| semObject {smicd} | R Documentation |
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 |
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 |
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 |
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