semLme {smicd} | R Documentation |
Linear Mixed Regression with Interval-Censored Dependent Variable
Description
This function estimates the linear mixed regression model when the dependent variable is interval-censored. The estimation of the standard errors is fasciliated by a parametric bootstrap.
Usage
semLme(
formula,
data,
classes,
burnin = 40,
samples = 200,
trafo = "None",
adjust = 2,
bootstrap.se = FALSE,
b = 100
)
Arguments
formula |
a two-sided linear formula object describing both the fixed-effects
and random-effects part of the model, with the response on the left of a ~ operator
and the terms, separated by + operators, on the right. Random-effects terms are
distinguished by vertical bars (|) separating expressions for design matrices from
grouping factors, as in |
data |
a data frame containing the variables of the model |
classes |
numeric vector of classes; |
burnin |
the number of burn-in iterations of the SEM-algorithm |
samples |
the number of additional iterations of the SEM-algorithm for parameter estimation |
trafo |
transformation of the dependent variable to fulfil the model assumptions
default is |
adjust |
extends the number of iteration steps of the SEM-algorithm
for finding the optimal lambda of the Box-Cox transformation. The number of iterations
is extended in the following way: |
bootstrap.se |
if |
b |
number of bootstrap iterations for the estimation of the standard errors |
Details
The model parameters are estimated using pseudo samples of the
interval-censored dependent variable. The object pseudo.y
returns the
pseudo samples of each iteration step of the SEM-algorithm.
Value
An object of class "sem" that provides parameter estimated for linear
regression models with interval-censored dependent variable. Generic
functions such as, print
,
plot
, and summary
have methods that can be used
to obtain further information. See semObject
for descriptions
of components
of objects of class "sem".
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
lmer
, print.sem
,
plot.sem
, summary.sem
Examples
## Not run:
# Load and prepare data
data <- Exam
classes <- c(1, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.7, 8.5, Inf)
data$examsc.class <- cut(data$examsc, classes)
# Run model with random intercept and default settings
model1 <- semLme(
formula = examsc.class ~ standLRT + schavg + (1 | school),
data = data, classes = classes
)
summary(model1)
## End(Not run)