smicd (version 1.1.5)

semLme: 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
)

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".

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 lmer. Note: Only models with a maximum of one random intercept and one random slope are implemented at this point (e.g. y ~ x + (1| ID), or y ~ x + (x|ID)). The dependent variable is measured as interval-censored values; factor with ordered factor values

data

a data frame containing the variables of the model

classes

numeric vector of classes; -Inf as lower interval bound and Inf as upper interval bound is allowed. If the Box-Cox or logarithmic transformation is chosen, the minimum interval bound must be \(\ge 0\).

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

• "log" for Logarithmic transformation

• "bc" for Box-Cox transformation

default is "None". Transformations can only be used if the minimum interval bound is \(\ge 0\).

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: (burnin+samples)*adjust

bootstrap.se

if TRUE standard errors of the regression parameters are estimated

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.

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

if (FALSE) {
# 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)

}
# \dontshow{
# 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, burnin = 4, samples = 10
)
summary(model1)

# }