semObject: Fitted semObject

In smicd: Statistical Methods for Interval-Censored Data

Fitted semObject

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 \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	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 \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

References

Walter, P. (2019). A Selection of Statistical Methods for Interval-Censored Data with Applications to the German Microcensus, PhD thesis, Freie Universitaet Berlin

See Also

smicd, lm, lmer, r.squaredGLMM

smicd documentation built on Nov. 19, 2023, 5:12 p.m.