The package VIM (Templ, Alfons, Kowarik, and Prantner 2016) is developed to explore and analyze the structure of missing values in data using visualization methods, to impute these missing values with the built-in imputation methods and to verify the imputation process using visualization tools, as well as to produce high-quality graphics for publications. This article focuses on the different imputation techniques available in the package. Four different imputation methods are currently implemented in VIM, namely hot-deck imputation, k-nearest neighbor imputation, regression imputation and iterative robust model-based imputation (Templ, Kowarik, and Filzmoser 2011). All of these methods are implemented in a flexible manner with many options for customization. Furthermore in this article practical examples are provided to highlight the use of the implemented methods on real-world applications.In addition, the graphical user interface of VIM has been re-implemented from scratch resulting in the package VIMGUI (Schopfhauser, Templ, Alfons, Kowarik, and Prantner 2016) to enable users without extensive R skills to access these imputation and visualization methods.
The demand for data from surveys, censuses or registers containing sensible information on people or enterprises has increased significantly over the last years. However, before data can be provided to the public or to researchers, confidentiality has to be respected for any data set possibly containing sensible information about individual units. Confidentiality can be achieved by applying statistical disclosure control (SDC) methods to the data in order to decrease the disclosure risk of data.The R package sdcMicro serves as an easy-to-handle, object-oriented S4 class implementation of SDC methods to evaluate and anonymize confidential micro-data sets. It includes all popular disclosure risk and perturbation methods. The package performs automated recalculation of frequency counts, individual and global risk measures, information loss and data utility statistics after each anonymization step. All methods are highly optimized in terms of computational costs to be able to work with large data sets. Reporting facilities that summarize the anonymization process can also be easily used by practitioners. We describe the package and demonstrate its functionality with a complex household survey test data set that has been distributed by the International Household Survey Network.
The production of synthetic datasets has been proposed as a statistical disclosure control solution to generate public use files out of protected data, and as a tool to create "augmented datasets" to serve as input for micro-simulation models. Synthetic data have become an important instrument for ex-ante assessments of policy impact. The performance and acceptability of such a tool relies heavily on the quality of the synthetic populations, i.e., on the statistical similarity between the synthetic and the true population of interest. Multiple approaches and tools have been developed to generate synthetic data. These approaches can be categorized into three main groups: synthetic reconstruction, combinatorial optimization, and model-based generation. We provide in this paper a brief overview of these approaches, and introduce simPop, an open source data synthesizer. simPop is a user-friendly R package based on a modular object-oriented concept. It provides a highly optimized S4 class implementation of various methods, including calibration by iterative proportional fitting and simulated annealing, and modeling or data fusion by logistic regression. We demonstrate the use of simPop by creating a synthetic population of Austria, and report on the utility of the resulting data. We conclude with suggestions for further development of the package.
The objective of the present naturalistic study was to assess the differential effects of opioid detoxification with methadone or buprenorphine on activity, circadian rhythm, and sleep. Forty-two consecutive inpatients with opiate addiction were switched to either methadone or buprenorphine and gradually tapered down over the course of 2 to 3 weeks. There were no significant differences in comedication (lofexidine, quetiapine, and valproic acid) between the methadone and buprenorphine groups. Patients in the methadone group showed 11% lower activity and were 24 minutes phase delayed as compared with buprenorphine-treated patients, whereas the latter had 2.5% lower sleep efficiency and 9% shorter actual sleep time. These significant group differences were most pronounced for the lowest doses (≤20% of maximum individual daily dose, ie, at the end of withdrawal representing late withdrawal effects). Furthermore, for the total sample, we found a significant decrease in the relative amplitude of the sleep-wake cycle and worsening of all actigraphic sleep parameters from the higher (100% to 20%) to the lowest doses (20% to 0%). The acrophase of the circadian rhythm displayed a phase advance (-88 minutes) from the highest (100% to 80%) to the lower doses (80% to 0%) in methadone-treated patients. Opioid tapering with methadone or buprenorphine leads to characteristic changes of the rest-activity cycle, but further study is required to validate these results.
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