smicd (version 1.0.3)

semLm: Linear Regression with Interval Censored Dependent Variable

Description

This function estimates the linear regression model when the dependent variable is interval censored. The estimation of the standard errors is fasciliated by a non-parametric bootstrap.

Usage

semLm(formula, data, classes, burnin = 40, samples = 200, trafo = "None",
  adjust = 2, bootstrap.se = FALSE, b = 100)

Arguments

formula

an object of class formula, as in lm. 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 fulfill 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

Value

An object of class "sem" that provides parameter estimates 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 a description of the components of objects of class "sem".

Details

The model parameters are estimated using pseudo samples as a proxy for the interval censored dependent variable. The object pseudo.y returns the pseudo samples of each iteration step of the SEM-algorithm.

References

Walter, P., Gross, M., Schmid, T. and Tzavidis, N. (2017). Estimation of Linear and Non-Linear Indicators using Interval Censored Income Data. FU-Berlin School of Business & Economics, Discussion Paper.

See Also

lm, 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 default settings
model <- semLm(formula = examsc.class ~ standLRT + schavg, data = data,
classes = classes)
summary(model)
# }
# NOT RUN {
 
# }

Run the code above in your browser using DataCamp Workspace