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.ya matrix containing the pseudo samples of the interval censored variable from each iteration step
coefthe estimated regression coefficients (fixed effects)
ranefthe estimated regression random effects
sigmaeestimated variance \(\sigma_e\)
VaVocestimated covariance matrix of the random effects
sebootstrapped standard error of the coefficients
cibootstrapped 95% confidence interval of the coefficients
lambdaestimated lambda for the Box-Cox transformation
bootstrapsnumber of bootstrap iterations for the estimation of the standard errors
r2estimated coefficient of determination
r2mestimated marginal coefficient of determination for
generalized mixed-effect models, as in r.squaredGLMM
r2cestimated conditional coefficient of determination for
generalized mixed-effect models, as in r.squaredGLMM
iccestimated interclass correlation coefficient
adj.r2estimated adjusted coefficient of determination
formulatransformationthe specified transformation "log" for logarithmic and "bc" for Box-Cox
n.classesthe number of classes, the dependent variable is censored to
conv.coefestimated coefficients for each iteration step of the SEM-algorithm
conv.sigmaeestimated variance \(\sigma_e\) for each iteration step of the SEM-algorothm
conv.VaCovestimated covariance matrix of the random effects for each iteration step of the SEM-algorithm
conv.lambdaestimated lambda for the Box-Cox transformation for each iteration step of the SEM-algorithm
b.lambdathe number of burn-in iteration the SEM-algorithm used to estimate lambda
m.lambdathe number of additional iteration the SEM-algorithm used to estimate lambda
burninthe number of burn-in iterations of the SEM-algorithm
samplesthe number of additional iterations of the SEM-algorithm
classesspecified intervals
original.ythe dependent variable of the regression model measured on an interval censored scale
callthe 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