semObject: Fitted semObject

Description Value References See Also

Description

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.

Value

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 σ_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

an object of class formula, as in lm or lmer

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 σ_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

References

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.

See Also

smicd, lm, lmer, r.squaredGLMM


smicd documentation built on May 2, 2019, 4:07 p.m.