interval censored scale 

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