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Logical operators of some statistical computing packages and missing values

机译:一些统计计算包和缺失值的逻辑运算符

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摘要

When using statistical computer packages in general, we rely on the results they produce. We are aware that numerical approximations are made and trust that the best algorithms are chosen to do them. Most manuals give us instructions about precision of calculations and some report how missing values are administered. What we are unaware of is that some packages can invent results when creating atomic formulas and compounding complex formulas out of atomic ones, what inflates sample sizes, and possibly leads us to incorrect statistical decisions. Two simple indicator variables, with missing values positioned so the results are always missing values, were tested as numerical, as logical and as character variables, by compounding them through connective ‘and’ (&) and ‘or’ (|) to form new indicator variables. The results show that one of the three very known packages does not, statistically, correctly handle missing values, and the three make atomic formulas out of character variables assigning the value false (0) for missing value, what can be said an statistical error. The conclusion is that statisticians and users of statistics must be aware of the capabilities of logically operating missing values of the statistical packages they use, otherwise wrong statistical decisions can be made. And that programmers of statistical packages should correct their algorithms in order to not permit their packages invent non-existing values.
机译:通常,当使用统计计算机软件包时,我们依赖于它们产生的结果。我们知道可以进行数值近似,并相信选择了最佳算法可以做到。大多数手册为我们提供了有关计算精度的说明,有些手册报告了如何管理缺失值。我们没有意识到的是,某些程序包可以在创建原子公式并将复杂公式与原子公式合成时发明结果,这会使样本数量膨胀,并可能导致我们做出错误的统计决策。通过简单地将两个简单的指标变量(其值定位为缺失值,因此结果始终为缺失值),通过将它们通过连接的“与”(&)和“或”(|)组合以形成新的值,以数值,逻辑和字符变量进行了测试指标变量。结果表明,三个众所周知的程序包之一在统计上不能正确处理缺失值,并且这三个程序包是根据字符变量使用原子公式来为缺失值赋值为false(0)的原子公式,这可以说是统计错误。结论是,统计学家和统计人员必须意识到他们使用的统计软件包缺失值的逻辑运算能力,否则可能会做出错误的统计决策。统计软件包的程序员应更正其算法,以免其软件包发明不存在的值。

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