smicd {smicd} | R Documentation |
Statistical Methods for Interval Censored (Grouped) Data
Description
The package smicd supports the estimation of linear and linear mixed regression models (random slope and random intercept models) with interval censored dependent variable. Parameter estimates are obtain by a stochastic expectation maximization (SEM) algorithm (Walter, 2019). Standard errors are estimated by a non-parametric bootstrap in the linear regression model and by a parametric bootstrap in the linear mixed regression model. To handle departures from the model assumptions transformations (log and Box-Cox) of the interval censored dependent variable are incorporated into the algorithm (Walter, 2019). Furthermore, the package smicd has implemented a non-parametric kernel density algorithm for the direct (without covariates) estimation of statistical indicators from interval censored data (Walter, 2019; Gross et al., 2017). The standard errors of the statistical indicators are estimated by a non-parametric bootstrap.
Details
The two estimation functions for the linear and linear mixed regression model
are called semLm
and semLme
. So far, only random
intercept and random slope models are implemented. For both functions
the following methods are available: summary.sem
,
print.sem
and plot.sem
.
The function for the direct estimation of statistical indicators is called
kdeAlgo
. The following methods are available:
print.kdeAlgo
and plot.kdeAlgo
.
An overview of all currently provided functions can be requested by
library(help=smicd)
.
References
Walter, P. (2019). A Selection of Statistical Methods for Interval-Censored
Data with Applications to the German Microcensus, PhD thesis,
Freie Universitaet Berlin
Gross, M., U. Rendtel, T. Schmid, S. Schmon, and N. Tzavidis (2017).
Estimating the density of ethnic minorities and aged people in Berlin: Multivariate
Kernel Density Estimation applied to sensitive georeferenced administrative data
protected via measurement error. Journal of the Royal Statistical Society: Series A
(Statistics in Society), 180.